JP2021192197A - Meat quality determination program - Google Patents

Abstract

To allow for automatically determining meat quality with high accuracy without relying on human hands.SOLUTION: A meat quality determination program for determining meat quality during livestock trading makes a computer perform: an information acquisition step for acquiring acceleration information corresponding to movements of a target domestic animal detected by an acceleration sensor attached to the body of the domestic animal; and a determination step for determining meat quality using three or more levels of association between meat quality and reference acceleration information corresponding to movements of domestic animals as detected by the acceleration sensors attached to the bodies of the domestic animals in the past and on the basis of the reference acceleration information corresponding to the acceleration information acquired in the above information acquisition step, giving priority to one with a higher level of association.SELECTED DRAWING: Figure 27

Description

The present invention relates to a meat quality discrimination program suitable for discriminating meat quality with high accuracy.

The meat quality of meat is evaluated based on items such as "fat crossing", "meat color", "meat tightness", and "fat color and quality". Then, the meat quality is finally expressed by the grade from the comprehensive discrimination result of each of these items. Conventionally, the meat quality of this meat has been evaluated by a panelist (consumer) as a sensory evaluation by a person, such as the quality and characteristics of the meat (hard or soft, strong or weak flavor, etc.). However, evaluation by panelists may cause blurring, and it is often difficult to make a unified judgment. In some cases, meat quality is analyzed through instrumental analysis, but if instrumental analysis is performed each time meat is shipped, labor and cost burden will increase.

For this reason, a system capable of evaluating meat quality with high accuracy without relying on human sensory evaluation or instrumental analysis has been conventionally desired. Moreover, there is a growing need to determine the quality of meat that can be obtained from livestock before it is decomposed as meat, but no technology has yet been proposed to achieve this. It was the current situation.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose thereof is to obtain meat that may be obtained from the meat at the time of biological transaction before the livestock is decomposed as meat. It is an object of the present invention to provide a meat quality discrimination program capable of discriminating quality automatically with high accuracy without relying on human sensory evaluation or instrument analysis.

The meat quality discrimination program according to the present invention is an information acquisition step for acquiring image information of the living body of the livestock to be discriminated in the meat quality discriminating program for discriminating the meat quality at the time of biological transaction of the livestock, and a reference to the living body of the livestock imaged in the past. Based on the reference image information corresponding to the image information acquired in the above information acquisition step, the meat quality is prioritized by giving priority to the one with the higher degree of association. It is characterized in that a computer is made to execute a discrimination step for discriminating.

The quality of meat that anyone can easily obtain at the time of biotrading before the livestock is easily decomposed as meat, without relying on human sensory evaluation or instrumental analysis, without any special skills or experience. Can be discriminated with high accuracy.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the meat quality discrimination program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG . 1 is a block diagram showing an overall configuration of a meat quality discrimination system 1 in which a meat quality discrimination program as a first embodiment is implemented. The meat quality discrimination system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the discrimination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of not only humans but also animals. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

The database 3 stores various information necessary for discriminating meat quality. Information necessary for determining meat quality includes reference image information of meat captured in the past, reference ultrasonic image information previously captured from the living body of livestock that provides meat, free amino acid analysis from meat, fatty acid composition, and so on. Reference analysis information that analyzes any one or more of oleic acid, inosynic acid, guanylate, and vitamin E, reference production area information regarding the meat production area that was imaged in the past, and reference obtained from the living body of livestock that provides meat in the past. Biometric information, reference breeding environment information regarding the breeding environment of livestock that provide meat imaged in the past, reference feed information regarding the feed given to livestock that provide meat that has been imaged in the past, and actual judgments based on these. The data set with the meat quality made is stored.

That is, in addition to such reference image information, the database 3 contains reference ultrasonic image information, reference analysis information, reference production area information, reference biological information, reference breeding environment information, and reference food information. Any one or more and the meat quality are associated with each other and memorized.

The discrimination device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by the discrimination device 2.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discrimination device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, a determination unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the discrimination unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The determination unit 27 determines the search solution. The discriminating unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the discriminating operation. The discriminating unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the meat quality discrimination system 1 having the above-described configuration will be described.

In the meat quality discrimination system 1, for example, as shown in FIG. 3, it is premised that the degree of association between the reference image information and the meat quality in three or more stages is set in advance. The reference image information is obtained from the image information obtained by taking an image of the appearance of the meat, and can be obtained by analyzing the image information. This image may be a moving image as well as a still image. For this reference image information, the meat quality may be specified by analyzing the image captured for the meat. The reference image information is premised on being composed of image data obtained by imaging the meat at the time of slaughter, which is disassembled for meat, but is composed of image data from the living body of the livestock that provides the meat. May be good.

The meat quality referred to here may be expressed by, for example, "fat crossing", that is, the degree of marbling, or may be evaluated based on the determination criteria of BMS (beef marbling standard). The meat quality may be expressed by "meat color". This "meat color" is the color and luster of the meat, and like the "fat crossbreeding", the color of the meat was evaluated based on the criteria called BCS (Beef Color Standard). May be good. The meat quality also includes luster. Meat quality also includes "meat tightness", which may be evaluated visually. This meat quality may be evaluated by the texture of the meat, and if these are fine, a soft texture can be obtained. Meat quality is also included in "fat color and quality", and the color is judged based on white or cream color, and is evaluated in consideration of luster and quality. In addition to this, the presence or absence of gravy is also a factor that affects the taste, so this may be included in the index.

This meat quality may be expressed through the grade of meat, and the meat quality may be expressed by a ranking evaluated on a 5-point or 10-point set by the system side or the user side. good. Alternatively, it may simply be a very tasty, tasty, so-so, ordinary expression.

These meat qualities may be discriminated based on the previously trained features. At this time, artificial intelligence is used to learn the image data of the meat and the meat quality, and when actually acquiring the reference image information, the meat quality is determined by comparing with the learned image data. You may try to do it.

Further, as an alternative to the meat quality, any one or more of the crossbreeding, the color of the meat, the tightness of the meat, and the color of the fat and the quality may be output. In such a case, the image data of the meat and one or more of the fat crossing, the color of the meat, the tightness of the meat, the color of the fat and the quality are learned, and the image information for reference is actually acquired. At that time, the discrimination may be made by comparing with these trained image data.

The meat quality may be judged to be good or bad based on the previous experience of the evaluator, or the taste may be judged by actually tasting it. In such a case, multiple inspectors who sample the meat quality evaluate the taste of each item such as texture, sourness, aroma, chewyness, and bitterness in multiple stages, and statistically analyze them for quality. It may be an evaluation value. Further, the meat quality may be determined through a taste sensor capable of detecting the taste, or may be determined through analysis of various instruments.

In the example of FIG. 3, it is assumed that the input data is, for example, reference image information P01 to P03. The reference image information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference image information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference image information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference image information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference image information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference image information. Is shown. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discrimination device 2 accumulates a past data set as to which of the reference image information and the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference image information captured for the meat in the past. By collecting and analyzing such a data set, the degree of association with the reference image information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference image information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference image information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference image information is input as input data, meat quality is output as output data, at least one hidden layer is provided between the input node and the output node, and the machine is provided. You may let them learn. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of the appearance image of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the above-mentioned is used to actually discriminate the meat quality from now on. The meat quality will be searched using the learned data. In such a case, the image information obtained by actually capturing the image of the meat in the area to be discriminated is newly acquired. The newly acquired image information is input by the above-mentioned information acquisition unit 9. The image information is acquired by capturing an image for which meat is to be discriminated. This determination method may be performed by the same method as the above-mentioned reference image information.

The meat quality is determined based on the image information newly acquired in this way. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for from the newly acquired image information and displayed to the user. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

As the above-mentioned image, in addition to the image captured by a normal camera, any one or more of a spectral image and an ultrasonic image may be acquired. In such a case, it is necessary to capture one or more of a spectral image, a visible image, and an ultrasonic image according to the image information to be acquired as reference image information. In particular, when this ultrasonic image is used, the biological data of the meat in the living body of the livestock that provides the meat may be captured.

In the example of FIG. 5, it is premised that a combination of the reference image information and the reference ultrasonic image information is formed. Here, the reference image information is image data of meat taken at the time of past slaughter. The reference image information is an image captured by a normal camera, but may be composed of a spectral image color-coded for each frequency band. On the other hand, the reference ultrasonic image information is ultrasonic image data of a portion of meat previously imaged in a living body of a livestock that provides meat.

By making a judgment by combining the reference ultrasonic image information in addition to the reference image information, the meat quality can be discriminated with higher accuracy. Therefore, in addition to the reference image information, the reference ultrasonic image information is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data is, for example, reference image information P01 to P03 and reference ultrasonic image information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference ultrasonic image information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference ultrasonic image information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference ultrasonic image information are arranged on the left side through this degree of association, and the fleshy material is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference ultrasonic image information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference image information and reference ultrasonic image information is likely to be associated with, and is a reference image information and a reference ultrasonic image. It shows the accuracy in selecting the most probable meat quality from the information. Therefore, the optimum meat quality is searched for by combining the reference image information and the reference ultrasonic image information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, to 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference ultrasonic image information, and the meat quality in that case is suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, it is assumed that the reference image information in the actual case in the past is the image data α. Further, it is assumed that the reference ultrasonic image information is the image data β. In such a case, the meat quality indicating how much the meat quality was actually is learned as a data set, and is defined in the form of the above-mentioned degree of association. In addition, such reference image information and reference ultrasonic image information may be extracted from a management database managed by a producer, a distributor, a distributor, or the like.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference ultrasonic image information P16, the meat quality thereof is analyzed from the past data. If there are many cases where the meat quality is A (sweetness degree 〇〇, acidity degree 〇〇, bitterness degree 〇〇, chewyness 〇〇, etc.), the degree of association that leads to this meat quality A is set higher, and the case of meat quality B When there are many cases and there are few cases of meat quality A, the degree of association leading to meat quality B is set high, and the degree of linkage leading to meat quality A is set low. For example, in the example of the intermediate node 61a, the output of meat quality A and quality B is linked, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node of the combination of the reference ultrasonic image information P14 with respect to the reference image information P01, the degree of association of the meat quality C is w15, and the degree of association of the meat quality E is. Is w16. The node 61c is a node in which the reference ultrasonic image information P15 and P17 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the above-mentioned trained data will be used when actually determining the meat quality from now on. In such a case, the image information and the ultrasonic image information of the meat for which the meat quality is to be actually determined are input or selected.

Based on the newly acquired image information and ultrasonic image information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the ultrasonic image information is P17, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In addition to the above-mentioned reference image information, instead of the above-mentioned reference ultrasonic image information, a combination with the reference spectrum information and a degree of association with the meat quality for the combination may be set in three or more stages. good.

By the way, the above-mentioned ultrasonic image information and ultrasonic image information are not limited to the case of capturing the ultrasonic image data at a certain point of time for the livestock applied as meat, and are not limited to the case of capturing the ultrasonic image data from the living body of the livestock in chronological order. The ultrasonic image may be taken a plurality of times to acquire the change tendency over time. The degree of association is formed as reference ultrasonic image information including the change tendency of such time-series ultrasonic image data, and images are taken multiple times in time series from the living body of the livestock that provides the meat to be discriminated. When the time-series change tendency of the ultrasonic image is acquired, it is discriminated by inputting it as input data.

In FIG. 6, in addition to the above-mentioned reference image information, a combination with the reference analysis information instead of the above-mentioned reference ultrasonic image information and a degree of association with the meat quality for the combination are set in three or more stages. An example is shown.

This reference analysis information, which is added as an explanatory variable instead of the reference ultrasound image information, is any information regarding the results of chemical and physical analysis performed on the meat. This reference analysis information may include analysis information obtained by analyzing any one or more of free amino acid analysis, fatty acid composition, oleic acid, inosinic acid, guanylic acid, and vitamin E. In free amino acid analysis, the ratio of various amino acids such as glutamic acid, which is an umami component, is analyzed. In the analysis of fatty acid composition, the analysis results of quantitative analysis of various fatty acids such as oleic acid contained in adipose tissue are shown as evaluation criteria of texture such as mellowness and melting in the mouth. In the analysis of oleic acid, the more the unit price of unsaturated fatty acid is contained, the softer and tastier it is evaluated, so this is analyzed. The analysis of inosinic acid is performed because inosinic acid, which is a kind of organic compound, is an umami component of dried bonito and is said to increase by aging after dismantling treatment. The analysis of guanylic acid analyzes guanylic acid because it elicits the umami component of shiitake mushrooms. Vitamin E also affects umami, so this is analyzed.

Since each index included in such reference analysis information also affects the taste of meat, the discrimination accuracy can be improved by discriminating the meat quality through the degree of association in combination with the reference image information.

Both the reference analysis information and the analysis information may be an analysis performed on the meat at the time of slaughter, or may be an analysis performed on the living body of the livestock that provides the meat.

In the example of FIG. 6, it is assumed that the input data is, for example, reference image information P01 to P03 and reference analysis information P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference analysis information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference analysis information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference analysis information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference analysis information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference image information and reference analysis information is likely to be associated with, and is the most reliable from the reference image information and the reference analysis information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, which of the reference image information, the reference analysis information obtained when acquiring the reference image information, and the meat quality in that case was suitable for the discrimination device 2 to discriminate the actual search solution. , Past data are accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

For example, at the time of actual evaluation of meat quality in the past, it is assumed that the reference analysis information has an oleic acid content of 〇〇 and an inosinic acid content of □□ for a certain reference image information. do. In such a case, if there are many cases where the meat quality is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference analysis information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference analysis information P18 is combined with the reference image information P01, and the association degree of the meat quality C is w15 and the association degree of the meat quality E is w16. It has become. The node 61c is a node in which the reference analysis information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the image information of the meat quality to be discriminated and the analysis information are actually acquired. Here, the analysis information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the above-mentioned reference analysis information.

By the way, the reference analysis information is obtained by preliminarily analyzing one or more of free amino acid analysis, fatty acid composition, oleic acid, inosinic acid, guanylic acid, and vitamin E according to this analysis information, and this is used for reference analysis. It is used as information, and the degree of association with the combination with the reference image information is formed.

Based on the image information newly acquired in this way and the analysis information, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the analysis information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In FIG. 7, in addition to the above-mentioned reference image information, the combination with the reference production area information instead of the above-mentioned reference ultrasonic image information and the degree of association with the meat quality for the combination are set to three or more levels. An example is shown.

This reference production area information, which is added as an explanatory variable instead of the reference ultrasonic image information, is information on the production area of the meat, for example, at the national level such as the United States and Japan, at the local level such as the Tohoku region and the Kyushu region, and Hokkaido. It may be shown at the prefectural level such as Kagoshima prefecture, and also at the group or town of Hokkaido, or even at the ranch level. Since the meat production area included in the reference production area information also affects the taste of the meat, the discrimination accuracy can be improved by discriminating the meat quality through the degree of association in combination with the reference image information.

In the example of FIG. 7, it is assumed that the input data is, for example, reference image information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference image information and reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference image information and reference production area information is likely to be associated with, and is the most reliable from the reference image information and the reference production area information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, in determining the actual search solution, the discrimination device 2 is suitable for the reference image information, the reference production area information obtained when the reference image information is acquired, and the meat quality in that case. , Past data are accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the reference image information P01 and the reference production area information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference production area information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the image information of the meat quality to be discriminated and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the newly acquired image information and the production area information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In FIG. 8, in addition to the above-mentioned reference image information, a combination with the reference biometric information instead of the above-mentioned reference ultrasonic image information and a degree of association with the meat quality for the combination are set to three or more levels. An example is shown.

This reference biometric information, which is added as an explanatory variable instead of the reference ultrasound image information, includes any biometric data measured for the livestock living body that provides the meat. The types of livestock biometric data include all biometric data such as livestock heart rate, body temperature, electrocardiogram, blood pressure, blood test results, and weight. Since the data on the living body included in the reference biological information also affects the taste of the meat, the discrimination accuracy can be improved by discriminating the meat quality through the degree of association in combination with the reference image information.

In the example of FIG. 8, it is assumed that the input data is, for example, reference image information P01 to P03 and reference biometric information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference image information and reference biometric information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference biometric information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference biometric information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference biometric information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference image information and reference biometric information are likely to be associated with, and is the most reliable from the reference image information and the reference biometric information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference biometric information, and the meat quality in that case is suitable for discriminating the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference biometric information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 8, the node 61b is a node of the combination of the reference image information P01 and the reference biological information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference biometric information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the image information of the meat quality to be discriminated and the biological information are actually acquired. Here, the biological information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the above-mentioned reference biological information.

Based on the image information newly acquired in this way and the biological information, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, if the newly acquired image information is the same as or similar to P02 and the biometric information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

The above-mentioned biometric information and reference biometric information may be obtained by acquiring biometric data from the living body a plurality of times in time series at time intervals and include the time-series change tendency of the biometric data. This makes it possible to judge the meat quality including the time-series change tendency of the biological data of livestock.

In FIG. 9, in addition to the above-mentioned reference image information, the combination with the reference breeding environment information instead of the above-mentioned reference ultrasonic image information and the degree of association with the meat quality for the combination are set to three or more levels. An example is shown.

This reference breeding environment information, which is added as an explanatory variable instead of the reference ultrasound image information, includes all data regarding the environment in which the livestock that provide the meat is bred. The types of data for this reference breeding environment information include temperature, humidity, wind direction, daily illuminance, degree of indoor lighting, audio data, pest extermination status, cleaning status, manure processing status, etc. It contains all the information about the breeding environment. Since the data included in the reference breeding environment information also affects the taste of the meat, the discrimination accuracy can be improved by discriminating the meat quality through the degree of association in combination with the reference image information.

In the example of FIG. 9, it is assumed that the input data is, for example, reference image information P01 to P03 and reference breeding environment information P18 to 21. The intermediate node shown in FIG. 9 is a combination of reference image information and reference breeding environment information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference breeding environment information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference breeding environment information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference breeding environment information arranged on the left side. In other words, this degree of association is an index indicating what kind of meat quality each reference image information and reference breeding environment information is likely to be associated with, and is based on the reference image information and reference breeding environment information. It shows the accuracy in selecting the most probable meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference breeding environment information, and the meat quality in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 9 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference breeding environment information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 9, the node 61b is a node of the combination of the reference image information P01 and the reference breeding environment information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference breeding environment information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the image information of the meat quality to be discriminated and the breeding environment information are actually acquired. Here, the breeding environment information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the above-mentioned reference breeding environment information.

Based on the image information newly acquired in this way and the breeding environment information, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the breeding environment information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

Further, the breeding environment information may be replaced with the feed information regarding the feed given to the livestock. In such a case, as a substitute for the reference breeding environment information, the degree of association of the reference feed information regarding the feed given to the previously acquired livestock with the reference image information is formed in advance. Then, when the feed information regarding the feed of the livestock that newly provides the meat to be discriminated is acquired, the meat quality is discriminated based on the feed information.

In the present embodiment, the image information and the reference image information are premised on being composed of image data obtained by imaging the meat at the time of slaughter, which is disassembled for meat, but before the meat is provided. It may be composed of image data from the living body of livestock.

In such a case, the image of the living body of the livestock at the time of the biological transaction is taken by the camera. The image of the living body of the livestock is not limited to the case of capturing the state at the time of the biological transaction, and the image of the living body of the livestock in the time zone before that may be captured.

This makes it possible to accurately and automatically determine the meat quality of meat that can be obtained from livestock before it is decomposed as meat, without relying on human sensory evaluation or intuition. Become.

Especially in the living body of livestock, the meat quality may be affected by the posture of the livestock and the movement of the livestock. Therefore, it is necessary to create learning data that can search the meat quality in relation to the image information of the living body of the livestock. Therefore, highly accurate meat quality discrimination can be realized.

Image data of behaviors such as restlessness of livestock, frequency and speed of walking, and riding on other cows are acquired, and this is acquired by image analysis of the image data as necessary. If necessary, a deep learning technique may be used to automatically discriminate based on the feature amount of the analyzed image and convert it into data. Further, such behaviors and movements of livestock may be categorized based on the feature amount of the analysis image and automatically arranged.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily discriminate and search for meat quality without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the above-mentioned input data and output data are not completely the same in the process of training, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A dataset may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference image information, any of the reference ultrasonic image information, the reference analysis information, the reference production area information, the reference biological information, the reference breeding environment information, and the reference food information. The explanation has been given by taking the case of being composed of a combination of the above as an example, but the explanation is not limited to this. In other words, in addition to the reference image information, the degree of association is any two or more of the reference ultrasonic image information, the reference analysis information, the reference production area information, the reference biological information, the reference breeding environment information, and the reference food information. It may be composed of a combination of. In addition to the reference image information, the degree of association is one or more of the reference ultrasonic image information, the reference analysis information, the reference production area information, the reference biological information, the reference breeding environment information, and the reference food information. In addition, other factors may be added to this combination to form a degree of association.

In either case, data is input according to the reference information of the degree of association, and the meat quality is obtained by using the degree of association.

Further, as shown in FIG. 10, the present invention determines the meat quality based on the degree of association between two or more types of information, the reference information U and the reference information V. The reference information Y is the reference image information, and the reference information V is the reference ultrasonic image information, the reference analysis information, the reference production area information, the reference biological information, the reference breeding environment information, and the reference food information. It shall be one of.

At this time, as shown in FIG. 10, the output obtained for the reference information U may be used as input data as it is, and may be associated with the output (flesh quality) via the intermediate node 61 in combination with the reference information V. .. For example, for reference information U (reference image information), after outputting an output solution as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. The output (flesh quality) may be searched.

Further, according to the present invention, instead of determining the meat quality as the output, the livestock breeding condition for improving the meat quality may be used as the output solution. In such cases, livestock breeding conditions should be included instead of meat quality for the dataset that trains the degree of association. Livestock breeding conditions include feed to be fed to livestock, temperature, humidity, wind direction, sunlight intensity, degree of indoor lighting, audio data, pest control status, cleaning status, manure processing status, etc. May be used.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By discriminating the most probable meat quality based on the degree of association represented by the numerical values of three or more stages, the search is performed in descending order of the degree of association under the situation where there are multiple possible candidates for the search solution. It is also possible to display it. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. Remind the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition, when each reference information such as reference image information is acquired and knowledge, information, and data regarding the meat quality and improvement measures for these are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and training the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

Second Embodiment Hereinafter, the second embodiment will be described. In executing this second embodiment, the meat quality discrimination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

In the second embodiment, for example, as shown in FIG. 11, it is premised that the degree of association between the reference analysis information and the meat quality is set in advance in three or more stages. The reference analysis information is the same as that described in the first embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 11, it is assumed that the input data is, for example, reference analysis information P01 to P03. The reference analysis information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference analysis information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference analysis information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference analysis information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference analysis information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference analysis information. Is shown. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets of reference analysis information and which of the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 11 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference analysis information analyzed for the meat in the past. By collecting and analyzing such data sets, the degree of association with the reference analysis information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference analysis information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference analysis information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference analysis information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 11 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 12, reference analysis information is input as input data, meat quality is output as output data, at least one hidden layer is provided between the input node and the output node, and the machine is provided. You may let them learn. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through a data set of the appearance image of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the above-mentioned is used to actually discriminate the meat quality from now on. The meat quality will be searched using the learned data. In such a case, the analysis information obtained by actually analyzing the meat in the area to be discriminated is newly acquired. The newly acquired analysis information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the analysis information newly acquired in this way. In such a case, the degree of association shown in FIG. 11 (Table 1) acquired in advance is referred to. For example, when the newly acquired analysis information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired analysis information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

FIG. 13 shows an example in which, in addition to the above-mentioned reference analysis information, the degree of association between the combination with the reference production area information and the meat quality for the combination is set to three or more levels. The details of the reference production area information are the same as those described in the first embodiment.

In the example of FIG. 13, it is assumed that the input data is, for example, reference analysis information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 13 is a combination of the reference analysis information and the reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference analysis information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference analysis information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference analysis information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference analysis information and reference production area information is likely to be associated with, and is the most reliable from the reference analysis information and the reference production area information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, which of the reference analysis information, the reference production area information obtained when the reference analysis information was acquired, and the meat quality in that case was suitable for the discrimination device 2 to discriminate the actual search solution. , Past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 13 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference analysis information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 13 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 13, the node 61b is a node of the combination of the reference analysis information P01 and the reference production area information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node of the combination of the reference production area information P19 and P21 with respect to the reference analysis information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the analysis information of the meat quality discrimination target and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the newly acquired analysis information and the production area information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 13 (Table 1) acquired in advance is referred to. For example, when the newly acquired analysis information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference biometric information. In such a case, a combination having the reference analysis information and the reference biometric information acquired from the living body of the livestock that provides the meat in the past, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the biological information acquired from the living body of the livestock that newly provides the meat is acquired, and the meat quality is determined based on the reference biological information corresponding to the acquired biological information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 13 with the reference biometric information and the biometric information, respectively.

The above-mentioned biometric information and reference biometric information may be obtained by acquiring biometric data from the living body a plurality of times in a time series at time intervals and include a time-series change tendency of the biometric data. This makes it possible to judge the meat quality including the time-series change tendency of the biological data of livestock.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference breeding environment information. In such a case, a combination having the reference analysis information and the reference breeding environment information regarding the breeding environment of the livestock that provides the meat analyzed in the past, and the degree of association with the meat quality at three or more levels are formed in advance. .. Then, the breeding environment information regarding the breeding environment of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference breeding environment information according to the acquired breeding environment information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference breeding environment information and the breeding environment information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference food information. In such a case, a combination having the reference analysis information and the reference feed information regarding the feed of the livestock that provides the meat analyzed in the past, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the feed information regarding the feed of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference feed information corresponding to the acquired feed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference bait information and the bait information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference site information. The reference part information and the part information referred to here are information indicating from which part of the livestock the meat was taken out, and may be indicated by names such as tan, fin, loin, and chest. , Buttocks, bottom, peach, around ribs, head, legs, chicken wings, etc. may be indicated by itself. Meat quality can be determined more accurately by including information on which part of the livestock the meat is taken from in addition to the reference analysis information. In such a case, a combination having the reference analysis information and the reference part information regarding the part of the livestock from which the meat analyzed in the past has been taken out, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the part information about the part of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference part information corresponding to the acquired part information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference site information and the site information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference elasticity information. The reference elasticity information and the elasticity information referred to here include any information regarding the elasticity of the meat, and are shown, for example, by the relationship of strain with respect to the stress applied to the meat, that is, the elastic modulus. It may be a numerical value measured by an elasticity meter or a hardness tester. The meat quality can be determined more accurately by including the elasticity information of the meat in addition to the reference analysis information. In such a case, a combination having the reference analysis information and the reference elasticity information regarding the elasticity of the meat analyzed in the past, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the elasticity information regarding the elasticity of the meat is acquired, and the meat quality is determined based on the reference elasticity information according to the acquired elasticity information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference elasticity information and the elasticity information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference odor information. The reference odor information and the odor information referred to here include any information regarding the odor emitted from the meat, and may be, for example, those measured via an odor meter or an odor sensor. Since the odor and odor of such meat are also factors that affect the meat quality, the discrimination accuracy can be improved by combining this with the reference analysis information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference analysis information and the reference odor information regarding the odor of the meat analyzed in the past and the degree of association with the meat quality in three or more stages are formed in advance. Then, the odor information regarding the odor of the meat is acquired, and the meat quality is determined based on the reference odor information corresponding to the acquired odor information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference odor information and the odor information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference variety information. The reference breed information and breed information referred to here include any information regarding the breed of the livestock from which the meat has been taken out. For example, in the case of cattle, Japanese Black, Japanese Brown, Japanese Polled, Japanese Shorthorn, etc. Indicates polled species, etc. Since such varieties are also factors that affect the meat quality, the discrimination accuracy can be improved by combining this with the reference analysis information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference analysis information and the reference breed information regarding the breed of the livestock from which the meat analyzed in the past has been taken out, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the breed information regarding the breed of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference breed information according to the acquired breed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 13 with the reference variety information and the variety information, respectively.

Further, in the second embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference breeding period information. The reference breeding period information and breeding period information referred to here indicate how long the livestock were raised before they were made into meat, and may be indicated by the age until they are made into meat. It may be indicated by the number of days or months from birth to meat. Since such a breeding period is also a factor that affects the meat quality, it is possible to improve the discrimination accuracy by combining this with the reference analysis information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference analysis information and the reference breeding period information regarding the breeding period of the livestock providing the meat analyzed in the past and the degree of association with the meat quality at three or more levels are formed in advance. .. Then, the breeding period information regarding the breeding period of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference breeding period information according to the acquired breeding period information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference breeding period information and the breeding period information, respectively.

In the above-mentioned degree of association, in addition to the reference analysis information, the reference production area information, the reference biological information, the reference breeding environment information, the reference food information, the reference site information, the reference elasticity information, and the reference odor It may be composed of a combination of any two or more of information, reference variety information, and reference breeding period information. In addition to the reference analysis information, the degree of association includes reference production area information, reference biological information, reference breeding environment information, reference food information, reference site information, reference elasticity information, reference odor information, and reference. In addition to any one or more of the breed information and the breeding period information for reference, other factors may be added to this combination to form the degree of association.

Further, in this second embodiment, the meat quality may be determined in combination with the first embodiment. That is, in the first embodiment, any reference information corresponding to the input of the neural network may be combined with any reference information corresponding to the input of the neural network to search for the meat quality corresponding to the output.

Third Embodiment Hereinafter, the third embodiment will be described. In executing this third embodiment, the meat quality discrimination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

In the third embodiment, for example, as shown in FIG. 14, it is premised that the elasticity information for reference and the degree of association with the meat quality in three or more stages are preset. The reference elasticity information is the same as that described in the second embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 14, it is assumed that the input data is, for example, reference elasticity information P01 to P03. The reference elasticity information P01 to P03 as such input data is connected to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference elasticity information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference elasticity information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference elasticity information arranged on the left side. In other words, this degree of association is an indicator of what kind of meat quality each reference elasticity information is likely to be associated with, and is used to select the most probable meat quality from the reference elasticity information. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets and analyzes which of the reference elasticity information and the meat quality in that case is adopted and evaluated in discriminating the actual search solution. , The degree of association shown in FIG. 14 is created by analysis.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference elasticity information that has been inspected for the meat in the past. By collecting and analyzing such data sets, the degree of association with the reference elasticity information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference elasticity information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference elasticity information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, this meat quality is evaluated. Set a higher degree of connection. For example, in the example of the elasticity information P01 for reference, the link between the meat quality A and the meat quality C is linked. It is set to 2 points.

Further, the degree of association shown in FIG. 14 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 15, reference elasticity information is input as input data, meat quality is output as output data, and at least one hidden layer is provided between the input node and the output node. You may let it learn by machine. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, elasticity information is newly acquired by actually inspecting the meat in the area to be discriminated. The elasticity information to be newly acquired is input by the above-mentioned information acquisition unit 9.

Based on the elasticity information newly acquired in this way, the meat quality is determined. In such a case, the degree of association shown in FIG. 14 (Table 1) acquired in advance is referred to. For example, when the newly acquired elasticity information is the same as or similar to P02, the meat quality B is associated with the association degree w15 and the meat quality C is associated with the association degree w16 through the association degree. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired elasticity information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

FIG. 16 shows an example in which, in addition to the above-mentioned reference elasticity information, the degree of association between the combination with the reference production area information and the meat quality for the combination is set to three or more levels. The details of the reference production area information are the same as those described in the first embodiment.

In the example of FIG. 16, it is assumed that the input data is, for example, reference elasticity information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 16 is a combination of the reference elasticity information and the reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference elasticity information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference elasticity information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference elasticity information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference elasticity information and reference production area information is likely to be associated with, and is based on the reference elasticity information and reference production area information. It shows the accuracy in selecting the most probable meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in discriminating the actual search solution, the discriminating device 2 prefers the reference elasticity information, the reference production area information obtained when acquiring the reference elasticity information, and the meat quality in that case. Or, by accumulating past data and analyzing and analyzing these, the degree of association shown in FIG. 16 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference elasticity information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 16 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 16, the node 61b is a node of the combination of the reference elasticity information P01 and the reference production area information P18, the association degree of the meat quality C is w15, and the association degree of the meat quality E is w16. It has become. The node 61c is a node in which the reference production area information P19 and P21 are combined with respect to the reference elasticity information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the elasticity information of the meat quality to be discriminated and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the elasticity information newly acquired in this way and the production area information, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 16 (Table 1) acquired in advance is referred to. For example, if the newly acquired elasticity information is the same as or similar to P02, and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference biometric information. In such a case, a combination having the reference elasticity information and the reference biometric information acquired from the living body of the livestock that provides the meat in the past, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the biological information acquired from the living body of the livestock that newly provides the meat is acquired, and the meat quality is determined based on the reference biological information corresponding to the acquired biological information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 16 with the reference biometric information and the biometric information, respectively.

The above-mentioned biometric information and reference biometric information may be obtained by acquiring biometric data from the living body a plurality of times in a time series at time intervals and include a time-series change tendency of the biometric data. This makes it possible to judge the meat quality including the time-series change tendency of the biological data of livestock.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference breeding environment information. In such a case, a combination having the reference elasticity information and the reference breeding environment information regarding the breeding environment of the livestock providing the meat analyzed in the past and the degree of association with the meat quality at three or more levels are formed in advance. back. Then, the breeding environment information regarding the breeding environment of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference breeding environment information according to the acquired breeding environment information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 13 with the reference breeding environment information and the breeding environment information, respectively.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference food information. In such a case, a combination having the reference elasticity information and the reference feed information regarding the feed of the livestock providing the meat analyzed in the past and the degree of association with the meat quality at three or more levels are formed in advance. Then, the feed information regarding the feed of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference feed information corresponding to the acquired feed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 16 with the reference bait information and the bait information, respectively.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference site information. Meat quality can be determined more accurately by including information on the part of livestock from which the meat is taken out, in addition to the reference elasticity information. In such a case, a combination having the reference elasticity information and the reference part information regarding the part of the livestock from which the meat analyzed in the past has been taken out, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the part information about the part of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference part information corresponding to the acquired part information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 16 with the reference site information and the site information, respectively.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference odor information. Since such meat odor and odor are also factors that affect the meat quality, the discrimination accuracy can be improved by combining this with the reference elasticity information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference elasticity information and the reference odor information regarding the odor of the meat analyzed in the past and the degree of association with the meat quality in three or more stages are formed in advance. Then, the odor information regarding the odor of the meat is acquired, and the meat quality is determined based on the reference odor information corresponding to the acquired odor information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 16 with the reference odor information and the odor information, respectively.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference variety information. Since such varieties are also factors that affect the meat quality, the discrimination accuracy can be improved by combining this with the elasticity information for reference and discriminating the meat quality through the degree of association. In such a case, a combination having the reference elasticity information and the reference breed information regarding the breed of the livestock from which the meat analyzed in the past is taken out, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the breed information regarding the breed of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference breed information according to the acquired breed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 16 with the reference variety information and the variety information, respectively.

Further, in the third embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference breeding period information. Since such a breeding period is also a factor that affects the meat quality, it is possible to improve the discrimination accuracy by combining this with the elasticity information for reference and discriminating the meat quality through the degree of association. In such a case, a combination having the reference elasticity information and the reference breeding period information regarding the breeding period of the livestock providing the meat analyzed in the past and the degree of association with the meat quality at three or more levels are formed in advance. back. Then, the breeding period information regarding the breeding period of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference breeding period information according to the acquired breeding period information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 16 with the reference breeding period information and the breeding period information, respectively.

In the above-mentioned degree of association, in addition to the reference elasticity information, the reference production area information, the reference biological information, the reference breeding environment information, the reference food information, the reference site information, the reference elasticity information, and the reference It may be composed of a combination of any two or more of odor information, reference variety information, and reference breeding period information. In addition to the reference elasticity information, the degree of association includes reference production area information, reference biological information, reference breeding environment information, reference food information, reference site information, reference elasticity information, and reference odor information. In addition to any one or more of the reference variety information and the reference breeding period information, other factors may be added to this combination to form the degree of association.

Further, in the third embodiment, as shown in FIG. 17, for example, it is premised that the degree of association between the reference odor information and the meat quality is set in advance in three or more stages. The reference odor information is the same as that described in the second embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 17, it is assumed that the input data is, for example, reference odor information P01 to P03. The reference odor information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference odor information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. Reference odor information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference odor information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference odor information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference odor information. Is shown. In the example of FIG. 17, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets of reference odor information and which of the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 14 is created.

For example, it is assumed that meat quality A is highly evaluated as the meat quality for reference odor information that has been inspected for meat in the past. By collecting and analyzing such a data set, the degree of association with the reference odor information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference odor information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference odor information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference odor information P01, the meat quality A and the meat quality C are linked, but from the previous case, the degree of association of w13 connected to the meat quality A is 7 points, and the degree of association of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 17 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, odor information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired odor information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired odor information in this way, the meat quality is determined. In such a case, the degree of association shown in FIG. 17 (Table 1) acquired in advance is referred to. For example, when the newly acquired odor information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired odor information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

Further, FIG. 18 shows an example in which, in addition to the above-mentioned reference odor information, the degree of association between the combination with the reference production area information and the meat quality for the combination is set to three or more levels. The details of the reference production area information are the same as those described in the first embodiment.

In the example of FIG. 18, it is assumed that the input data is, for example, reference odor information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 18 is a combination of the reference odor information and the reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference odor information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference odor information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference odor information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference odor information and reference production area information is likely to be associated with, and is the most reliable from the reference odor information and reference production area information. It shows the accuracy in selecting a peculiar meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the discrimination device 2 is suitable for the reference odor information, the reference production area information obtained when acquiring the reference odor information, and the meat quality in that case. , Past data are accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 18 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference odor information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 18 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 18, the node 61b is a node of the combination of the reference odor information P01 and the reference production area information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference production area information P19 and P21 are combined with respect to the reference odor information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the odor information of the meat quality to be discriminated and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the newly acquired odor information and the production area information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 18 (Table 1) acquired in advance is referred to. For example, when the newly acquired odor information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the third embodiment, as a substitute for the reference production area information, the reference biological information, the reference breeding environment information, the reference food information, the reference site information, the reference elasticity information, the reference variety information, and the reference The degree of association may be formed by a combination having the breeding period information. In such a case, the reference odor information, the reference biological information, the reference breeding environment information, the reference food information, the reference site information, the reference elasticity information, the reference variety information, and the reference breeding period information. A combination having any of these and a degree of association with meat quality of three or more levels are formed in advance. Then, information is acquired from the living body of the livestock that newly provides the meat, and the meat quality is determined based on the reference information according to the acquired information, which is the same as the above. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 18 with each reference information.

In the above-mentioned degree of association, in addition to the reference odor information, the reference production area information, the reference biological information, the reference breeding environment information, the reference food information, the reference site information, the reference elasticity information, and the reference variety. It may be composed of a combination of any two or more of the information and the reference breeding period information. In addition to the reference odor information, the degree of association includes reference production area information, reference biological information, reference breeding environment information, reference food information, reference site information, reference elasticity information, reference odor information, and reference. In addition to any one or more of the breed information and the breeding period information for reference, other factors may be added to this combination to form the degree of association.

Further, in the third embodiment, the meat quality may be determined in combination with the first and second embodiments. That is, in the first embodiment and the second embodiment, any reference information corresponding to the input of the neural network is combined with any reference information corresponding to the input of the neural network in the third embodiment, and the meat quality corresponding to the output is searched. You may.

Fourth Embodiment Hereinafter, the fourth embodiment will be described. In executing this fourth embodiment, the meat quality discrimination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

In the fourth embodiment, for example, as shown in FIG. 19, it is premised that three or more levels of association between the reference breeding environment information and the meat quality are set in advance. The reference breeding environment information is the same as that described in the first embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 19, it is assumed that the input data is, for example, reference breeding environment information P01 to P03. The reference breeding environment information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference breeding environment information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference breeding environment information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference breeding environment information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference breeding environment information is likely to be associated with, and is used to select the most probable meat quality from the reference breeding environment information. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets of reference breeding environment information and which of the meat quality in that case was adopted and evaluated in discriminating the actual search solution, and analyzes these. , The degree of association shown in FIG. 19 is created by analysis.

For example, it is assumed that meat quality A is highly evaluated as the meat quality for the reference breeding environment information acquired in the past. By collecting and analyzing such data sets, the degree of association with the reference breeding environment information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference breeding environment information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference breeding environment information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, this meat quality is evaluated. Set a higher degree of connection. For example, in the example of the breeding environment information P01 for reference, the link is linked to the meat quality A and the meat quality C. It is set to 2 points.

Further, the degree of association shown in FIG. 19 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 20, reference breeding environment information is input as input data, meat quality is output as output data, and at least one hidden layer is provided between the input node and the output node. You may let it learn by machine. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, the breeding environment information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired breeding environment information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the newly acquired breeding environment information in this way. In such a case, the degree of association shown in FIG. 19 (Table 1) acquired in advance is referred to. For example, when the newly acquired breeding environment information is the same as or similar to P02, the meat quality B is associated with the association degree w15 and the meat quality C is associated with the association degree w16 through the association degree. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired breeding environment information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

FIG. 21 shows an example in which, in addition to the above-mentioned reference breeding environment information, a combination with the reference production area information and a degree of association with the meat quality for the combination are set to three or more levels. The details of the reference production area information are the same as those described in the first embodiment.

In the example of FIG. 21, it is assumed that the input data is, for example, reference breeding environment information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 21 is a combination of the reference breeding environment information and the reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference breeding environment information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference breeding environment information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the meat quality with respect to the reference breeding environment information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference breeding environment information and reference production area information is likely to be associated with, and is based on the reference breeding environment information and reference production area information. It shows the accuracy in selecting the most probable meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 21. That is, in the discrimination device 2, whichever is preferable, the reference breeding environment information, the reference production area information obtained when acquiring the reference breeding environment information, and the meat quality in that case are suitable for discriminating the actual search solution. Or, by accumulating past data and analyzing and analyzing these, the degree of association shown in FIG. 16 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference breeding environment information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 21 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 21, the node 61b is a node of the combination of the reference breeding environment information P01 and the reference production area information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node of a combination of the reference production area information P19 and P21 with respect to the reference breeding environment information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the breeding environment information of the meat quality to be discriminated and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the newly acquired breeding environment information and production area information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 21 (Table 1) acquired in advance is referred to. For example, when the newly acquired breeding environment information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference biometric information. In such a case, a combination having the reference breeding environment information and the reference biological information acquired from the living body of the livestock that provides the meat in the past, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the biological information acquired from the living body of the livestock that newly provides the meat is acquired, and the meat quality is determined based on the reference biological information corresponding to the acquired biological information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 21 with the reference biometric information and the biometric information, respectively.

The above-mentioned biometric information and reference biometric information may be obtained by acquiring biometric data from the living body a plurality of times in a time series at time intervals and include a time-series change tendency of the biometric data. This makes it possible to judge the meat quality including the time-series change tendency of the biological data of livestock.

Further, in the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference food information. In such a case, a combination having the reference breeding environment information and the reference feed information regarding the feed of the livestock that provides the meat analyzed in the past, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the feed information regarding the feed of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference feed information corresponding to the acquired feed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 21 with the reference bait information and the bait information, respectively.

Further, in the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference site information. The meat quality can be determined more accurately by including the information on the breeding environment for reference and the information on which part of the livestock the meat is taken from. In such a case, a combination having the reference breeding environment information and the reference part information regarding the part of the livestock from which the meat analyzed in the past has been taken out, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the part information about the part of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference part information corresponding to the acquired part information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 21 with the reference site information and the site information, respectively.

Further, in the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference variety information. Since such varieties are also factors that affect the meat quality, the discrimination accuracy can be improved by combining this with the reference breeding environment information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference breeding environment information and the reference breed information regarding the breed of livestock from which the meat analyzed in the past is taken out, and the degree of association with the meat quality at three or more levels are formed in advance. Then, the breed information regarding the breed of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference breed information according to the acquired breed information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 21 with the reference variety information and the variety information, respectively.

Further, in the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having the reference breeding period information. Since such a breeding period is also a factor that affects the meat quality, it is possible to improve the discrimination accuracy by combining this with the reference breeding environment information and discriminating the meat quality through the degree of association. In such a case, a combination having the reference breeding environment information and the reference breeding period information regarding the breeding period of the livestock providing the meat analyzed in the past and the degree of association with the meat quality at three or more levels are formed in advance. back. Then, the breeding period information regarding the breeding period of the livestock that provides the meat is acquired, and the meat quality is determined based on the reference breeding period information according to the acquired breeding period information. The details of the discrimination process will be omitted below by replacing the reference production area information and the production area information in the explanation based on FIG. 21 with the reference breeding period information and the breeding period information, respectively.

In the above-mentioned degree of association, in addition to the reference breeding period information, any one of the reference production area information, the reference biological information, the reference food information, the reference site information, the reference breed information, and the reference breeding period information 2 It may be configured in combination with the above. In addition to the reference breeding period information, the degree of association is added to any one or more of the reference production area information, the reference biological information, the reference food information, the reference site information, the reference breed information, and the reference breeding period information. , Other factors may be added to this combination to form the degree of association.

Further, in the fourth embodiment, as shown in FIG. 22, for example, it is premised that the degree of association between the reference bait information and the meat quality is set in advance in three or more stages. The reference bait information is the same as that described in the second embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 22, it is assumed that the input data is, for example, reference bait information P01 to P03. The reference bait information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference bait information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. Reference bait information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference food information arranged on the left side. In other words, this degree of association is an indicator of what kind of meat quality each reference food information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference food information. Is shown. In the example of FIG. 22, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 22. That is, the discrimination device 2 accumulates a past data set as to which of the reference food information and the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 22 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference food information imaged for the meat in the past. By collecting and analyzing such data sets, the degree of association with the reference bait information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference bait information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference bait information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set the degree of association higher. For example, in the example of the reference bait information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 22 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, the food information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired bait information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the newly acquired food information in this way. In such a case, the degree of association shown in FIG. 22 (Table 1) acquired in advance is referred to. For example, when the newly acquired bait information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired bait information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

Further, FIG. 23 shows an example in which, in addition to the above-mentioned reference bait information, a combination with the reference production area information and a degree of association with the meat quality to the combination are set to three or more levels. The details of the reference production area information are the same as those described in the first embodiment.

In the example of FIG. 23, it is assumed that the input data is, for example, reference food information P01 to P03 and reference production area information P18 to 21. The intermediate node shown in FIG. 23 is a combination of the reference food information and the reference production area information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference food information and the reference production area information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference food information and the reference production area information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the meat quality with respect to the reference food information and the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference bait information and reference production area information is likely to be associated with, and is the most reliable from the reference food information and the reference production area information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 23. That is, in determining the actual search solution, the discrimination device 2 is suitable for the reference food information, the reference production area information obtained when the reference food information is acquired, and the meat quality in that case. By accumulating past data and analyzing and analyzing them, the degree of association shown in FIG. 23 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference food information P01 and the reference production area information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 23 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 23, the node 61b is a node of the combination of the reference food information P01 and the reference production area information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node of the combination of the reference production area information P19 and P21 with respect to the reference food information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the bait information of the meat quality to be discriminated and the production area information are actually acquired. Here, the production area information is newly acquired when actually estimating the meat quality, and the acquisition method is the same as the reference production area information described above. The method of acquiring the production area information and reference production area information is to input the keyboard to a device such as a PC or smartphone, or to capture and analyze the character information and the two-dimensional code described on the label on which the production area is written for meat. You may get it by doing.

Based on the newly acquired food information and the production area information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 23 (Table 1) acquired in advance is referred to. For example, if the newly acquired bait information is the same as or similar to P02 and the production area information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the fourth embodiment, as an alternative to the reference production area information, the degree of association may be formed by a combination having reference site information, reference variety information, and reference breeding period information. In such a case, a combination having any of the reference food information, the reference site information, the reference breed information, and the reference breeding period information, and the degree of association with the meat quality at three or more levels are formed in advance. .. Then, information is acquired from the living body of the livestock that newly provides the meat, and the meat quality is determined based on the reference information according to the acquired information, which is the same as the above. The details of the determination process will be omitted below by replacing the reference production area information and the production area information in the description based on FIG. 23 with each reference information.

In addition, in the above-mentioned degree of association, in addition to the reference food information, it may be composed of a combination of any two or more of the reference production area information, the reference site information, the reference variety information, and the reference breeding period information. .. In addition to the reference food information, the degree of association is one or more of the reference production area information, the reference site information, the reference variety information, and the reference breeding period information, and other factors are added to this combination for association. Degrees may be formed.

Further, in the fourth embodiment, the meat quality may be determined in combination with the first to third embodiments. That is, in the first to third embodiments, any reference information corresponding to the input of the neural network may be combined with any reference information corresponding to the input of the neural network to search for the meat quality corresponding to the output. ..

Further, in the fourth embodiment, as shown in FIG. 24, for example, it is premised that the degree of association between the reference production area information and the meat quality in three or more stages is set in advance. The reference production area information is the same as that described in the second embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 24, it is assumed that the input data is, for example, reference production area information P01 to P03. The reference production area information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference production area information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference production area information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference production area information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference production area information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference production area information. Is shown. In the example of FIG. 24, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 24. That is, the discriminating device 2 accumulates past data sets of reference production area information and which of the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 24 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference production area information imaged for the meat in the past. By collecting and analyzing such data sets, the degree of association with the reference production area information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference production area information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference production area information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference production area information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 24 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, the production area information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired production area information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the newly acquired production area information in this way. In such a case, the degree of association shown in FIG. 24 (Table 1) acquired in advance is referred to. For example, when the newly acquired production area information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, it is possible to search for the most suitable meat quality from the newly acquired production area information and display it to the user. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

Further, FIG. 25 shows an example in which, in addition to the above-mentioned reference production area information, the degree of association between the combination with the reference site information and the meat quality for the combination is set to three or more levels. The details of the reference site information are the same as those described in the second embodiment.

In the example of FIG. 25, it is assumed that the input data is, for example, reference production area information P01 to P03 and reference site information P18 to 21. The intermediate node shown in FIG. 25 is a combination of the reference production area information and the reference site information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference production area information and the reference site information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference production area information and the reference site information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference production area information and the reference site information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference production area information and reference site information is likely to be associated with, and is the most reliable from the reference production area information and the reference site information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 23.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference production area information P01 and the reference site information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 25 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 25, the node 61b is a node of the combination of the reference production area information P01 and the reference site information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference site information P19 and P21 are combined with respect to the reference production area information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the production area information and the site information of the meat quality to be discriminated are actually acquired.

Based on the newly acquired production area information and site information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 25 (Table 1) acquired in advance is referred to. For example, when the newly acquired production area information is the same as or similar to P02 and the site information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the fourth embodiment, as a substitute for the reference site information, the degree of association may be formed by a combination having the reference variety information and the reference breeding period information. In such a case, a combination having any of the reference production area information, the reference breed information, and the reference breeding period information, and the degree of association with the meat quality at three or more levels are formed in advance. Then, information is acquired from the living body of the livestock that newly provides the meat, and the meat quality is determined based on the reference information according to the acquired information, which is the same as the above. The details of the discrimination process will be omitted below by replacing the reference site information and the site information in the description based on FIG. 25 with each reference information.

In addition, in the above-mentioned degree of association, in addition to the reference production area information, it may be composed of a combination of any two or more of the reference site information, the reference variety information, and the reference breeding period information. In addition to the reference production area information, the linkage degree is formed by adding any one or more of the reference site information, the reference breed information, and the reference breeding period information, and other factors to this combination. You may.

Further, in the fourth embodiment, the meat quality may be determined in combination with the first to third embodiments. That is, in the first to third embodiments, any reference information corresponding to the input of the neural network may be combined with any reference information corresponding to the input of the neural network to search for the meat quality corresponding to the output. ..

Fifth Embodiment Hereinafter, the fifth embodiment will be described. In executing the fifth embodiment, the meat quality discrimination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

The fifth embodiment is the same as the first embodiment in that the degree of association is formed with the reference image information as an axis. Therefore, the detailed processing operation in the fifth embodiment will be omitted below by diverting the description of the first embodiment.
In the fifth embodiment, for example, as shown in FIG. 26, in addition to the reference image information, a combination with the reference elasticity information and an example in which three or more levels of association with the meat quality for the combination are set. Is shown. The details of the reference elasticity information are the same as those described in the second embodiment.

In the example of FIG. 26, it is assumed that the input data is, for example, reference image information P01 to P03 and reference elasticity information P18 to 21. The intermediate node shown in FIG. 26 is a combination of reference image information and reference elasticity information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference elasticity information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference image information and the reference elasticity information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference image information and the reference elasticity information arranged on the left side. In other words, this degree of association is an index indicating what kind of meat quality each reference image information and reference elasticity information is likely to be associated with, and is based on the reference image information and reference elasticity information. It shows the accuracy in selecting the most probable meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 26. That is, in the discrimination device 2, the reference image information, the reference elasticity information obtained when the reference image information is acquired, and the meat quality in that case are preferable for discriminating the actual search solution. The degree of association shown in FIG. 26 is created by accumulating past data and analyzing and analyzing them.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference elasticity information P20, the meat quality thereof is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 26 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 26, the node 61b is a node of the combination of the reference image information P01 and the reference elasticity information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference elasticity information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the image information of the meat quality to be discriminated and the elasticity information are actually acquired.

Based on the image information newly acquired in this way and the elasticity information, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 26 (Table 1) acquired in advance is referred to. For example, if the newly acquired image information is the same as or similar to P02 and the elasticity information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the fifth embodiment, as an alternative to the reference elasticity information, the degree of association may be formed by a combination having the reference site information. The details of the reference site information and the site information referred to here are the same as those described in the second embodiment. The meat quality can be determined more accurately by including information on which part of the livestock the meat is taken from in addition to the reference image information. In such a case, a combination having the reference image information and the reference part information regarding the part of the livestock from which the meat analyzed in the past has been taken out, and the degree of association with the meat quality in three or more stages are formed in advance. Then, the part information about the part of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference part information corresponding to the acquired part information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the description based on FIG. 26 with the reference site information and the site information, respectively.

In the fifth embodiment, as an alternative to the reference elasticity information, the degree of association may be formed by a combination having the reference odor information. The details of the reference odor information and the odor information referred to here are the same as those described in the second embodiment. The meat quality can be determined more accurately by including information on what the smell of the meat is in addition to the reference image information. In such a case, a combination having the reference image information and the reference odor information regarding the odor of the meat analyzed in the past and the degree of association with the meat quality in three or more stages are formed in advance. Then, the odor information of the meat is acquired, and the meat quality is determined based on the reference odor information corresponding to the acquired odor information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the description based on FIG. 26 with the reference odor information and the odor information, respectively.

In the fifth embodiment, as an alternative to the reference elasticity information, the degree of association may be formed by a combination having the reference product type information. The details of the reference variety information and the variety information referred to here are the same as those described in the second embodiment. The meat quality can be determined more accurately by including information on which breed of livestock the meat is taken from in addition to the reference image information. In such a case, a combination having reference image information and reference breed information regarding the breed of livestock from which the meat analyzed in the past has been extracted is formed in advance with three or more levels of association with the meat quality. Then, the breed of the livestock from which the meat is taken out is perceived, and the meat quality is determined based on the reference breed information according to the acquired breed information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the description based on FIG. 26 with the reference variety information and the variety information, respectively.

In the fifth embodiment, as an alternative to the reference elasticity information, the degree of association may be formed by a combination having the reference breeding period information. The details of the reference breeding period information and the breeding period information referred to here are the same as those described in the second embodiment. The meat quality can be determined more accurately by including the information on the breeding period of the livestock from which the meat was taken out in addition to the reference image information. In such a case, a combination having the reference image information and the reference breeding period information regarding the breeding period of the livestock from which the meat analyzed in the past was taken out, and the degree of association with the meat quality at three or more levels are formed in advance. .. Then, the breeding period information of the livestock from which the meat is taken out is acquired, and the meat quality is determined based on the reference breeding period information according to the acquired breeding period information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the explanation based on FIG. 26 with the reference breeding period information and the breeding period information, respectively.

In the present embodiment, the image information and the reference image information are premised on being composed of image data obtained by imaging the meat at the time of slaughter, which is disassembled for meat, but before the meat is provided. It may be composed of image data from the living body of livestock.

In such a case, the image of the living body of the livestock at the time of the biological transaction is taken by the camera. The image of the living body of the livestock is not limited to the case of capturing the state at the time of the biological transaction, and the image of the living body of the livestock in the time zone before that may be captured.

This makes it possible to accurately and automatically determine the meat quality of meat that can be obtained from livestock before it is decomposed as meat, without relying on human sensory evaluation or intuition. Become.

Especially in the living body of livestock, the meat quality may be affected by the posture of the livestock and the movement of the livestock. Therefore, it is necessary to create learning data that can search the meat quality in relation to the image information of the living body of the livestock. Therefore, highly accurate meat quality discrimination can be realized.

Image data of behaviors such as restlessness of livestock, frequency and speed of walking, and riding on other cows are acquired, and this is acquired by image analysis of the image data as necessary. If necessary, a deep learning technique may be used to automatically discriminate based on the feature amount of the analyzed image and convert it into data. Further, such behaviors and movements of livestock may be categorized based on the feature amount of the analysis image and automatically arranged.

When image information and reference image information are obtained from the living body of the livestock before the meat is provided, acceleration information and audio information may be further acquired from the living body of the livestock.

As an alternative to the reference elasticity information shown in FIG. 26, the degree of association may be formed by a combination having the reference acceleration information. The reference acceleration information and acceleration information referred to here are acceleration data detected by an acceleration sensor attached to the neck or body of livestock such as cows, horses, and pigs. This reference acceleration information clearly reflects the movement of livestock. It is said that depending on the livestock, the restlessness and the speed and frequency of walking will change, but such behavior will be reflected as reference acceleration information detected by the acceleration sensor.

In such a case, a combination having the reference image information and the reference acceleration information acquired from the acceleration sensor of the livestock and the degree of association with the meat quality in three or more stages are formed in advance. Then, the acceleration information acquired from the acceleration sensor of the livestock to be evaluated is acquired, and the meat quality is determined based on the reference acceleration information corresponding to the acquired acceleration information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the description based on FIG. 26 with the reference acceleration information and the acceleration information, respectively.

As an alternative to the reference elasticity information shown in FIG. 26, the degree of association may be formed by a combination having the reference voice information. The reference voice information and voice information referred to here are voice data of livestock that can be detected through, for example, a microphone installed in a stable or the like. By analyzing the sound of livestock, it is possible to determine whether or not the livestock is healthy, and this also affects the meat quality, so it is added to the explanatory variables.

In such a case, a combination having the reference image information and the reference audio information acquired from the microphone and the degree of association with the meat quality at three or more levels are formed in advance. Then, the voice information acquired from the livestock to be evaluated is acquired, and the meat quality is determined based on the reference voice information corresponding to the acquired voice information. The details of the discrimination process will be omitted below by replacing the reference elasticity information and the elasticity information in the description based on FIG. 26 with the reference voice information and the voice information, respectively.

Further, in the fifth embodiment, the meat quality may be determined in combination with the first to fourth embodiments. That is, in the first to fourth embodiments, any reference information corresponding to the input of the neural network may be combined with any reference information corresponding to the input of the neural network to search for the meat quality corresponding to the output. ..

Sixth Embodiment Hereinafter, the sixth embodiment will be described. In executing this sixth embodiment, the meat quality discrimination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by citing the description of the first embodiment.

In the present embodiment, the meat quality is not discriminated from the meat at the time of slaughter, which is disassembled for meat, but the meat quality is judged from the living body of the livestock before the meat is provided.

In such a case, the judgment is made through the acceleration and voice of the livestock in the living body at the time of the biological transaction. The image of the living body of the livestock is not limited to the case of capturing the state at the time of the biological transaction, and the image of the living body of the livestock in the time zone before that may be captured.

This makes it possible to accurately and automatically determine the meat quality of meat that can be obtained from livestock before it is decomposed as meat, without relying on human sensory evaluation or intuition. Become.

Further, in the sixth embodiment, as shown in FIG. 27, for example, it is premised that the degree of association between the reference acceleration information and the meat quality in three or more stages is set in advance. The reference acceleration information is the same as that described in the fifth embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 27, it is assumed that the input data is, for example, reference acceleration information P01 to P03. The reference acceleration information P01 to P03 as such input data is connected to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference acceleration information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference acceleration information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference acceleration information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference acceleration information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference acceleration information. Is shown. In the example of FIG. 27, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 27. That is, the discrimination device 2 accumulates a past data set as to which of the reference acceleration information and the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 27 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference acceleration information imaged for the meat in the past. By collecting and analyzing such a data set, the degree of association with the reference acceleration information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference acceleration information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference acceleration information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference acceleration information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 27 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, acceleration information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired acceleration information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the newly acquired acceleration information in this way. In such a case, the degree of association shown in FIG. 27 (Table 1) acquired in advance is referred to. For example, when the newly acquired acceleration information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired acceleration information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

Further, FIG. 28 shows an example in which, in addition to the above-mentioned reference acceleration information, the degree of association between the combination with the reference food information and the meat quality with respect to the combination is set to three or more levels. The details of the reference bait information are the same as those described in the first embodiment.

In the example of FIG. 28, it is assumed that the input data is, for example, reference acceleration information P01 to P03 and reference bait information P18 to 21. The intermediate node shown in FIG. 28 is a combination of the reference acceleration information and the reference bait information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference acceleration information and the reference bait information is associated with each other through three or more levels of association with the meat quality as this output solution. Reference acceleration information and reference bait information are arranged on the left side through this degree of association, and meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference acceleration information and the reference bait information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference acceleration information and reference bait information is likely to be associated with, and is the most reliable from the reference acceleration information and the reference bait information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 28.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference acceleration information P01 and the reference bait information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 28 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 28, the node 61b is a node of the combination of the reference acceleration information P01 and the reference bait information P18, and the degree of association of the meat quality C is w15 and the degree of association of the meat quality E is w16. It has become. The node 61c is a node in which the reference food information P19 and P21 are combined with respect to the reference acceleration information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the acceleration information of the meat quality discrimination target and the bait information are actually acquired.

Based on the newly acquired acceleration information and bait information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 28 (Table 1) acquired in advance is referred to. For example, if the newly acquired acceleration information is the same as or similar to P02 and the bait information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the sixth embodiment, as a substitute for the reference feed information, reference production area information, reference biological information, reference breeding environment information, reference breed information, reference breeding period information, and reference audio information are used. The combination may be used to form the degree of association. In such a case, a combination having any of the reference acceleration information, the reference production area information, the reference biological information, the reference breeding environment information, the reference breed information, the reference breeding period information, and the reference voice information. , Three or more levels of association with meat quality are formed in advance. Then, information is acquired from the living body of the livestock that newly provides the meat, and the meat quality is determined based on the reference information according to the acquired information, which is the same as the above. For details of the discrimination process, refer to the reference feed information and the feed information in the description based on FIG. 28, each reference information (reference production area information, reference biological information, reference breeding environment information, reference breed information, reference. The following explanation will be omitted by reading each of them as breeding period information and reference audio information).

In the above-mentioned degree of association, in addition to the reference acceleration information, the reference feed information, the reference production area information, the reference biological information, the reference breeding environment information, the reference breed information, the reference breeding period information, and the reference voice. It may be composed of a combination of any two or more of the information. In addition to the reference acceleration information, the degree of association can be any of reference food information, reference production area information, reference biological information, reference breeding environment information, reference breed information, reference breeding period information, and reference voice information. In addition to 1 or more, other factors may be added to this combination to form the degree of association.

Further, in the sixth embodiment, the meat quality may be determined in combination with the first to fifth embodiments. That is, in the first to fifth embodiments, any reference information corresponding to the input of the neural network may be combined with any reference information corresponding to the input of the neural network to search for the meat quality corresponding to the output. ..

Further, in the sixth embodiment, as shown in FIG. 29, for example, it is premised that the degree of association between the reference voice information and the meat quality is set in advance in three or more stages. The reference voice information is the same as that described in the sixth embodiment. The meat quality is also the same as that described in the first embodiment.

In the example of FIG. 29, it is assumed that the input data is, for example, reference voice information P01 to P03. The reference voice information P01 to P03 as such input data is linked to the meat quality as output. In this output, the meat quality as the output solution is displayed.

The reference voice information is associated with each other through three or more levels of association with the meat quality A to D as the output solution. The reference voice information is arranged on the left side through this degree of association, and each meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of which meat quality is highly related to the reference voice information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference voice information is likely to be associated with, and the accuracy in selecting the most probable meat quality from the reference voice information. Is shown. In the example of FIG. 27, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the meat quality as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discrimination device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 27. That is, the discrimination device 2 accumulates a past data set as to which of the reference voice information and the meat quality in that case is adopted and evaluated in discriminating the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 27 is created.

For example, it is assumed that the meat quality A is highly evaluated as the meat quality for the reference voice information imaged for the meat in the past. By collecting and analyzing such a data set, the degree of association with the reference voice information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference voice information P01, analysis is performed from various data as a result of past evaluation of meat quality. In the case of reference voice information P01, if there are many cases of meat quality A, the degree of association that leads to the evaluation of this meat quality is set higher, and if there are many cases of meat quality B, it leads to the evaluation of this meat quality. Set a higher degree of association. For example, in the example of the reference voice information P01, the meat quality A and the meat quality C are linked, but from the previous case, the linkage degree of w13 connected to the meat quality A is 7 points, and the linkage degree of w14 connected to the meat quality C is 2. It is set to a point.

Further, the degree of association shown in FIG. 29 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the meat to be evaluated before and the meat quality actually discriminated and evaluated, the trained data described above is used to actually discriminate the meat quality from now on. Will be used to search for meat quality. In such a case, voice information is newly acquired by actually inspecting the meat in the area to be discriminated. The newly acquired voice information is input by the above-mentioned information acquisition unit 9.

The meat quality is determined based on the newly acquired voice information in this way. In such a case, the degree of association shown in FIG. 29 (Table 1) acquired in advance is referred to. For example, when the newly acquired voice information is the same as or similar to P02, the meat quality B is associated with w15 and the meat quality C is associated with the association degree w16 via the degree of association. In such a case, the meat quality B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, the most suitable meat quality can be searched for and displayed to the user from the newly acquired voice information. By seeing this search result, the user, that is, the meat producer, the distributor, and the distributor can sort the meat based on the searched meat quality, can predict the taste of the meat, and further, the meat can be predicted. You can decide the price of.

Further, FIG. 30 shows an example in which, in addition to the above-mentioned reference voice information, the degree of association between the combination with the reference food information and the meat quality for the combination is set to three or more levels. The details of the reference bait information are the same as those described in the first embodiment.

In the example of FIG. 30, it is assumed that the input data is, for example, reference voice information P01 to P03 and reference food information P18 to 21. The intermediate node shown in FIG. 30 is a combination of the reference voice information and the reference bait information as such input data. Each intermediate node is further linked to the output. In this output, the meat quality as the output solution is displayed.

Each combination (intermediate node) of the reference voice information and the reference bait information is associated with each other through three or more levels of association with the meat quality as this output solution. The reference voice information and the reference food information are arranged on the left side through this degree of association, and the meat quality is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the meat quality with respect to the reference voice information and the reference food information arranged on the left side. In other words, this degree of association is an index showing what kind of meat quality each reference voice information and reference food information is likely to be associated with, and is the most reliable from the reference voice information and the reference food information. It shows the accuracy in selecting a unique meat quality.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 30.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference voice information P01 and the reference food information P20, the meat quality is analyzed from the past data. When there are many cases of meat quality A, the degree of association where this meat quality is connected to A is set higher, and when there are many cases where meat quality is B and there are few cases where meat quality is A, the degree of association where meat quality is connected to B is set. And set the degree of association that the meat quality leads to A low. For example, in the example of the intermediate node 61a, it is linked to the output of meat quality A and meat quality B, but from the previous case, the degree of association of w13 connected to meat quality A is 7 points, and the degree of association of w14 connected to meat quality B is 2 points. Is set to.

Further, the degree of association shown in FIG. 30 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 30, the node 61b is a node in which the reference food information P18 is combined with the reference voice information P01, and the association degree of the meat quality C is w15 and the association degree of the meat quality E is w16. It has become. The node 61c is a node in which the reference food information P19 and P21 are combined with respect to the reference voice information P02, and the degree of association of the meat quality B is w17 and the degree of association of the meat quality D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually searching for meat quality from now on, the above-mentioned trained data will be used. In such a case, the voice information of the meat quality discrimination target and the bait information are actually acquired.

Based on the newly acquired voice information and food information in this way, the optimum meat quality is searched for. In such a case, the degree of association shown in FIG. 28 (Table 1) acquired in advance is referred to. For example, if the newly acquired voice information is the same as or similar to P02 and the bait information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the meat quality C by w19 and the meat quality D by the degree of association w20. In such a case, meat quality C having a higher degree of association is selected as the optimum solution.

In the sixth embodiment, as an alternative to the reference feed information, the degree of association is a combination of reference production area information, reference biological information, reference breeding environment information, reference breed information, and reference breeding period information. May be formed. In such a case, a combination having any of the reference audio information, the reference production area information, the reference biological information, the reference breeding environment information, the reference breed information, the reference breeding period information, and the reference audio information. , Three or more levels of association with meat quality are formed in advance. Then, information is acquired from the living body of the livestock that newly provides the meat, and the meat quality is determined based on the reference information according to the acquired information, which is the same as the above. For details of the discrimination process, refer to the reference feed information and the feed information in the description based on FIG. 30, each reference information (reference production area information, reference biological information, reference breeding environment information, reference variety information, reference. The following explanation will be omitted by replacing each with (breeding period information).

In the above-mentioned degree of association, in addition to the reference audio information, any one of the reference feed information, the reference production area information, the reference biological information, the reference breeding environment information, the reference breed information, and the reference breeding period information 2 It may be configured in combination with the above. In addition to the reference audio information, the degree of association is added to any one or more of the reference feed information, the reference production area information, the reference biological information, the reference breeding environment information, the reference breed information, and the reference breeding period information. , Other factors may be added to this combination to form a degree of association.

1 Meat quality discrimination system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Discrimination unit 28 Storage unit 61 Node

Claims (10)

In a meat quality discrimination program that discriminates meat quality during livestock trading
An information acquisition step for acquiring acceleration information according to the movement of the livestock detected by an acceleration sensor attached to the living body of the livestock to be discriminated, and
Using the reference acceleration information according to the movement of the livestock detected by the acceleration sensor attached to the living body of the livestock in the past and the degree of association with the meat quality in three or more stages, the acceleration information acquired in the above information acquisition step can be used. A meat quality discrimination program characterized by having a computer execute a discrimination step for discriminating meat quality by giving priority to those having a higher degree of association based on the corresponding acceleration information for reference. In the above information acquisition step, further acquisition of feed information regarding the feed given to the livestock to be discriminated is performed.
In the determination step, the combination having the reference acceleration information and the reference feed consumption information regarding the feed applied to the livestock acquired in advance and the degree of association with the meat quality in three or more stages are used, and further, the above information. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference bait information according to the bait information acquired in the acquisition step. In the above information acquisition step, the production area information regarding the above meat production area is acquired, and the information is acquired.
In the discrimination step, the combination having the reference acceleration information and the reference production area information regarding the meat production area imaged in the past and the degree of association with the meat quality in three or more stages are used, and further in the information acquisition step. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference production area information according to the acquired production area information. In the above information acquisition step, biological information acquired from the living body of the livestock that provides the meat is acquired, and the biological information is acquired.
In the discrimination step, the combination having the reference acceleration information and the reference biological information acquired from the living body of the livestock that provides the meat in the past and the degree of association with the meat quality in three or more stages are used, and further described above. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference biometric information corresponding to the biological information acquired in the information acquisition step. In the above information acquisition step, the breeding environment information regarding the breeding environment of the livestock that provides the meat is acquired.
In the discrimination step, the combination having the reference acceleration information and the reference breeding environment information regarding the breeding environment of the livestock that provides the meat imaged in the past and the degree of association with the meat quality in three or more stages are used. The meat quality discrimination program according to claim 1, further comprising discriminating the meat quality based on the reference breeding environment information according to the breeding environment information acquired in the above information acquisition step. In the above information acquisition step, breed information regarding the above livestock breeds is acquired, and the breed information is acquired.
In the discrimination step, the combination having the reference image information and the reference breed information regarding the breed of the livestock inspected in the past and the degree of association with the meat quality in three or more stages are used, and further in the information acquisition step. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference breed information according to the acquired breed information. In the above information acquisition step, the breeding period information regarding the breeding period of the above livestock is acquired, and the information is acquired.
In the discrimination step, the combination having the reference image information and the reference breeding period information regarding the breeding period of the livestock inspected in the past and the degree of association with the meat quality in three or more stages are used to further acquire the information. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference breeding period information acquired in the step. In the above information acquisition step, the audio information obtained by recording the audio of the livestock is acquired, and the voice information is acquired.
In the discrimination step, the combination of the reference image information and the reference voice information obtained by recording the voice of livestock in the past and the degree of association with the meat quality in three or more stages are used, and further in the information acquisition step. The meat quality discrimination program according to claim 1, wherein the meat quality is discriminated based on the reference voice information according to the acquired voice information. In a meat quality discrimination program that discriminates meat quality during livestock trading
An information acquisition step to acquire voice information recorded from the voice of the livestock to be discriminated,
The reference voice information obtained by recording the voice of livestock in the past and the reference voice information corresponding to the voice information acquired in the above information acquisition step are used based on the reference voice information corresponding to the voice information acquired in the above information acquisition step. A meat quality discrimination program characterized by having a computer execute a discrimination step for discriminating meat quality by giving priority to higher ones. The meat quality discrimination program according to any one of claims 1 to 9, wherein in the discrimination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used.

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