WO2021079785A1

WO2021079785A1 - Meat quality distinction program, and system

Info

Publication number: WO2021079785A1
Application number: PCT/JP2020/038582
Authority: WO
Inventors: 綾子澤田
Original assignee: Ａｓｓｅｓｔ株式会社
Priority date: 2019-10-26
Filing date: 2020-10-13
Publication date: 2021-04-29
Also published as: JP2021067618A; JP6732271B1

Abstract

[Problem] To automatically distinguish meat quality with high accuracy and with less reliance on labor. [Solution] A meat quality distinction program for distinguishing meat quality, wherein the program is characterized by causing a computer to execute an information acquisition step for acquiring image information related to the external appearance of meat quality to be distinguished, and a distinction step for using three-stage or higher relatedness between meat quality and reference image information related to the external appearance of meat quality acquired in the past to distinguish meat quality on the basis of reference image information that corresponds to the image information acquired in the information acquisition step.

Description

Meat quality discrimination program and system

The present invention relates to a meat quality discrimination program and system 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 represented 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 that can evaluate meat quality with high accuracy without relying on human sensory evaluation or instrumental analysis has been conventionally desired.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to determine the meat quality of meat with high accuracy and automatically without relying on human sensory evaluation or instrumental analysis. It is to provide a meat quality discrimination program and a system capable of this.

The meat quality discrimination program according to the present invention is a meat quality discrimination program for discriminating the meat quality of meat, which includes an information acquisition step of acquiring image information obtained by imaging the meat to be discriminated, reference image information of the meat imaged in the past, and meat quality. Based on the reference image information corresponding to the image information acquired in the above information acquisition step, the one with the higher degree of association is prioritized and the meat quality is determined. Is characterized by having a computer execute the above.

Even if you do not have any special skills or experience, anyone can easily determine the meat quality with high accuracy without relying on human sensory evaluation or instrumental analysis.

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.

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

FIG. 1 is a block diagram showing an overall configuration of a meat quality discrimination system 1 in which a meat quality discrimination program to which the present invention is applied 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 who uses this system to input various commands and information, and is specifically 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 determination 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 this 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 animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

Database 3 stores various information necessary for determining 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, inosic 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 that was given to livestock that provide meat that was 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 memorized in association with each other.

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 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 determinations, 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 in response 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 determination 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 discrimination unit 27 discriminates 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 technique.

The display unit 23 is composed of 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 this is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is 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 capturing 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. This reference image information may be used to identify the meat quality by analyzing an image captured of the meat. The reference image information is premised on being composed of image data obtained by capturing 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 frost, and may be evaluated based on the judgment 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 crossing", the color of the meat was evaluated based on the BCS (Beef Color Standard) criterion. May be good. The meat quality also includes luster. Meat quality also includes "meat tightness and crush", 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.

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, ok, ordinary expression.

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

Also, as an alternative to meat quality, any one or more of crossbreeding, meat color, meat tightness, and fat color and quality may be output. In such a case, the image data of the meat and any one or more of the crossover, 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 discriminant 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 to improve the 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 various instrumental analyzes.

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 related to each other through the degree of association of 3 or more levels with respect to 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 which meat quality is highly relevant to the reference image 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 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 discriminating 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 meat quality A is often evaluated as the meat quality for reference image information captured for meat in the past. By collecting and analyzing such a data set, the degree of association with the reference image information is strengthened.

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 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. 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 weighting of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such 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 information acquisition unit 9 described above. 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 reference image information described above.

The meat quality is determined based on the newly acquired image information 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 through the degree of association. In such a case, the meat quality B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality C, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

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 select the meat based on the searched meat quality, predict the taste of the meat, and further, the meat. 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 spectrum image and an ultrasonic image may be acquired. In such a case, it is necessary to capture one or more of a spectrum 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 imaged.

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 spectrum image color-coded for each frequency band. On the other hand, the reference ultrasonic image information is the ultrasonic image data of the meat portion imaged in advance in the living body of the livestock that provides the meat.

By combining and determining the reference ultrasonic image information in addition to the reference image information, the meat quality can be determined 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 related to 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 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 ultrasonic image 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 ultrasonic image information is likely to be associated with, and is a reference image information and 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, 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 was learned as a data set and 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 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 association 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 in which the reference ultrasonic image information P14 is combined with the reference image information P01, the association degree of the meat quality C is w15, and the association degree 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 degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the meat quality from now on, the above-mentioned learned data will be used. 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.

Search for the optimum meat quality based on the newly acquired image information and ultrasonic image information in this way. 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, the meat quality C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Table 2 below shows examples of the degree of association w1 to w12 extending from the input.

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 degrees of association w1 to w12 may all have the same value, and the weights 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 at three or more levels. Good.

By the way, the above-mentioned ultrasonic image information and ultrasonic image information are not limited to the case of capturing ultrasonic image data at a certain point in time for livestock applied as meat, and are time-series from the living body of livestock. 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 this 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 at three or more levels. An example is shown.

This reference analysis information, which is added as an explanatory variable instead of the reference ultrasound image information, is all information regarding the results of chemical and physical analysis performed on 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 proportion 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 for 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 combining the meat quality with the reference image information and discriminating the meat quality through the degree of association.

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 related to 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 is 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. To do. In such a case, if there are many cases where the meat quality is determined to be A, these are trained 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. If there are many cases of meat quality A, the degree of association that this meat quality leads to A is set higher, and if there are many cases of meat quality B and there are few cases of meat quality A, the degree of association that meat quality leads 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, the output of meat quality A and meat 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. 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 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 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 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 pre-analyzing any 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 will be used as information, and the degree of association with the combination with the reference image information will be formed.

Search for the optimum meat quality based on the newly acquired image information and analysis information in this way. 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, the meat quality C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In FIG. 7, in addition to the above-mentioned reference image information, a combination with the reference production area information instead of the above-mentioned reference ultrasonic image information and a degree of association with the meat quality for the combination are set at 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, national level such as the United States and Japan, regional level such as Tohoku region and Kyushu region, Hokkaido and 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 combining the meat quality with the reference image information and discriminating the meat quality through the degree of association.

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 related to 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, which of the reference image information, the reference production area information obtained when the reference image 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. 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. If there are many cases of meat quality A, the degree of association that this meat quality leads to A is set higher, and if there are many cases of meat quality B and there are few cases of meat quality A, the degree of association that meat quality leads 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, the output of meat quality A and meat 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. 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 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.

Search for the optimum meat quality based on the newly acquired image information and production area information in this way. 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, the meat quality C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

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 at 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 all biometric data measured for the livestock living body that provides the meat. The types of biological data of livestock include all biological data such as heart rate, body temperature, electrocardiogram, blood pressure, blood test result, and weight of livestock. 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 biological 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 related to 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 indicating 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 was 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. If there are many cases of meat quality A, the degree of association that this meat quality leads to A is set higher, and if there are many cases of meat quality B and there are few cases of meat quality A, the degree of association that meat quality leads 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, the output of meat quality A and meat 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. 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 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.

Search for the optimum meat quality based on the newly acquired image information and biological information in this way. In such a case, the degree of association shown in FIG. 8 (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 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, the meat quality C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Note that the above-mentioned biological information and reference biological information may be obtained by acquiring biological data from the biological body a plurality of times in a time series at time intervals and including the time-series change tendency of the biological 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, a combination with the reference breeding environment information instead of the above-mentioned reference ultrasonic image information and a degree of association with the meat quality for the combination are set at 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 are bred. The types of data for this reference breeding environment information include temperature, humidity, wind direction, sunlight intensity, indoor lighting degree, 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 related to 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 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. If there are many cases of meat quality A, the degree of association that this meat quality leads to A is set higher, and if there are many cases of meat quality B and there are few cases of meat quality A, the degree of association that meat quality leads 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, the output of meat quality A and meat 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. 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 that is a combination of the reference breeding environment information P19 and P21 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 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.

Search for the optimum meat quality based on the newly acquired image information and breeding environment information in this way. 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, the meat quality C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the meat quality D, which has the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

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 previously acquired reference feed information regarding the feed given to the livestock in combination 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 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-mentioned 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 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.

Note that the above-mentioned input data and output data may not be exactly the same in the process of learning, so that 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 description 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 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 the 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 an output solution is output 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 conditions for improving the meat quality may be used as the output solution. In such cases, livestock breeding conditions should be included in place 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, indoor lighting level, audio data, pest extermination 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 in 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 the degree of association 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 users 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 extremely low output such as the degree of association of 1%. It warns 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. is there. 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 that it appears once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize 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 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 is equivalent to learning in terms 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, treatises, 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.

In addition, 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 learning 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.

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

In the meat quality discrimination program that discriminates the meat quality of meat
The information acquisition step to acquire the image information of the meat to be discriminated, and
Using the reference image information of meat captured in the past and the degree of association with meat quality in three or more stages, the degree of association is higher based on the reference image information corresponding to the image information acquired in the above information acquisition step. A meat quality discrimination program characterized in that a computer executes a discrimination step for discriminating meat quality by giving priority to things.
In the above information acquisition step, a spectrum image is acquired as the above image information, and the spectrum image is acquired.
The meat quality discrimination program according to claim 1, wherein in the discrimination step, an image captured as a spectrum image is used as the reference image information.
The information acquisition step acquires the above-mentioned image information obtained by capturing the meat at the time of slaughter and the ultrasonic image information previously captured by the living body of the livestock providing the meat.
In the determination step, a combination having the above-mentioned reference image information of the meat imaged at the time of past slaughter and the reference ultrasonic image information previously imaged with respect to the living body of the livestock providing the meat, and the above-mentioned meat quality The meat quality according to claim 1, wherein the meat quality is determined based on the reference ultrasonic image information according to the ultrasonic image information acquired in the above information acquisition step by utilizing the three or more levels of association of the above. Discrimination program.
In the information acquisition step, as the ultrasonic image information, the time-series change tendency of the ultrasonic image captured a plurality of times in a time series with respect to the living body is acquired.
The meat quality according to claim 3, wherein in the discrimination step, as the reference ultrasonic image information, a time-series change tendency of an ultrasonic image captured a plurality of times in a time series with respect to a living body is acquired. Discrimination program.
The information acquisition step acquires analytical information obtained by analyzing any one or more of free amino acids, fatty acid composition, oleic acid, inosinic acid, guanylic acid, and vitamin E from the meat.
In the discrimination step, the reference image information and any one or more of free amino acid, fatty acid composition, oleic acid, inosinic acid, guanylic acid, and vitamin E according to the analysis information are analyzed in advance for reference analysis. It is characterized in that the meat quality is discriminated based on the combination having information and the degree of association with the meat quality in three or more stages, and further based on the reference analysis information corresponding to the analysis information acquired in the information acquisition step. The meat quality determination program according to claim 1 or 2.
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 determination step, the combination having the reference image information and the reference production area information regarding the meat production area captured 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 determination program according to claim 1 or 2, wherein the meat quality is determined 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 determination step, the combination having the reference image 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, the above The meat quality discrimination program according to claim 1 or 2, 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 information acquisition step, as the biological information, the time-series change tendency of the biological data acquired from the living body a plurality of times in the time series is acquired.
The meat quality discrimination program according to claim 7, wherein in the discrimination step, as the reference biological information, the time-series change tendency of the biological data acquired from the living body a plurality of times in time series is acquired.
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 image information and the reference breeding environment information regarding the breeding environment of the livestock providing 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 or 2, 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, feed information regarding the feed given to the livestock that provides the meat is acquired.
In the discrimination step, the combination having the reference image information and the reference feed information regarding the feed given to the livestock providing the meat imaged in the past and the degree of association with the meat quality in three or more stages are used. Further, the meat quality discrimination program according to claim 1 or 2, wherein the meat quality is discriminated based on the reference bait information according to the bait information acquired in the above information acquisition step.
The meat quality discrimination program according to any one of claims 1 to 10, 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.
In the meat quality discrimination system that discriminates the meat quality of meat
Information acquisition means for acquiring image information of the meat to be discriminated,
Based on the reference image information of the meat captured in the past and the reference image information according to the image information acquired by the information acquisition means by using the three or more levels of association with the meat quality, the degree of association is determined. A meat quality discrimination system characterized in that it is provided with a discrimination means for discriminating meat quality by giving priority to higher ones.