JP2022021267A - Quality determination program of cultured fish - Google Patents

Abstract

To determine the quality of the cultured fish with high accuracy and automatically without depending on manpower more.SOLUTION: A quality determination program of the cultured fish for determining the quality of the cultured fish is characterized by making a computer execute: an information acquisition step of detecting a water temperature in the farm; and a determination step of determining the quality of the cultured fish, by using an association degree of being 3-phases or more between a reference water temperature in the farm detected in the past and the quality of the cultured fish, and on the basis of the reference water temperature according to the water temperature acquired in the above information acquisition step.SELECTED DRAWING: Figure 3

Description

The present invention relates to a quality discrimination program for farmed fish.

The weight of aquaculture is increasing in the fishery industry in recent years. In particular, tuna and eels cannot be caught in large quantities at sea or in rivers compared to the past, and the ratio of aquaculture is increasing.

When aquaculture is carried out, a farm is created, and the farmed fish are raised from fry in the cage, and after they have grown large and have a lot of meat, they are landed. The quality of farmed fish has traditionally been determined by determining the freshness with the human eye. However, evaluation by the human eye may cause blurring, and it is often difficult to make a unified judgment. In addition, it was necessary to determine the quality of the farmed fish with high accuracy without relying on the evaluation by human eyes at the stage of the living body before shipping the farmed fish.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to be able to determine the quality of aquaculture fish with high accuracy when aquaculture fish is cultivated in a farm. The purpose is to provide a quality discrimination program for farmed fish.

The quality discrimination program for farmed fish according to the present invention is an information acquisition step for detecting the water temperature in the farm, the reference water temperature in the farm detected in the past, and the farming in the quality discrimination program program for the farmed fish. It is characterized by having a computer execute a discrimination step for determining the quality of farmed fish based on the reference water temperature according to the water temperature acquired in the above information acquisition step by utilizing the degree of association with the fish quality in three or more stages. ..

Anyone can easily determine the quality of farmed fish with high accuracy without relying on human sensory evaluation without any special skill or experience.

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.

Hereinafter, the quality determination program for farmed fish 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 farmed fish quality discriminating system 1 to which a farmed fish quality discriminating program to which the present invention is applied is implemented. The fish 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 utilizes 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 taking 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 configured by a communication interface for acquiring data about 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.

In addition, the information acquisition unit 9 includes a water temperature gauge for measuring the water temperature of the farm, various sensors for measuring the amount of solar radiation, various sensors for measuring the dissolved oxygen concentration, various sensors for measuring the water quality, and the like. Is also included.

The database 3 stores various information necessary for determining the quality of fish. The information necessary for determining the quality of fish is the reference water temperature in the farm detected in the past, the amount of solar radiation to the farm where the water temperature was measured, and the reference water temperature in the farm where the water temperature was measured. Reference water quality data in the farm where the dissolved oxygen concentration and water temperature were measured, reference movement video information which captured the movement of the farmed fish in the farm where the water temperature was measured, and the inside of the farm where the water temperature was measured. The reference residual feed amount remaining by analyzing the image obtained by the above, the reference residual feed amount of the above residual feed amount to the quality of the previous fish in the farm where the water temperature was measured, etc. Accumulated in relation to quality.

That is, in addition to such reference water temperature, the database 3 contains reference solar radiation amount, reference dissolved oxygen concentration, reference water quality data, reference motion image information, reference residual feed amount, reference residual ratio, and the like. Any one or more and the quality of the farmed fish as output data are stored 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 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 fish quality discrimination system 1 having the above-described configuration will be described.

In the fish quality determination system 1, for example, as shown in FIG. 3, it is premised that the degree of association between the reference water temperature and the quality of the fish is set in advance in three or more stages. The reference water temperature is the water temperature in the farm detected in the past, and also includes information obtained for the first time by analyzing this water temperature. The water temperature may be one obtained by measuring the temperature of water at a certain temporary point, or may be one obtained by obtaining an average value of water temperatures at a plurality of time points. Further, this water temperature may be configured as one data of the time-series transition of the temperature change of water. The degree of growth, movement, and appetite of farmed fish differ depending on the water temperature, and the optimum quality of fish also changes accordingly. Therefore, the water temperature is added to the input (explanatory variable) in the degree of association.

The quality of fish may be expressed in terms of freshness, or may be evaluated in terms of luster or taste. The quality of this fish may be expressed by a ranking evaluated on a 5-point or 10-point set by the system side or the user side. Alternatively, it may simply be a very tasty, tasty, so-so, ordinary expression.

The quality of these fish may be determined based on previously trained features. At this time, artificial intelligence is used to learn the image data of the fish and the quality of the fish, and when actually acquiring the image information for reference, the quality of the fish is checked by comparing with these learned image data. You may try to discriminate.

The quality of the fish 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 tasting the quality of the fish evaluate the taste of each item such as texture, aroma, texture, bitterness, and mellowness in multiple stages, and statistically analyze them. It may be used as a quality evaluation value. Further, the quality of the fish may be determined through a taste sensor capable of detecting the taste, or may be determined through analysis of various instruments.

Further, the freshness of the fish may be evaluated as the quality of the fish itself. Similarly, the freshness of fish may be evaluated by a plurality of inspectors at a plurality of stages for each food item and statistically analyzed as a quality evaluation value.

In the example of FIG. 3, it is assumed that the input data is, for example, reference water temperatures P01 to P03. The reference water temperatures P01 to P03 as such input data are linked to the quality of the fish as an output. In this output, the fish qualities A to D as the output solution are displayed.

The reference water temperature is associated with each other through three or more levels of association with the quality A to D of the fish as the output solution. The reference water temperature is arranged on the left side through this degree of association, and the quality of each fish is arranged on the right side through this degree of association. The degree of association indicates the degree of which fish is highly related to the quality of the reference water temperature arranged on the left side. In other words, this degree of association is an indicator of what fish quality each reference water temperature is likely to be associated with, and the accuracy with which the most probable fish quality is selected from the reference water temperature. 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 quality of the fish 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 discriminating 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 water temperature and the quality of the fish in that case was adopted and evaluated in discriminating the actual search solution. By analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the quality of fish A is the highest as the quality of fish with respect to the reference water temperature measured for the farm in the past. By collecting and analyzing such data sets, the degree of association with the reference water temperature becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference water temperature is P01, analysis is performed from various data as a result of evaluating the quality of fish in the past. When the reference water temperature is P01, if there are many cases of fish quality A, the degree of association that leads to the evaluation of the quality of this fish is set higher, and if there are many cases of fish quality B, the quality of this fish is set. Set a higher degree of association that leads to evaluation. For example, in the example of the reference water temperature P01, the quality A of the fish and the quality C of the fish are linked. The degree is set to 2 points.

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, the reference water temperature is input as input data, the quality of fish 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 water temperature of the farm to be discriminated before and the quality of the fish actually applied, the above-mentioned learning is used to actually discriminate the quality of the fish from now on. We will search for the quality of fish using the completed data. In such a case, the water temperature of the farm to be discriminated is newly acquired. The farm to be discriminated does not necessarily have to be the same as the farm in which the above-mentioned reference water temperature is measured, and may be the water temperature of another farm. The newly acquired water temperature is measured by the above-mentioned information acquisition unit 9.

Based on the newly acquired water temperature in this way, the quality of the fish is determined. 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 water temperature is the same as or similar to P02, the fish quality B is associated with w15 and the fish quality C is associated with the association degree w16 through the degree of association. In such a case, the quality B of the fish 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 quality C of the fish, which has the lowest degree of association but the association itself is recognized, 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 fish quality can be searched for and displayed to the user from the newly acquired water temperature.

In the example of FIG. 5, the degree of association between the reference water temperature and the reference solar radiation amount is formed. The reference amount of solar radiation is the amount of solar radiation to the farm. The growth, appetite, and movement of farmed fish differ depending on the amount of solar radiation, and it also affects the water temperature. Therefore, by combining this with the learning data and making a judgment together with the reference water temperature, it is possible to judge the quality of the fish with higher accuracy. Therefore, in addition to the reference water temperature, the reference solar radiation amount is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data are, for example, reference water temperatures P01 to P03 and reference solar radiation amounts P14 to 17. The intermediate node shown in FIG. 5 is a combination of the reference water temperature and the reference solar radiation amount as such input data. Each intermediate node is further linked to the output. In this output, the quality of the fish as the output solution is displayed.

Each combination of the reference water temperature and the reference insolation (intermediate node) is associated with each other through three or more levels of association with the quality of the fish as this output solution. The reference water temperature and the reference insolation are arranged on the left side through this degree of association, and the quality of the fish is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of the fish with respect to the reference water temperature and the reference insolation arranged on the left side. In other words, this degree of association is an indicator of what fish quality each reference water temperature and reference insolation is likely to be associated with, and is most likely from the reference water temperature and reference insolation. It shows the accuracy in selecting the quality of fish. Therefore, the optimum fish quality will be searched for by combining the reference water temperature and the reference solar radiation amount.

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 discriminating 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 water temperature, the reference solar radiation amount, and the quality of the fish in that case is suitable for discriminating the actual search solution, and these By analyzing and analyzing, the degree of association shown in FIG. 5 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference water temperature is P01 and the reference insolation amount is P16, the quality of the fish is analyzed from the past data. If there are many cases of fish quality A, the degree of association that leads to this fish quality A is set higher, and if there are many cases of fish quality B and there are few cases of fish quality A, it leads to fish quality B. The degree of association is set high, and the degree of association that leads to the quality A of fish is set low. For example, in the example of the intermediate node 61a, the output of the quality A and the quality B of the fish is linked, but from the previous case, the degree of association of w13 connected to the quality A of the fish is set to 7 points, and the degree of association of the w14 connected to the quality B of the fish is set to 7 points. Is set to 2 points.

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 water temperature P01 and the reference solar radiation amount P14, the degree of association of the fish quality C is w15, and the degree of association of the fish quality E is. It is w16. The node 61c is a node of a combination of the reference insolation amount P15 and P17 with respect to the reference water temperature P02, and the degree of association of the fish quality B is w17 and the degree of association of the fish 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 determining the quality of the fish from now on, the above-mentioned trained data will be used. In such a case, the water temperature of the farm where the quality of the fish is to be actually determined and the amount of solar radiation of the farm are input or selected.

Based on the newly acquired water temperature and amount of solar radiation in this way, the optimum fish 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 water temperature is the same as or similar to P02 and the amount of solar radiation is P17, the node 61d is associated with the node 61d through the degree of association, and the node 61d is associated with the node 61d. The fish quality C is associated with w19, and the fish quality D is associated with the degree of association w20. In such a case, the quality C of the fish 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 quality D of the fish, which has the lowest degree of association but the association itself is recognized, 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, 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 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.

FIG. 6 shows an example in which, in addition to the above-mentioned reference water temperature, a combination with the reference dissolved oxygen concentration instead of the above-mentioned reference insolation amount and a degree of association of three or more levels with the fish quality for the combination are set. Is shown.

This reference dissolved oxygen concentration, which is added as an explanatory variable instead of the reference insolation amount, is the dissolved oxygen concentration in the water of the farm. Since such dissolved oxygen concentration is also a factor that greatly affects the growth of farmed fish in relation to the quality of fish, this is added as an explanatory variable.

In the example of FIG. 6, it is assumed that the input data are, for example, reference water temperatures P01 to P03 and reference dissolved oxygen concentrations P18 to 21. The intermediate node shown in FIG. 6 is a combination of the reference water temperature and the reference dissolved oxygen concentration as such input data. Each intermediate node is further linked to the output. In this output, the quality of the fish as the output solution is displayed.

Each combination of reference water temperature and reference dissolved oxygen concentration (intermediate node) is associated with each other through three or more levels of association with fish quality as this output solution. The reference water temperature and the reference dissolved oxygen concentration are arranged on the left side through this degree of association, and the quality of the fish is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of the fish with respect to the reference water temperature and the reference dissolved oxygen concentration arranged on the left side. In other words, this degree of association is an indicator of what kind of fish quality each reference water temperature and reference dissolved oxygen concentration are likely to be associated with, and is the most from the reference water temperature and reference dissolved oxygen concentration. It shows the accuracy in choosing a probable fish 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 water temperature, the reference dissolved oxygen concentration obtained when the reference water temperature was obtained, and the quality of the fish in that case was suitable for the discrimination device 2 to discriminate the actual search solution. , The past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference water temperature is P01 and the reference dissolved oxygen concentration is P20, the quality of the fish is analyzed from the past data. If there are many cases of fish quality A, the degree of association that this fish quality leads to A is set higher, and if there are many cases of fish quality B and there are few cases of fish quality A, the fish quality is Set the degree of association that leads to B high and the degree of association that the quality of fish leads to A low. For example, in the example of the intermediate node 61a, the output of fish quality A and fish quality B is linked, but from the previous case, the degree of association of w13 that leads to fish quality A is 7 points, and the association of w14 that leads to fish quality B. The degree is set to 2 points.

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 of the combination of the reference water temperature P01 and the reference dissolved oxygen concentration P18, the degree of association of the fish quality C is w15, and the degree of association of the fish quality E is. It is w16. The node 61c is a node of a combination of the reference dissolved oxygen concentrations P19 and P21 with respect to the reference water temperature P02, and the degree of association of the fish quality B is w17 and the degree of association of the fish 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 the quality of fish from now on, the above-mentioned trained data will be used. In such a case, the water temperature to be discriminated on the quality of the fish and the dissolved oxygen concentration are actually obtained. Here, the dissolved oxygen concentration is newly acquired for the farm for which the water temperature is to be acquired when the quality of the fish is newly determined, and the acquisition method is the same as the above-mentioned reference dissolved oxygen concentration.

Based on the newly obtained water temperature and the dissolved oxygen concentration, the optimum fish 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, if the newly acquired water temperature is the same as or similar to P02 and the dissolved oxygen concentration 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 fish quality C by w19 and the fish quality D by the degree of association w20. In such a case, the quality C of the fish 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 quality D of the fish, which has the lowest degree of association but the association itself is recognized, 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.

As an alternative to the above-mentioned dissolved oxygen concentration for reference, the above-mentioned degree of association with the reference water quality data may be configured. The reference water quality data referred to here is composed of, for example, the content of the following chemical substances in addition to pH and hardness. Examples of chemicals include antimony and its compounds, uranium and its compounds, nickel and its compounds, 1,2-dichloroethane, toluene, di (2-ethylhexyl) phthalate, chlorous acid, chlorine dioxide, dichloronitrile, residues. Chlorine, calcium, magnesium, etc. (hardness), but are not limited to these.

When inspecting the water quality, the water from the farm may be taken out as a sample and analyzed based on chemical and physical analysis methods.

Such reference water quality data is replaced with an alternative to the reference dissolved oxygen concentration shown in FIG. 6 to form fish quality and association with the reference water temperature via the combination node 61. Then, when the quality of the fish in the farm is actually determined, the water quality data is acquired in addition to the water temperature of the farm. This type of water quality data needs to correspond to the type of reference water quality data.

Then, the quality of the fish is searched based on the degree of association between the reference water quality data according to the acquired water quality data and the reference water temperature according to the acquired water temperature.

FIG. 7 shows an example in which, in addition to the above-mentioned reference water temperature, a combination with the reference movement image information instead of the above-mentioned reference insolation amount and a degree of association of three or more levels with the fish quality for the combination are set. Is shown.

This reference motion video information, which is added as an explanatory variable instead of the reference solar radiation amount, is a moving image of the motion of the farmed fish in the water of the farm. The movement of the farmed fish may be patterned into a predetermined type, which may be used as reference movement video information. In such a case, the trajectory of the movement of the farmed fish may be analyzed through a moving image captured, and the analyzed trajectory may be applied to a predetermined type. Since the movement of the farmed fish included in the reference movement video information also affects the appetite, bite, and growth degree of the farmed fish, this is added to the explanatory variables.

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

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

The discriminating 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 water temperature, the reference movement image information obtained when acquiring the reference water temperature, and the quality of the fish in that case was suitable for the discrimination device 2 to discriminate the actual search solution. , The 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, when the reference water temperature is P01 and the reference motion image information is P20, the quality of the fish is analyzed from the past data. If there are many cases of fish quality A, the degree of association that this fish quality leads to A is set higher, and if there are many cases of fish quality B and there are few cases of fish quality A, the fish quality is Set the degree of association that leads to B high and the degree of association that the quality of fish leads to A low. For example, in the example of the intermediate node 61a, the output of fish quality A and fish quality B is linked, but from the previous case, the degree of association of w13 that leads to fish quality A is 7 points, and the association of w14 that leads to fish quality B. The degree is set to 2 points.

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 movement image information P18 with respect to the reference water temperature P01, the degree of association of the fish quality C is w15, and the degree of association of the fish quality E is. It is w16. The node 61c is a node in which the reference motion image information P19 and P21 are combined with respect to the reference water temperature P02, and the degree of association of the fish quality B is w17 and the degree of association of the fish 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 the quality of fish from now on, the above-mentioned trained data will be used. In such a case, the water temperature to be discriminated on the quality of the fish and the motion image information are actually acquired. Here, the motion video information is newly acquired for the farm for which the water temperature is to be acquired when the quality of the fish is newly determined, and the acquisition method is the same as the reference motion video information described above.

Based on the newly acquired water temperature and the motion image information in this way, the optimum fish 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 water temperature is the same as or similar to P02 and the motion image 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 fish quality C by w19 and the fish quality D by the degree of association w20. In such a case, the quality C of the fish 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 quality D of the fish, which has the lowest degree of association but the association itself is recognized, 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.

FIG. 8 shows an example in which, in addition to the above-mentioned reference water temperature, a combination with the above-mentioned reference residual food amount instead of the above-mentioned reference insolation amount and a degree of association of three or more levels with the fish quality for the combination are set. Is shown.

This reference residual food amount, which is added as an explanatory variable instead of the reference solar radiation amount, indicates the amount of residual food due to the leftover food of the farmed fish in the water of the farm. The measurement of the residual food amount may be obtained by analyzing an image taken by a camera installed in water or an image taken from the ground. Further, the measurement of the residual food amount may be estimated from the above-mentioned water quality data. In such a case, a trained model is created by artificial intelligence between each of the above-mentioned chemical substances constituting the water quality data and the measured residual food amount, with weighting of three or more levels of association with each other. May be used to estimate the amount of residual food through water quality data. In addition, since the color of water changes according to the turbidity of water from the captured image, data on the color of water and the amount of residual feed is collected, and this is machine-learned to obtain the amount of residual feed. May be good. A large amount of residual food indicates that the farmed fish are being supplied with excessive food. Therefore, when the amount of residual food is large, it may be preferable to set the quality of the fish to be slightly lower. Since the residual food amount is a parameter that affects the optimum fish quality in this way, it is added to the explanatory variables.

In the example of FIG. 8, it is assumed that the input data is, for example, the reference water temperature P01 to P03 and the reference residual food amount P18 to 21. The intermediate node shown in FIG. 8 is a combination of the reference water temperature and the reference residual food amount as such input data. Each intermediate node is further linked to the output. In this output, the quality of the fish as the output solution is displayed.

Each combination (intermediate node) of the reference water temperature and the reference residual bait is associated with each other through three or more levels of association with the quality of the fish as this output solution. The reference water temperature and the reference residual bait are arranged on the left side through this degree of association, and the quality of the fish is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the quality of the fish with respect to the reference water temperature and the reference residual food amount arranged on the left side. In other words, this degree of association is an indicator of what kind of fish quality each reference water temperature and reference residual bait is likely to be associated with, and is the most from the reference water temperature and reference residual bait. It shows the accuracy in selecting the quality of a probable fish.

The discriminating 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 water temperature, the reference residual bait amount obtained when the reference water temperature is acquired, and the quality of the fish in that case. , The past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, when the reference water temperature is P01 and the reference residual bait amount is P20, the quality of the fish is analyzed from the past data. If there are many cases of fish quality A, the degree of association that this fish quality leads to A is set higher, and if there are many cases of fish quality B and there are few cases of fish quality A, the fish quality is Set the degree of association that leads to B high and the degree of association that the quality of fish leads to A low. For example, in the example of the intermediate node 61a, the output of fish quality A and fish quality B is linked, but from the previous case, the degree of association of w13 that leads to fish quality A is 7 points, and the association of w14 that leads to fish quality B. The degree is set to 2 points.

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 water temperature P01 and the reference residual bait amount P18, the degree of association of the fish quality C is w15, and the degree of association of the fish quality E is. It is w16. The node 61c is a node of a combination of the reference residual bait amount P19 and P21 with respect to the reference water temperature P02, and the degree of association of the fish quality B is w17 and the degree of association of the fish 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 the quality of fish from now on, the above-mentioned trained data will be used. In such a case, the water temperature for which the quality of the fish is to be determined and the amount of residual food are actually obtained. Here, the residual bait amount is newly acquired for the farm for which the water temperature is to be acquired when the quality of the fish is newly determined, and the acquisition method is the same as the above-mentioned reference residual bait amount.

Based on the newly obtained water temperature and the amount of residual food, the optimum fish 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 water temperature is the same as or similar to P02 and the residual feed amount 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 fish quality C by w19 and the fish quality D by the degree of association w20. In such a case, the quality C of the fish 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 quality D of the fish, which has the lowest degree of association but the association itself is recognized, 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 addition, as an alternative to the above-mentioned residual reference feed amount, the above-mentioned degree of association with the reference residual ratio may be configured. The reference residual ratio referred to here is composed of the ratio of the residual feed amount to the quality of the previous fish in the farm.

Such a reference residual ratio is replaced with a substitute for the reference residual bait amount shown in FIG. 8, and the fish quality and the degree of association are formed through the combination node 61 with the reference water temperature. Then, when the quality of the fish in the farm is actually determined, in addition to the water temperature of the farm, the residual feed amount and the quality of the previous fish are obtained, and the residual ratio consisting of the quality of the previous fish to the residual feed amount is calculated. Ask. Then, the quality of the fish is searched based on the degree of association between the reference water temperature according to the acquired water temperature and the reference residual ratio according to the obtained residual ratio.

The present invention is not limited to the above-described embodiment. The search solution corresponding to the output may determine not only the quality of the fish but also its feeding timing. The feeding timing defines the hour and minute of the day, the feeding time interval, and the like. In such a case, in addition to the quality of the fish, the feeding timing data is read together and trained at the stage of creating the trained model. As a result, not only the quality of the fish but also its feeding timing is output as a search solution.

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 determine and search for the quality of fish 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 input data and the output data described above do not exist completely the same in the process of learning, the information may be classified by type between the input data and the output data. 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 water temperature, any of the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual food amount, and the reference residual ratio. The explanation has been given by taking the case of being composed of combinations as an example, but the explanation is not limited to this. In other words, in addition to the reference water temperature, the degree of association is any two or more of the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, and the reference residual ratio. It may be composed of a combination. In addition to the reference water temperature, the degree of association is added to any one or more of the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual food amount, and the reference residual ratio. , 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 quality of the fish is obtained by using the degree of association.

Further, as shown in FIG. 9, the present invention determines the quality of fish based on the degree of association between two or more types of information, reference information U and reference information V. The reference information Y is the reference water temperature, and the reference information V is the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, and the reference residual ratio. It shall be either.

At this time, as shown in FIG. 9, even if the output obtained for the reference information U is used as input data as it is and is associated with the output (fish quality) via the intermediate node 61 in combination with the reference information V. good. For example, for reference information U (reference water temperature), 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 for output. You may try to search for (fish quality).

Further, according to the present invention, instead of obtaining the quality of the fish as the output as the output solution, it may be reflected in the operation of the feeder that automatically feeds based on the quality of the fish. When the quality of the fish as an output solution comes out, it is sent to the feeder. When this feeder receives the quality of the fish, it automatically feeds the farmed fish according to the quality of the received fish. The feeder may be equipped with, for example, a spraying means for spraying the feed to the farm.

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 determining the most probable fish quality based on the degree of association expressed by the numerical values of three or more stages, in a situation where there are multiple possible candidates for the search solution, the order of the degree of association is high. It is also possible to search and display. 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%. 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. 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 in 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 the reference water temperature is acquired and knowledge, information, and data regarding the quality of fish 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.

Second Embodiment Hereinafter, the second embodiment will be described. In executing this second embodiment, the 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. 10, it is premised that three or more levels of association between the reference image information and the quality of the fish are set in advance. The reference image information is obtained from the image information obtained by capturing an image of the farmed fish, and can be obtained by analyzing the image information. The target farmed fish may be at the stage of fry or at the stage of growing up to just before landing. The image of the farmed fish may be taken by an underwater camera while actually swimming in the farm, or the captured fish may be taken out of the water or put in a container such as a bucket and taken by the camera. You may. This image may be a moving image as well as a still image. This reference image information may be used to identify the quality of the fish by analyzing the image captured of the fish. The fish referred to here also includes all kinds of fish and shellfish such as squid, octopus, sea urchin, salmon roe, and shellfish.

In the example of FIG. 10, 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 quality of the fish as an output. In this output, the quality of the fish as the output solution is displayed.

The reference image information is associated with each other through three or more levels of association with the quality A to D of the fish as the output solution. The reference image information is arranged on the left side through this degree of association, and the quality of each fish is arranged on the right side through this degree of association. The degree of association indicates the degree of which fish is highly related to the quality of the reference image information arranged on the left side. In other words, this degree of association is an indicator of what kind of fish quality each reference image information is likely to be associated with, and is used to select the most probable fish quality from the reference image information. It shows the accuracy. In the example of FIG. 10, 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 quality of the fish 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 discriminating 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 image information and the quality of the fish in that case was adopted and evaluated in discriminating the actual search solution. , The degree of association shown in FIG. 10 is created by analysis.

For example, it is assumed that the quality A of the fish is highly evaluated as the quality of the fish with respect to the reference image information captured on the fish 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 evaluating the quality of fish in the past. In the case of reference image information P01, if there are many cases of fish quality A, the degree of association that leads to the evaluation of the quality of this fish is set higher, and if there are many cases of fish quality B, the quality of this fish. Set a higher degree of association that leads to the evaluation of. For example, in the example of the reference image information P01, the quality A of the fish and the quality C of the fish are linked. The degree of association is set to 2 points.

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, fish 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 appearance image of the fish to be evaluated before and the quality of the fish actually discriminated and evaluated, in order to actually discriminate the quality of the fish from now on. The quality of the fish will be searched using the above-mentioned trained data. In such a case, the image information obtained by actually capturing the image of the fish in the area to be discriminated is newly acquired. The newly acquired image information is input by the above-mentioned information acquisition unit 9. Image information is acquired by capturing an image for which a fish is to be discriminated. This acquisition method may be performed by the same method as the above-mentioned reference image information. That is, a judgment model is generated by machine learning in which the reference image information and the fish quality are used as teacher data, the input is the image information, and the output is the quality of the fish imaged as the target of the image information.

Based on the image information newly acquired in this way, the quality of the fish is determined. 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 fish quality B is associated with w15 and the fish quality C is associated with the association degree w16 via the degree of association. In such a case, the quality B of the fish 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 quality C of the fish, which has the lowest degree of association but the association itself is recognized, 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, it is possible to search for the most suitable fish quality from the newly acquired image information and display it to the user. By looking at this search result, the user, that is, the fish trader, the fish seller, and the distributor can sort the fish based on the quality of the searched fish, predict the taste of the fish, and further, the fish. You can decide the price.

As for the above-mentioned image, any one or more ultrasonic images may be acquired in addition to the image captured by a normal camera. In such a case, it is necessary to capture one or more of a visible image and an ultrasonic image according to the image information to be acquired as reference image information.
The reference image information may be composed of an image of a fish's eyeball or gills. Since the eyeballs and gills of the fish particularly affect the freshness of the fish, the quality of the fish may be determined based on the reference image information and the image information targeting them.
Further, the reference image information and the image information may be extracted from scratches and stains on the skin of the fish.
All of these eyeballs, ella, scratches on the skin, etc. may be extracted by using artificial intelligence deep learning technology.

FIG. 11 shows an example in which, in addition to the above-mentioned reference image information, the degree of association between the combination with the reference solar radiation amount and the quality of the fish with respect to the combination is set to three or more levels.

In the example of FIG. 11, it is assumed that the input data is, for example, reference image information P01 to P03 and reference solar radiation amount P18 to 21. The intermediate node shown in FIG. 11 is a combination of the reference image information and the reference solar radiation amount as such input data. Each intermediate node is further linked to the output. In this output, the quality of the fish as the output solution is displayed.

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

The discriminating 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 solar radiation amount, and the quality of the fish in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 11 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 solar radiation amount P20, the quality of the fish is analyzed from the past data. If there are many cases of fish quality A, the degree of association that this fish quality leads to A is set higher, and if there are many cases of fish quality B and there are few cases of fish quality A, the fish quality is Set the degree of association that leads to B high and the degree of association that the quality of fish leads to A low. For example, in the example of the intermediate node 61a, the output of fish quality A and fish quality B is linked, but from the previous case, the degree of association of w13 that leads to fish quality A is 7 points, and the association of w14 that leads to fish quality B. The degree is set to 2 points.

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. 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. 11, the node 61b is a node of the combination of the reference image information P01 and the reference solar radiation amount P18, the degree of association of the fish quality C is w15, and the degree of association of the fish quality E is. It is w16. The node 61c is a node of a combination of the reference solar radiation amounts P19 and P21 with respect to the reference image information P02, and the degree of association of the fish quality B is w17 and the degree of association of the fish 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 the quality of fish from now on, the above-mentioned trained data will be used. In such a case, the image information of the fish quality to be discriminated and the amount of solar radiation are actually acquired. Here, the amount of solar radiation is newly acquired when actually estimating the quality of the fish, but the acquisition method is the same as the above-mentioned amount of reference solar radiation.

Based on the newly acquired image information and the amount of solar radiation in this way, the optimum fish 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 amount of solar radiation 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 fish quality C by w19 and the fish quality D by the degree of association w20. In such a case, the quality C of the fish 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 quality D of the fish, which has the lowest degree of association but the association itself is recognized, 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. That is, a judgment model is generated by machine learning, in which the image information for reference and the quality of the fish with respect to the amount of solar radiation for reference are used as teacher data, the input is the image information, and the output is the quality of the fish imaged as the target of the image information. It will be.

Also in the second embodiment, as a substitute for the reference solar radiation amount, the reference water temperature, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, the reference residual ratio, etc. The quality of the fish with respect to the degree of association between any one or more of the reference information and the reference image information may be learned in advance. In such a case, in addition to the image information, information corresponding to such reference information is newly acquired, and the quality of the fish as a search solution is obtained by referring to the above-mentioned degree of association.

In the above-mentioned degree of association, in addition to the reference image information, the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, and the reference residual ratio. The explanation has been given by taking the case of being configured in combination with any of them as an example, but the present invention is not limited to this. In other words, in addition to the reference image information, the degree of association is any of the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, and the reference residual ratio. It may be composed of a combination of two or more. In addition to the reference image information, the degree of association can be any of the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference motion image information, the reference residual feed amount, and the reference residual ratio. In addition to 1 or more, other factors may be added to this combination to form the degree of association.

Further, in the second embodiment, the main reference information is used as the reference image information, but the present invention is not limited to this, and the reference motion video information may be substituted for the reference image information. In such a case, the description below will be omitted by replacing all the reference image information in the second embodiment with the reference motion video information. Even in such a case, in the above-mentioned degree of association, in addition to the reference motion image information, the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference residual food amount, and the reference residual ratio. The explanation has been given by taking the case of being configured in combination with any of them as an example, but the present invention is not limited to this. In other words, the degree of association is a combination of any two or more of the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data reference residual food amount, and the reference residual ratio, in addition to the reference motion image information. It may be composed of. In addition to the reference motion image information, the degree of association is added to any one or more of the reference water temperature, the reference solar radiation amount, the reference dissolved oxygen concentration, the reference water quality data, the reference residual food amount, and the reference residual ratio. , Other factors may be added to this combination to form a degree of association.

1 Quality discrimination system for farmed fish 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 (14)

In the quality discrimination program of farmed fish to determine the quality of farmed fish
Information acquisition step to detect the water temperature in the farm and
The quality of the farmed fish is determined based on the reference water temperature according to the water temperature acquired in the above information acquisition step, using the degree of association between the reference water temperature in the farm detected in the past and the quality of the farmed fish in three or more stages. A quality discrimination program for farmed fish, characterized by having a computer perform a discrimination step. The above information acquisition step further acquires the amount of solar radiation to the farm and
In the above discrimination step, the combination having the above reference water temperature and the reference solar radiation amount to the farm where the water temperature is measured and the degree of association with the quality of the farmed fish in three or more stages are used, and further, the above information acquisition step. The quality discrimination program for farmed fish according to claim 1, wherein the quality of the farmed fish is discriminated based on the above combination with the reference solar radiation amount according to the solar radiation amount acquired in 1. The above information acquisition step further acquires the dissolved oxygen concentration in the farm and obtains the dissolved oxygen concentration.
In the above discrimination step, the combination having the above reference water temperature and the reference dissolved oxygen concentration in the farm where the water temperature is measured and the degree of association with the quality of the farmed fish in three or more stages are used to further acquire the above information. The quality determination program for cultured fish according to claim 1, wherein the quality of the cultured fish is determined based on the above combination with the dissolved oxygen concentration for reference according to the dissolved oxygen concentration acquired in the step. The above information acquisition step further acquires water quality data in the farm and
In the above-mentioned discrimination step, the combination having the above-mentioned reference water temperature and the reference water quality data in the farm where the water temperature is measured and the degree of association with the quality of the farmed fish in three or more stages are used, and further, the above-mentioned information acquisition step. The quality discrimination program for farmed fish according to claim 1, wherein the quality of the farmed fish is discriminated based on the above combination with the reference water quality data according to the water quality data acquired in 1. The above information acquisition step further acquires motion video information that captures the motion of farmed fish.
In the above discrimination step, a combination having the above reference water temperature, a reference movement image information that captures the movement of the farmed fish in the farm where the water temperature is measured, and a degree of association with the quality of the farmed fish in three or more stages are used. The quality discrimination program for farmed fish according to claim 1, further comprising discriminating the quality of farmed fish based on the above combination with the motion video information for reference according to the motion video information acquired in the information acquisition step. In the above information acquisition step, the remaining amount of food remaining is acquired by further analyzing the image obtained by imaging the inside of the farm.
In the discrimination step, the combination of the reference water temperature and the remaining reference water amount remaining by analyzing the image obtained by imaging the inside of the farm where the water temperature is measured, and the quality of the farmed fish. The claim is characterized in that the quality of the farmed fish is determined based on the above combination with the residual feed amount for reference according to the residual feed amount acquired in the above information acquisition step by utilizing the degree of association of three or more stages of. 1 The quality discrimination program for farmed fish described. The quality determination program for farmed fish according to any one of claims 1 to 6, wherein the degree of association is composed of nodes of a neural network in artificial intelligence. In the quality discrimination program of farmed fish to determine the quality of farmed fish
The information acquisition step to acquire the image information of the imaged fish in the farm, and
Based on the reference image information corresponding to the image information acquired in the above information acquisition step, using the reference image information obtained by imaging the farmed fish in the farm in the past and the degree of association with the quality of the farmed fish in three or more stages. A quality discrimination program for farmed fish, characterized by having a computer perform a discriminant step to discriminate the quality of the farmed fish. The above information acquisition step detects the water temperature in the farm and
In the above discrimination step, the degree of association between the reference image information, the reference water temperature in the farm where the reference image information is imaged, and the quality of the farmed fish is used in three or more stages, and further acquired in the information acquisition step. The quality determination program for cultured fish according to claim 8, wherein a computer is made to execute a determination step for determining the quality of the cultured fish based on the reference water temperature according to the water temperature. The above information acquisition step further acquires the amount of solar radiation to the farm and
In the above-mentioned discrimination step, the combination having the above-mentioned reference image information and the reference solar radiation amount to the farm where the reference image information is imaged, and the degree of association with the quality of the farmed fish in three or more stages are used, and further. The quality discrimination program for farmed fish according to claim 8, wherein the quality of the farmed fish is discriminated based on the above combination with the reference solar radiation amount according to the solar radiation amount acquired in the information acquisition step. The above information acquisition step further acquires the dissolved oxygen concentration in the farm and obtains the dissolved oxygen concentration.
In the above-mentioned discrimination step, the combination having the above-mentioned reference image information and the reference dissolved oxygen concentration in the farm where the reference image information is imaged, and the degree of association with the quality of the farmed fish in three or more stages are used. The quality discrimination program for farmed fish according to claim 8, further comprising discriminating the quality of farmed fish based on the above combination with the dissolved oxygen concentration for reference according to the dissolved oxygen concentration acquired in the above information acquisition step. The above information acquisition step further acquires water quality data in the farm and
In the above-mentioned discrimination step, the combination having the above-mentioned reference image information and the reference water quality data in the farm where the reference image information is imaged, and the degree of association with the quality of the farmed fish in three or more stages are used, and further. The quality discrimination program for farmed fish according to claim 8, wherein the quality of the farmed fish is discriminated based on the above combination with the reference water quality data corresponding to the water quality data acquired in the above information acquisition step. The above information acquisition step further acquires motion video information that captures the motion of farmed fish.
In the above-mentioned discrimination step, there are three or more stages of a combination having the above-mentioned reference image information, the reference movement image information of the movement of the farmed fish in the farm where the reference image information is imaged, and the quality of the farmed fish. The farmed fish according to claim 8, wherein the quality of the farmed fish is determined based on the above combination with the motion image information for reference according to the motion video information acquired in the above information acquisition step by using the degree of association. Quality discrimination program. In the quality discrimination program of farmed fish to determine the quality of farmed fish
An information acquisition step to acquire motion video information that captures the motion of farmed fish in the farm, and
Reference movement according to the movement video information acquired in the above information acquisition step by using the three or more levels of association between the reference movement video information that captured the movement of the farmed fish in the farm in the past and the quality of the farmed fish. A quality discrimination program for farmed fish, which is characterized by having a computer execute a discrimination step for discriminating the quality of farmed fish based on video information.

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