JP2021128736A - Cultivation environment discrimination program - Google Patents

Abstract

To discriminate a cultivation environment and control the actual growth state of farm crops and a future cultivation method and cultivation environment from the discriminated cultivation environment.SOLUTION: The present invention causes a computer to execute: an association degree acquisition step for preliminarily acquiring a first degree of association at three or more levels between reference supply facility data and a cultivation environment, and preliminarily acquiring a second degree of association at three or more levels between a reference cultivation environment and the growth state of farm crops; an information acquisition step for acquiring facility data that corresponds to the reference facility data; a discrimination step for discriminating a cultivation environment consisting of a temperature or humidity on the basis of the facility data acquired via the information acquisition step and using the first degree of association acquired in the association degree acquisition step; and an estimation step for estimating the growth state of farm crops on the basis of the cultivation environment discriminated by the discrimination step and using the second degree of association acquired in the association degree acquisition step.SELECTED DRAWING: Figure 13

Description

The present invention relates to a cultivation environment determination program for determining the normality of the cultivation environment for cultivating agricultural products.

The quality of the growth of agricultural products varies greatly depending on the cultivation environment. For this reason, crop vendors pay considerable attention to the normality of the cultivation environment. In recent years, the automation of equipment in greenhouses that create a cultivation environment for agricultural products has also progressed, and by optimally controlling the temperature, humidity, fertilizer supply amount, water supply amount, light amount, gas and airflow flow rate, manpower It is also possible to create an optimal cultivation environment without intervention, and it is expected to be a clue to solve the problem of labor shortage in the agricultural industry.

However, when an abnormality such as a failure occurs in the equipment for creating such a cultivation environment, the cultivation environment for agricultural products deteriorates sharply. If the control equipment breaks down, it may take some time to call a contractor to repair it. If possible, it is necessary to detect signs of failure in advance and issue an alert as soon as possible. In particular, if the temperature and humidity control equipment breaks down at the stage of seedlings or before harvesting the crops, it may be too late unless the equipment is finished early. In the past, no particular technique has been proposed in which the normality of this cultivation environment is monitored from the side of the equipment that produces it, and an alert is immediately sent when an abnormality occurs or a sign of it appears. It was the current situation.

In addition, when the cultivation environment can be actually determined, it is necessary for the farmers to understand what the actual growth situation of the crop is from the cultivation environment, and based on the cultivation environment. It is necessary to propose to farmers what kind of cultivation method should be implemented in the future. It is also necessary to propose how to actually control the cultivation environment in the future when the cultivation environment is automatically controlled through a greenhouse or the like. However, the current situation is that the techniques capable of proposing these based on the cultivation environment have not yet been disclosed.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose of the present invention is to control the actual growth status of agricultural products, the future cultivation method, and the cultivation environment from the determined cultivation environment. The purpose is to provide a cultivation environment discrimination program capable of this.

In order to solve the above-mentioned problems, the cultivation environment determination program according to the present invention was acquired from the air conditioning equipment that creates the cultivation environment for cultivating the crops in the cultivation environment determination program for determining the normality of the cultivation environment for cultivating the crops. The first degree of association of three or more stages between the reference equipment data consisting of one or more of power, electricity, voltage, vibration, and sound and the cultivation environment consisting of temperature or humidity is acquired in advance, and it consists of temperature or humidity. From the linkage degree acquisition step that acquires the second degree of association of three or more stages between the reference cultivation environment and the growth status of the crop in advance, and the air conditioning equipment that creates the cultivation environment when newly determining the cultivation environment of the crop. Using the information acquisition step for acquiring the equipment data corresponding to the reference equipment data among any one or more of power, electricity, voltage, vibration, and sound, and the first association degree acquired in the association degree acquisition step, Based on the equipment data acquired through the information acquisition step, the determination step for determining the cultivation environment consisting of temperature or humidity and the second association degree acquired in the association degree acquisition step are used to determine by the determination step. It is characterized by having a computer perform an estimation step of estimating the growth status of crops based on the cultivation environment.

Even if there is no special skill or experience, it is possible to discriminate the cultivation environment and control the actual growth status of crops, future cultivation method, and cultivation environment from the discriminated cultivation environment.

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 the estimation apparatus. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the cultivation environment determination program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG . 1 is a block diagram showing an overall configuration of a cultivation environment determination system 1 in which a cultivation environment determination program to which the present invention is applied is implemented. The cultivation environment discrimination system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties.

Database 3 stores various information necessary for determining the normality of the cultivation environment in which crops are cultivated. Information necessary for determining the normality of the cultivation environment includes reference equipment data acquired from the equipment that creates the cultivation environment for cultivating crops, reference environment data that directly senses the cultivation environment, and direct sensing of the state of the crops. Includes reference crop status data, etc. In the database 3, any one or more of such reference equipment data, reference environment data, and reference crop status data and information on the normality of the cultivation environment in which the actual crop is cultivated are linked and stored. Has been done.

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 discriminating 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, an estimation 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 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 estimation 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 estimation unit 27 estimates the search solution. The estimation 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 estimation operation. The estimation 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 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 cultivation environment determination system 1 having the above-described configuration will be described.

In the cultivation environment determination system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference equipment data and the normality of the cultivation environment are set and acquired in advance. Reference equipment data is data directly acquired from equipment (devices) necessary for creating a cultivation environment. The cultivation environment is determined by temperature, humidity, fertilizer supply amount, pesticide supply amount, light, water supply amount, gas flow rate and airflow flow rate and direction, lighting illuminance, and the like. In agriculture in automated greenhouses and plant factories in recent years, these cultivation environments are created through various facilities when cultivating crops. For example, for temperature and humidity, air conditioning equipment, fertilizer, water, and pesticides are supplied through pipelines and plant equipment for supplying fertilizer. In addition, gas and airflow are also carried out through pipelines and plant equipment. In addition, the illuminance of lighting is performed through lighting equipment.

Reference equipment data includes any data obtained by directly or indirectly attaching sensors or measuring instruments to such equipment. For example, electric power, electricity, voltage, vibration, sound, light, radio waves (hereinafter collectively referred to as physical data), air and liquid flow rates, fertilizer supply amount, and pesticides supplied to the above-mentioned various facilities. The supply amount, the amount of drainage in the drainage facility, etc. are the reference equipment data. Detects operation data consisting of one or more of physical data for operating equipment, supply amount data for supplying fertilizer and water, gas flow rate data for supplying or exhausting gas, supply amount data for pesticides, and light amount of irradiating light. .. By detecting these operation data, it is possible to grasp whether the current state of the cultivation environment is normal or whether some abnormality has occurred. In addition, by detecting these operation data, it is possible to estimate whether the cultivation environment may have an abnormality in the near future or whether it remains normal.

In the example of FIG. 3, it is assumed that the input data is, for example, reference equipment data P01 to P03. The reference equipment data as such input data is linked to the output. In this output, the cultivation environment as the output solution is displayed. The cultivation environment includes whether it is normal or abnormal, and if an abnormality has occurred, the specific content of the abnormality (for example, low temperature, low fertilizer, low light, etc.).

The reference equipment data are associated with each other through three or more levels of association with the cultivation environment as this output solution. The reference equipment data is arranged on the left side through this degree of association, and each cultivation environment is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which cultivation environment with respect to the reference equipment data arranged on the left side. In other words, this degree of association is an index showing what kind of cultivation environment each reference equipment data is likely to be associated with, and is used to select the most probable cultivation environment from the reference equipment data. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the cultivation environment 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 reference equipment data and the equipment data may be replaced with the reference supply amount data and the supply amount data in the second embodiment described later.

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 reference equipment data and past data on how much the cultivation environment was in that case in discriminating the actual search solution, and analyzes and analyzes these. The degree of association shown in FIG. 3 is created in.

For example, suppose that the reference equipment data is data related to vibration generated from the air conditioning equipment. When a specific sign appears in the vibration amount and vibration frequency component of this vibration data, it is assumed that the air conditioning equipment is often damaged several hours to several days later. By collecting and analyzing such a data set, the degree of association between the reference equipment data and the abnormal cultivation environment (for example, the temperature of the cultivation environment becomes low due to the breakage of the air conditioner) becomes strong.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference equipment data P01, analysis is performed from the data of the past cultivation environment. In the case of reference equipment data P01, if there are many cases of abnormal cultivation environment, the degree of association leading to this abnormality is set higher, and if there are many cases of normal cultivation environment, this leads to normality. Set a higher degree of association. For example, in the example of reference equipment data P01, there is a link between anomaly (low temperature) and anomaly (less fertilizer), but from the previous case, the degree of association of w13 that leads to anomaly (low temperature) is 7 points. In addition, the degree of association of w14, which leads to abnormalities (less fertilizer), 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.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually trying to determine a new cultivation environment from now on, the cultivation environment will be determined using the above-mentioned learned data. In such a case, the equipment data of various equipments that create the cultivation environment is newly acquired.

The newly acquired equipment data is input by the above-mentioned information acquisition unit 9.

Based on the equipment data newly acquired in this way, the cultivation environment actually created by the equipment 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 equipment data is the same as or similar to P02, "normal" is associated with w15 and "abnormal (less fertilizer)" is associated with association degree w16 through the association degree. ing. In such a case, "normal" with a higher 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 it is also possible to select "abnormality (less fertilizer)" as the optimum solution, in which the degree of association is low but the association itself is recognized. .. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, from the newly acquired equipment data, it is possible to grasp whether or not an abnormality has occurred in the cultivation environment, and if necessary, the details of the abnormality that has occurred. In addition, it is possible to discriminate not only the abnormalities currently occurring in the cultivation environment but also the abnormalities that may occur in the near future. In such a case, as the data set to be trained, the equipment data acquired in time series is used, and as a result, whether or not an abnormality has occurred is linked. For example, when the power acquired in chronological order gradually weakens and eventually the equipment breaks down and becomes an abnormal state, the process in which the power gradually weakens in chronological order is linked to the abnormal state of the cultivation environment. learn. By learning with such a learning data set and associating them through the degree of association, it is possible to determine the abnormal state in the future. In addition, if an abnormal situation occurs at the present time or in the future, an alert for alerting can be sent.

In the example of FIG. 4, it is premised that a combination of the reference equipment data and the reference environment data is formed. The reference environment data is data obtained by directly sensing the cultivation environment, and clearly represents the state of the cultivation environment at the time of the sensing. The cultivation environment sensed as the reference environment data may be any one or more of temperature, humidity, fertilizer supply amount, light amount, water supply amount, and gas or air flow rate.

In the example of FIG. 4, it is assumed that the input data is, for example, reference equipment data P11 to P13 and reference environment data P14 to 17. The intermediate node shown in FIG. 4 is a combination of the reference equipment data and the reference environment data as such input data. Each intermediate node is further linked to the output. In this output, the cultivation environment as the output solution is displayed.

Each combination (intermediate node) of the reference equipment data and the reference environment data is associated with each other through three or more levels of association with the normality of the cultivation environment as this output solution. The reference equipment data and the reference environment data are arranged on the left side through this degree of association, and the normality of the cultivation environment is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the cultivation environment with respect to the reference equipment data and the reference environment data arranged on the left side. In other words, this degree of association is an index showing what kind of cultivation environment normality each reference equipment data and reference environment data are likely to be associated with, and the reference equipment data and reference environment. It shows the accuracy in selecting the most probable normality of the cultivation environment from the data. In the example of FIG. 4, 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 price as an output, and conversely. The closer to one point, the lower the degree of association between each combination as an intermediate node with the normality of the cultivation environment as an 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 the reference equipment data, the reference environment data, and the past data on how much the cultivation environment was in that case in order to discriminate the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 4 is created.

For example, it is assumed that the equipment data sensed in the past is a certain amount of water supplied per unit time (for example, 1 liter per minute). Further, it is assumed that the reference environment data is "temperature is 34 ° C.". If there is a clear shortage of water to supply to the crops due to the relationship between the amount of water supplied and the temperature, the cultivation environment is considered abnormal (water shortage), and these are learned as a data set and described above. It is defined in the form of the degree of association.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference equipment data P01 and the reference environment data P16, the normality of the cultivation environment is analyzed from the past data. When the cultivation environment has many cases of "abnormality (low temperature)", the degree of association leading to this "abnormality (low temperature)" is set higher, and there are many cases of "normal", and "abnormality (low temperature)". When there are few cases of "low temperature", the degree of association leading to "normal" is set high, and the degree of association leading to "abnormal (low temperature)" is set low. For example, in the example of the intermediate node 61a, it is linked to the output of "abnormal (low temperature)" and "normal", but from the previous case, the degree of association of w13 leading to "abnormal (low temperature)" is 7 points. In addition, the degree of association of w14 that leads to "normal" is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference environment data P14 is combined with the reference equipment data P01, and the degree of association of "abnormality (less fertilizer)" is w15, " The degree of association of "normal" is w16. The node 61c is a node that is a combination of the reference environment data P15 and P17 with respect to the reference equipment data P02, and the "normal" association degree is w17 and the "abnormal (less light)" association degree is w18. It has become.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the normality of the cultivation environment from now on, the above-mentioned learned data will be used to determine the normality. In such a case, it is acquired by actually measuring the equipment data that creates the cultivation environment for the discrimination and the environmental data.

The newly acquired equipment data and environmental data may be acquired via various sensors and the information acquisition unit 9.

Based on the newly acquired equipment data and environmental data in this way, the normality of the cultivation environment in which the newly acquired equipment data and environmental data are actually acquired is determined. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired equipment data is the same as or similar to P02 and the environment data is P17, the node 61d is associated through the degree of association, and this node In 61d, "abnormality (less fertilizer)" is associated with w19, and "abnormality (less light)" is associated with a degree of association w20. In such a case, "abnormality (less fertilizer)" with a higher 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 it is also possible to select "abnormality (less light)" as the optimum solution, in which the degree of association is low but the association itself is recognized. .. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees 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.

FIG. 5 shows an example in which the combination of the above-mentioned reference equipment data and the reference crop condition data and the degree of association with the cultivation environment for the combination are set at three or more levels.

As the input data, such reference equipment data and reference crop condition data are arranged side by side. The intermediate node shown in FIG. 5 is a combination of the reference equipment data and the reference crop status data as such input data.

The reference crop condition data is data obtained by sensing the condition of the crop. The state of the crop can be acquired, for example, through image data. All the conditions in which crops are dead, sick, or eaten by pests can be grasped from image analysis.

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 reference equipment data, reference crop condition data, and data on how much the cultivation environment was in that case in order to discriminate the normality of the actual cultivation environment. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created. The normality of the cultivation environment can be determined not only from the equipment data but also from the condition of the crops. By combining these equipment data and crop condition data, it is possible to determine the normality of the cultivation environment with higher accuracy.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that is a combination of the reference equipment data P01 and the reference crop state data P18 (for example, discolored by being eaten by a pest). The degree of association of "abnormal (less fertilizer)" is w15, and the degree of association of "normal" is w16.

Similarly, when such a degree of association is set, equipment data is newly acquired and crop condition data is acquired. The equipment data corresponds to the reference equipment data, and the crop condition data corresponds to the reference crop condition data.

In determining the cultivation environment, the degree of association shown in FIG. 5 acquired in advance is referred to. For example, when the acquired equipment data is the same as or similar to the reference equipment data P02 and the acquired crop state data corresponds to the reference crop state data P19, the combination is associated with the node 61c. In this node 61c, "normal" is associated with the degree of association w17, and "abnormality (less light)" is associated with the degree of association w18. As a result of such a degree of association, the normality of the cultivation environment in the cultivation building where the newly acquired equipment data and the crop condition data are actually acquired will be determined based on w17 and w18.

FIG. 6 shows an example in which the combination of the above-mentioned reference equipment data and the reference crop growth phase data and the degree of association with the cultivation environment for the combination are set at three or more levels.

As input data, such reference equipment data and reference crop cultivation phase data are arranged side by side. The intermediate node shown in FIG. 6 is a combination of the reference equipment data and the reference crop growth data as such input data.

The reference crop growing phase data shows the growing status of the crop. In other words, it indicates the growing phase of whether the crop is just sowing seeds, just planting seedlings, planting seedlings for one month, two months, three months ..., or in the harvesting season. It is a thing.

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 reference equipment data, reference crop growing phase data, and data on how much the cultivation environment was in that case in order to discriminate the normality of the actual cultivation environment. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created. The normality of the cultivation environment is affected not only by the equipment data but also by the crop growing phase. For example, except when the crops are planted, the cultivation environment is judged to be "normal" because even if the air conditioning equipment breaks down and the temperature drops sharply, there is little effect on the cultivation of the crops. If the crop is at the time of planting the seedlings, and if the air conditioning equipment breaks down at this stage and the temperature drops suddenly, it will die at once, it is judged to be "abnormal". In this way, whether or not the cultivation environment is normal is largely controlled by the crop growth phase data in addition to the equipment data. Therefore, by combining these equipment data with the crop growing phase data, it is possible to determine the normality of the cultivation environment with higher accuracy.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference equipment data P01 is combined with the reference crop status data P22 (for example, 3 months after the time when the seedlings are planted). The degree of association of "abnormal (less fertilizer)" is w15, and the degree of association of "normal" is w16.

Similarly, when such a degree of association is set, equipment data is newly acquired and crop growth phase data is acquired. The equipment data corresponds to the reference equipment data, and the crop growing phase data corresponds to the reference crop growing phase data.

In determining the cultivation environment, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, when the acquired equipment data is the same as or similar to the reference equipment data P02 and the acquired crop growth phase data corresponds to the reference crop growth phase data P23, the combination is associated with the node 61c. In this node 61c, "normal" is associated with the degree of association w17, and "abnormality (less light)" is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the normality of the cultivation environment in the cultivation building where the newly acquired equipment data and the crop growth phase data are actually acquired will be determined.

In FIG. 7, in addition to the above-mentioned reference equipment data and reference environment data, the degree of association between the combination of the reference crop condition data and the normality of the cultivation environment with respect to the combination is set. An example is shown.

In such a case, as shown in FIG. 7, the degree of association is such that the set of the combination of the reference equipment data, the reference environment data, and the reference agricultural product state data is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 7, the reference equipment data P02 is associated with the reference degree w3, the reference environment data P15 is associated with the association degree w7, and the reference crop state data P19 is associated with the association degree w11. Similarly, in the node 61e, the reference crop data P03 is associated with the association degree w5, the reference environment data P15 is associated with the association degree w8, and the reference crop state data P18 is associated with the association degree w10.

Similarly, when such a degree of association is set, the cultivation environment is determined based on the newly acquired equipment data, environmental data, and crop condition data.

In determining the cultivation environment, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, when the acquired equipment data is the same as or similar to the reference equipment data P02, the acquired environmental data corresponds to the reference environment data P15, and the acquired agricultural product status data corresponds to the reference agricultural product status data P19. The combination is associated with a node 61c, in which "normal" is associated with a degree of association w17 and "abnormal (less light)" is associated with a degree of association w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

When combining three or more types of such input parameters, in addition to the reference equipment data, the combination is composed of any two or more of the reference environment data, the reference crop status data, and the reference crop growth phase data. It is applicable even if it has been done.

Second Embodiment Hereinafter, the second embodiment will be described. The system used in the second embodiment uses the cultivation environment determination system 1 described with reference to FIGS. 1 and 2 in the above-described first embodiment.

FIG. 8 is based on the premise that three or more levels of association between the reference supply amount data and the supply and demand environment are set and acquired in advance. Reference supply data is information on the supply of water, fertilizers, pesticides, etc. supplied to plants through pipelines and plant equipment. This supply amount may be expressed in units such as liters / second.

Reference supply data includes any data obtained by directly or indirectly attaching sensors or instruments to such supplies. The amount of water supplied to the various facilities described above, the amount of fertilizer supplied, and the amount of pesticide supplied are obtained through data measured through, for example, a flow meter or the remaining amount of liquid in a tank that stores these liquids. can do.

The supply and demand environment is, for example, too much pesticide, less water, less fertilizer, more pesticide, etc., is the water, fertilizer, and pesticide actually supplied to the plant actually sufficient or insufficient? Furthermore, it is data showing whether there is an oversupply and whether the supply-demand relationship is in a good condition. Regarding this supply and demand environment, if there is little water, the crops tend to wilt, and if there is a lot of water, puddles will form in the soil of the crops. After human identification of these situations, the degree of supply may be determined numerically and converted into data, or an image of the agricultural product or soil may be imaged to image the degree of wilting of the agricultural product or the degree of water accumulation in the soil. The supply and demand environment may be converted into data by performing analysis and, if necessary, automatically determining based on the feature amount of the analyzed image using deep learning technology. As for pesticides, if the amount of pesticides is too small, pests will eat more worms, but the degree may be determined by humans or the degree may be extracted by analyzing the captured image and used as data of the supply and demand environment.

By learning the data set of the reference supply amount data thus obtained and the data of the supply and demand environment with respect to the data set, the degree of association shown in FIG. 8 is constructed.

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.

In such a case, as shown in FIG. 9, reference supply amount data is input as input data, each supply and demand environment is output as output data, and at least one or more hidden layers are provided between the input node and the output node. It may be made to be machine-learned. On the contrary, the supply and demand environment may be configured to be an input and the reference supply amount data may be output.

In the example of FIG. 8, it is assumed that the input data is, for example, reference supply amount data P01 to P03. The reference supply amount data as such input data is linked to the output. In this output, the supply and demand environment as an output solution is displayed.

The reference supply amount data are related to each other through three or more levels of association with the supply and demand environment as this output solution. The reference supply data is arranged on the left side through this degree of association, and each supply and demand environment is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which supply and demand environment the reference supply amount data arranged on the left side. In other words, this degree of association is an indicator of what supply and demand environment each reference supply data is likely to be associated with, and is used to select the most probable supply and demand environment from the reference supply data. It shows the accuracy in. In the example of FIG. 8, 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 supply and demand environment 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 on the reference supply amount data and the supply / demand environment in that case in order to discriminate the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 8 is created.

For example, suppose that the reference supply data is data related to the fertilizer supply. Regarding the amount of fertilizer supplied, it is analyzed that when the crops were actually cultivated, the crops did not grow very large in many cases, and the cause was the lack of fertilizer. By collecting and analyzing such data sets, the degree of association between the reference supply data and the supply and demand environment of too little fertilizer is strengthened.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference supply amount data P01, analysis is performed from the data of the past supply and demand environment. In the case of reference supply data P01, if there are many cases where the supply and demand environment is oversupplied with water, the degree of association that leads to the oversupply of water is set higher, and the supply and demand environment is balanced. If there are many, set a higher degree of association that leads to this balanced case. For example, in the example of the supply amount data P01 for reference, if there are too many pesticides, it is linked to less pesticides. The degree of association of the connected w14 is set to 2 points.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually trying to determine the supply and demand environment from now on, the supply and demand environment will be determined by using the above-mentioned learned data. In such a case, the supply amount data of various facilities that create the supply and demand environment is newly acquired.

The newly acquired supply amount data is input by the information acquisition unit 9 described above.

Based on the supply amount data newly acquired in this way, the supply and demand environment actually created by the equipment is determined. In such a case, the degree of association shown in FIGS. 8 and 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired supply amount data is the same as or similar to P02, "less water" is associated with w15 and "less pesticide" is associated with w16 through the degree of association. ing. In such a case, “less water” with a higher 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 it is also possible to select "there are few pesticides" as the optimum solution, although the degree of association is low but the association itself is recognized. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to grasp the details of the state of the supply and demand environment from the newly acquired supply amount data. Further, it is possible not only to determine the bad supply and demand environment currently occurring in the supply and demand environment, but also to determine that an imbalance between excess demand and excess supply may occur in the near future. In such a case, as the data set to be trained, the supply amount data acquired in time series is used, and as a result, whether or not an imbalance has occurred is linked. For example, when the supply amount acquired in chronological order eventually deteriorates the yield of agricultural products due to oversupply, the process of time-series change in the supply amount is learned in association with the imbalance of the supply and demand environment. By training with such a learning data set and associating it through the degree of association, it becomes possible to determine the imbalance in the future. Also, if an imbalance occurs at the present time or in the future, an alert to call attention can be sent.

When newly determining the supply and demand environment, the supply amount data corresponding to the reference supply amount data among any one or more of the fertilizer supply amount, the pesticide supply amount, and the water supply amount is acquired from the plant equipment. Is a prerequisite. For example, when the above-mentioned degree of association is constructed in relation to the reference supply amount data consisting of the supply amount of pesticides, the supply amount data acquired at the time of this new determination also corresponds to the supply of pesticides. Get things about quantities. In such a case, the supply and demand environment acquired as a data set will determine the supply and demand environment of pesticides.

The reference supply amount data may be composed of different combinations of fertilizer supply amount, pesticide supply amount, and water supply amount. For example, when the reference supply amount data P01 shown in FIG. 9 is the fertilizer supply amount and the reference supply amount data P02 is the water supply amount, the supply and demand environment is one search solution for the supply and demand environment of fertilizer and water. It may be displayed according to the inside.

Note that this reference supply amount data is not limited to the case where it is composed of liquids and solids such as fertilizer supply amount, pesticide supply amount, and water supply amount, and is not limited to gas, oxygen, carbon dioxide, and nitrogen. The same may be applied to the case of supplying a gas such as a constituent component of air such as.

In such a case, the degree of association is configured in advance between the reference supply amount data of these gases and the supply and demand environment as shown in FIGS. 8 and 9. Then, when it is desired to newly determine the supply and demand environment, the current gas supply amount data is newly acquired and the actual supply and demand environment is determined. In such a case, the supply and demand environment is determined by the same method with reference to the degree of association shown in FIGS. 8 and 9 (Table 1) acquired in advance. The amount of gas supplied may be measured by installing a flow meter in the pipeline that supplies the gas. The acquisition of supply and demand environment data is the same as described above.

In this way, even when the gas is supplied, it is possible to grasp the details of the state of the supply and demand environment from the newly acquired supply amount data. Further, it is possible not only to determine the bad supply and demand environment currently occurring in the supply and demand environment, but also to determine that an imbalance between excess demand and excess supply may occur in the near future. In such a case, as the data set to be trained, the supply amount data acquired in time series is used, and as a result, whether or not an imbalance has occurred is linked. For example, when the supply of oxygen acquired in chronological order eventually deteriorates as a result of insufficient supply, the process of chronological change in the supply is linked to the imbalance of the oxygen supply and demand environment. To learn. By training with such a learning data set and associating it through the degree of association, it becomes possible to determine the imbalance in the future. Also, if an imbalance occurs at the present time or in the future, an alert to call attention can be sent.

The gas supply and demand environment is not limited to the case of determining via the reference supply amount data, and may be determined via the reference displacement data as shown in FIG.

In such a case, the reference displacement data consisting of the displacement of gas acquired from the pipeline that creates the cultivation environment for cultivating agricultural products and the degree of association with these supply and demand environments at three levels or more are acquired in advance. .. Acquire gas displacement data when newly determining the supply and demand environment. Next, the supply and demand environment is determined based on the acquired displacement data using the acquired degree of association. Even when the gas is exhausted, it is possible to grasp the details of the state of the supply and demand environment from the newly acquired exhaust amount data.

In the second embodiment, the case of determining the supply and demand environment has been described as an example, but the present invention is not limited to this. From this supply and demand environment, it is possible to determine whether or not the current state of the cultivation environment is normal. For example, when it is judged that the supply and demand environment is just right, or when the balance between supply and demand is not enough to hinder the growth of crops, it is judged that the cultivation environment is normal. .. On the other hand, if the supply and demand environment is largely out of balance, such as "too much displacement" or "too little displacement", it may be determined to be abnormal.

In this way, normal and abnormal may be linked to the output of each supply and demand environment, and the normal and abnormal may be output. The specific process when normal or abnormal is determined is the same as that of the first embodiment.

In addition, the normality and abnormality (hereinafter referred to as normality) associated with the output of each supply and demand environment in this way are referred to as the above-mentioned reference supply amount data, reference exhaust amount data, and further reference. It may be made to learn in relation to the light amount data. That is, as shown in FIG. 12, the degree of association is formed by learning from each other in the relationship between the reference supply amount data and the normality of the cultivation environment linked via the supply and demand environment. The same applies to other reference displacement data and reference light intensity data.

When it is desired to actually determine the cultivation environment, it is possible to determine the cultivation environment according to the above-mentioned supply amount data by using this degree of association. When other exhaust amount data and light amount data are input, it is possible to determine the cultivation environment in the same manner.

Further, as shown in FIGS. 4 to 6 in the first embodiment, the reference supply amount data, the reference exhaust amount data, and the reference light amount data are the reference environment data, the reference agricultural product state data, and the reference agricultural product cultivation. It may be associated with the cultivation environment through the degree of association with each combination with the phase data. That is, the reference equipment data in FIGS. 4 to 6 is replaced with any of the reference supply amount data, the reference exhaust amount data, and the reference light amount data. Even in such a case, it is possible to determine the cultivation environment as in the first embodiment.

Third Embodiment Hereinafter, the third embodiment will be described. The system used in the third embodiment uses the cultivation environment determination system 1 described with reference to FIGS. 1 and 2 in the above-described first embodiment. Further, in the third embodiment, the search solution according to each degree of association described in the first embodiment and the second embodiment is used.

FIG. 13 shows an example in which the second degree of association of three or more stages between the reference cultivation environment and the growth status is preset. This second degree of association 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, the cultivation environment for reference is input as input data, each growth status is output as output data, and at least one hidden layer is provided between the input node and the output node so that machine learning is performed. May be good. On the contrary, the growth status may be input and the reference cultivation environment may be output.

The reference cultivation environment corresponds to the cultivation environment as output data in the first embodiment. The first embodiment gives an example of the case where the normality of the cultivation environment is output, but the present invention is not limited to this, and the cultivation environment itself may be used. The cultivation environment itself indicates the specific content of the abnormality if the cultivation environment in the first embodiment is normal or if an abnormality has occurred, for example, fertilizer having a low temperature. There is little light, there is little light, etc.

The growth status as an exploratory solution indicates how much the finished crop is growing, such as the yield of the crop, the quality and taste of the crop, and the yield of the crop. However, this growth situation is not limited to these, and may indicate how much the growing crop before harvest is growing. For example, how large the bud is, in fact. May indicate how ripe it is.

By learning the data set of the reference cultivation environment thus obtained and the data of the growth status with respect to the data set, the degree of association shown in FIG. 13 is constructed.

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 on the reference cultivation environment and the growth status in that case in order to discriminate the actual search solution, and analyzes and analyzes these. The degree of association shown in FIG. 13 is created in.

For example, if the temperature continues to be high as a reference cultivation environment and the yield has decreased as a growing condition, the degree of association with the decreased yield becomes stronger. On the other hand, if the temperature remains the best as a reference cultivation environment and the yield increases as a growing condition, the degree of association with the increased yield becomes stronger. This analysis and analysis may be performed by artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually trying to determine a new growth situation from now on, first, a search solution of the cultivation environment is obtained through the first embodiment and the second embodiment. Next, the reference cultivation environment corresponding to the cultivation environment as the search solution is input, and the growth status which is the output is searched.

In this way, it becomes possible for the farmers to understand what the actual growth situation of the crops is from the search solution of the cultivation environment through the first embodiment and the second embodiment, and the farmers can also understand. From the growth situation, it is possible to think of guidelines for future agricultural improvement measures.

FIG. 14 shows an example in which the second degree of association of three or more stages between the reference cultivation environment and the cultivation method is preset.

The cultivation method is information on the optimum cultivation method for the crop currently being cultivated. For example, advice on specific cultivation methods such as "spraying fertilizer 〇〇 twice a week, spraying pesticide △△ once a month" and "spraying water twice a day, weeding once a week". It may contain contents close to. By investigating the cultivation method of the actual crop in the previous data and finding out which cultivation method is the best for improving the yield or the quality of the crop, the data on the cultivation method can be obtained. get. In such a case, this second degree of association is determined after investigating the data on the cultivation environment and what kind of cultivation method was applied to it in the past, and as a result, what was the yield and quality of the crops. Form a degree.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually trying to determine a new cultivation method from now on, first, a search solution of the cultivation environment is obtained through the first embodiment and the second embodiment. Next, the reference cultivation environment corresponding to the cultivation environment as the search solution is input, and the output cultivation method is searched.

In this way, it becomes possible for the farmers to understand what the actual cultivation method of the crops is from the search solution of the cultivation environment through the first embodiment and the second embodiment, and the farmers can also understand. From the cultivation method, it is possible to think of guidelines for future agricultural improvement measures.

FIG. 15 shows an example in which a second degree of association of three or more stages between the reference cultivation environment and the control instruction information for cultivating the crop is preset.

The control instruction information is all information for controlling the equipment (device) necessary for creating the cultivation environment. The cultivation environment is determined by temperature, humidity, fertilizer supply, pesticide supply, light, water supply, gas flow rate and airflow flow rate and direction, lighting illuminance, etc., but these supplies are controlled. The instruction information for this is the control instruction information. For example, for temperature and humidity, air conditioning equipment, fertilizer, water, and pesticides are supplied through pipelines and plant equipment for supplying fertilizer. In addition, gas and airflow are also carried out through pipelines and plant equipment. In addition, the illuminance of lighting is performed through lighting equipment. Specifically, this control instruction information defines a processing operation such as instructing a sprinkler to inject water for one minute or stopping heating in a greenhouse. Alternatively, it may be control instruction information to the control system that automatically opens and closes the blackout curtain in the vinyl house.

By investigating the actual control instruction information in the previous data and finding out which control instruction is the best for improving the yield or the quality of the crop, the data on the control instruction information can be obtained. get. In such a case, this second degree of association is based on the data on the cultivation environment and what control instructions have been given to it in the past, and as a result, the yield and quality of the crops have been investigated. Form a degree.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually trying to discriminate new control instruction information from now on, first, a search solution of the cultivation environment is obtained through the first embodiment and the second embodiment. Next, the reference cultivation environment corresponding to the cultivation environment as the search solution is input, and the control instruction information which is the output is searched.

In this way, it becomes possible for the farmer to understand what the actual control instruction information is from the search solution of the cultivation environment through the first embodiment and the second embodiment, and the farmer can also understand it. From the cultivation method, it is possible to consider guidelines for future agricultural improvement measures. Further, in the present invention, control may be performed in actually cultivating an agricultural product based on the extracted control instruction information. That is, if a sprinkler that injects water is instructed to perform for 1 minute, or if control instruction information such as stopping heating in the greenhouse is extracted, various equipment, plants, equipment, etc. corresponding to this are extracted. It will be controlled.

In this third embodiment, as shown in FIG. 16, the degree of association may be formed in relation to the supply and demand environment, which is the search solution obtained through the second embodiment. A second degree of association of three or more levels is created in advance between the reference supply and demand environment corresponding to this supply and demand environment and the growth status of agricultural products. The method of making this is the same as described above. In other words, in determining the actual search solution, past data on the supply and demand environment for reference and the degree of growth in that case are accumulated, and these are analyzed and analyzed to show in FIG. Create a degree of association.

For example, if the supply and demand environment for reference continues to be low in water, and if the yield has decreased due to the growth situation, the degree of association with the decreased yield becomes stronger. On the other hand, if there are too many pesticides as a reference cultivation environment and the yield increases as a growth situation, the degree of association with the increased yield becomes stronger. This analysis and analysis may be performed by artificial intelligence.

After forming the second degree of association shown in FIG. 16 in advance, the growth status of the crop is estimated via the reference supply and demand environment corresponding to the supply and demand environment determined in the second embodiment.

This supply and demand environment is the same when the supply and demand environment of not only fertilizers, pesticides, and water but also gases such as oxygen, hot air, and cold air is determined. By creating a second degree of association in advance in the relationship, the growth status of crops can be estimated in the same way.

In addition, this supply and demand environment is the same when determining the supply and demand environment of light, and the same applies by creating a second degree of linkage in advance in relation to the growth status of agricultural products in the supply and demand environment for reference according to this. It is possible to estimate the growth status of crops.

In addition to the growth status, as a search solution, when searching for crop cultivation methods and control instruction information, the second degree of association with the above-mentioned reference supply and demand environment is formed in the same manner. It is possible to search for a 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 the cultivation environment without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed 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.

Further, according to the present invention, there is a feature that an 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 determining the most probable normality of the cultivation environment based on the degree of association represented by the numerical values of three or more levels, the degree of association can be determined under the situation where there are multiple possible candidates for the search solution. It is also possible to search and display in descending order. 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 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 a high probability. 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 equipment data is acquired, and in addition to this, environmental data, crop condition data, crop growth phase data, and knowledge, information, and data regarding the normality of the cultivation environment for these are acquired, the degree of association is increased accordingly. Or lower it.

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 not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the 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.
Fourth Embodiment In the fourth embodiment, the above-mentioned reference equipment data is used to search for the storage environment and meat quality of meat and fish traded in the market, and the quality of fish (hereinafter, these are referred to as fish meat). Is.
For example, as shown in FIG. 17, it is premised that the degree of association between the reference equipment data and the quality of fish meat is set in advance at three or more levels. The reference equipment data is obtained from the equipment data obtained by taking an image of the appearance of fish meat, and can be obtained by analyzing the equipment data. This image may be a moving image as well as a still image. This reference equipment data may be used to identify the quality of fish meat by analyzing an image taken of the fish meat.
The reference equipment data referred to here is data on the storage environment in all logistics, transportation markets, retail stores, wholesalers, and supermarkets from the time fish meat is produced or landed to the time it is delivered to consumers. The reference equipment data is data directly acquired from the equipment (device) necessary for creating the storage environment. In the case of a wholesale market, for example, the storage environment is determined by the temperature, humidity, light, water supply amount, flow rate of supplied gas, flow rate and direction of air flow, illuminance of lighting, and the like. The storage environment of various facilities such as automated refrigerators in recent years is also determined by such parameters. For example, for temperature and humidity, air conditioning equipment and water are supplied through pipelines and plant equipment. In addition, gas and airflow are also carried out through pipelines and plant equipment. In addition, the illuminance of lighting is performed through lighting equipment.

Reference equipment data includes any data obtained by directly or indirectly attaching sensors or measuring instruments to such equipment. For example, the electric power, electricity, voltage, vibration, sound, light, radio waves (hereinafter, these are collectively referred to as physical data), the flow rate of air and liquid, the amount of drainage in the drainage facility, etc. supplied to the above-mentioned various facilities. Reference equipment data. It detects operation data consisting of one or more of physical data for operating equipment, supply amount data for supplying water, gas flow rate data for supplying or exhausting gas, and light amount of irradiating light. By detecting these operation data, it is possible to grasp whether the current state of the storage environment is normal or whether some abnormality has occurred. In addition, by detecting these operation data, it is possible to estimate whether the storage environment may have an abnormality in the near future or whether it remains normal. Fish meat imaged as reference equipment data are fish meat immediately after landing at the fish meat market, fish meat traded at the fish meat market, fish meat being transported to stores and supermarkets, and displayed at stores and supermarkets. All of the fish meats that have been made are included. The fish meat referred to here includes the meat of all fish such as squid, octopus, sea urchin, salmon roe, and shellfish, or the meat of all animals such as cow, horse, sheep, pig, bird, and wild boar.

The quality of fish meat referred to here may be expressed by freshness, or may be evaluated by luster or taste, particularly in the case of fish. The quality of the fish meat may be expressed by a ranking set by the system side or the user side and evaluated on a 5-point or 10-point scale. Alternatively, it may simply be a very tasty, tasty, ok, ordinary expression.

The quality of these fish meats may be determined based on previously trained features. At this time, artificial intelligence is used to learn the image data of fish meat and the quality of fish meat, and when actually acquiring the reference equipment data, compare it with these learned image data. The quality of the fish meat may be determined.

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

Further, especially in the case of fish, the freshness may be evaluated as the quality of fish meat itself. Similarly, the freshness of fish meat may be evaluated by a plurality of inspectors for the taste of each food item in a plurality of stages, and statistically analyzed to obtain a quality evaluation value.

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

The reference equipment data are associated with each other through three or more levels of association with the quality A to D of the fish meat as the output solution. The reference equipment data is arranged on the left side through this degree of association, and the quality of each fish meat is arranged on the right side through this degree of association. The degree of association indicates the degree of which fish meat is highly related to the quality of the reference equipment data arranged on the left side. In other words, this degree of association is an indicator of what kind of fish meat quality each reference equipment data is likely to be associated with, and the most probable fish meat quality is selected from the reference equipment data. It shows the accuracy in doing so. In the example of FIG. 17, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the quality of fish meat 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 as to which of the reference equipment data and the quality of fish meat in that case is adopted and evaluated in discriminating the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 17 is created.

For example, it is assumed that the quality A of fish meat is often evaluated as the quality of fish meat with respect to the reference equipment data imaged for fish meat in the past. By collecting and analyzing such data sets, the degree of association with the reference equipment data is strengthened.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference equipment data P01, analysis is performed from various data as a result of past evaluation of the quality of fish meat. In the case of reference equipment data P01, if there are many cases of fish meat quality A, the degree of association that leads to the evaluation of the fish meat quality is set higher, and there are many cases of fish meat quality B. To set a higher degree of association that leads to the evaluation of the quality of this fish meat. For example, in the example of the reference equipment data P01, the quality A of the fish meat and the quality C of the fish meat are linked. The degree of association of w14, which leads to the quality C of meat, is set to 2 points.

Further, the degree of association shown in FIG. 17 may be composed of the nodes of the neural network in artificial intelligence.

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

Based on the equipment data newly acquired in this way, the quality of fish meat is determined. In such a case, the degree of association shown in FIG. 17 (Table 1) acquired in advance is referred to. For example, when the newly acquired equipment data is the same as or similar to P02, the fish meat quality B is associated with w15 and the fish meat quality C is associated with the association degree w16 through the degree of association. There is. In such a case, the quality B of the fish meat 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 fish meat, 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 as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search for the most suitable quality of fish meat from the newly acquired equipment data and display it to the user. By seeing this search result, the user, that is, the fish meat trader, the fish meat distributor, and the distributor can sort the fish meat based on the quality of the searched fish meat, and the fish meat can be selected. The taste can be predicted and the price of fish meat can be determined.

FIG. 18 shows reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from an air conditioning equipment that creates a storage environment for storing fish meat, and atmosphere information consisting of temperature or humidity. This is an example of acquiring the first degree of association of a stage or higher in advance. Atmosphere information is information about the atmosphere actually produced from the air conditioning equipment, and temperature or humidity is a typical example. Such an atmosphere also has a great influence on the preservation environment, which in turn affects the quality of fish meat.

By the way, the cultivation environment shown in FIG. 3 is learning data for searching the environment including temperature and humidity from the equipment data. Similarly, in FIG. 18, the atmosphere information consisting of temperature or humidity is searched from the equipment data. Is what you do. The description of this search method will be omitted below by diverting the description of FIG. The actual training data is created by collecting the power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment, and the data set of the actually measured temperature and / or humidity data.

After searching the atmosphere information from the equipment data in this way, the quality of fish meat is searched through the searched atmosphere information as shown in FIG.

In such a case, it is premised that the reference atmosphere information and the quality of fish meat are formed as a pre-learned model with a degree of association. The reference atmosphere information corresponds to the above-mentioned atmosphere information, is information about the atmosphere actually produced from the air conditioning equipment, and temperature or humidity is a typical example thereof. The search for fish meat is carried out using such a degree of association, and the specific method thereof will be omitted below by quoting the explanation of FIG.

In such a case, the quality of the fish meat may be searched for via the reference atmosphere information corresponding to the atmosphere information searched by FIG.

That is, in this embodiment, reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from an air conditioning equipment that creates a storage environment for storing fish meat, and an atmosphere consisting of temperature or humidity. The first degree of association of three or more levels with the information is acquired in advance, and the second degree of association of three or more levels of the reference atmosphere information consisting of temperature or humidity and the quality of fish meat is acquired in advance. Then, when newly determining the quality of fish meat, first, the equipment data corresponding to the above-mentioned reference equipment data among any one or more of electric power, electricity, voltage, vibration, and sound is obtained from the air conditioning equipment that creates the storage environment. get. Next, using the acquired first degree of association, the atmosphere information consisting of temperature or humidity is determined based on the newly acquired equipment data. Next, using the acquired second degree of association, the quality of fish meat is estimated via the reference atmosphere information corresponding to the determined atmosphere information.

In the present embodiment, it is not essential to use the reference atmosphere information corresponding to the temperature or humidity defined in the atmosphere information searched by FIG. 18, and the atmosphere information may be directly acquired and input. good. Atmosphere information may be acquired by measuring the temperature or humidity of the storage environment of the fish meat whose quality is to be actually measured, and the solution search may be performed by inputting this as input data.

Further, FIG. 20 shows an example in which the storage environment is set on the output side as an alternative to the cultivation environment on the output side of FIG. Similarly, in such a case, the reference equipment data consisting of any one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates the storage environment for storing fish meat, and the normality of the storage environment 3 Obtain the degree of association above the stage. Then, based on the newly acquired equipment data, the storage environment actually created by the equipment is determined. As for the specific method, the description below will be omitted by quoting the description of FIG.

Incidentally, whether the storage environment is normal or abnormal may be determined by whether or not it is within the optimum temperature and humidity conditions verified in advance for each type of fish meat. For example, in the case of chicken, the optimum storage temperature is assumed to be 10 to 12 ° C. If the storage environment created by the equipment data satisfies this optimum storage temperature of 10 to 12 ° C., the weighting to "normal" is increased as the output solution. For certain equipment data, when the optimum storage temperature of 10 to 12 ° C. is often exceeded, the weighting of "abnormality (high temperature)" is increased as the storage environment.

FIG. 21 is an example in which reference atmosphere information is set on the input side as a substitute for the reference equipment data on the input side of FIG. 20. In such a case as well, the degree of association between the reference atmosphere information consisting of the temperature or humidity that creates the storage environment for storing fish meat and the normality of the storage environment is acquired. Then, based on the newly acquired atmosphere information, the storage environment actually created by the equipment is determined. As for the specific method, the description below will be omitted by quoting the description of FIG.

Also in the form shown in FIG. 21, it is not essential to use the reference atmosphere information corresponding to the temperature or humidity defined in the atmosphere information searched by FIG. 18, and the atmosphere information is directly acquired and input. You may. Atmosphere information may be acquired by measuring the temperature or humidity of the storage environment of the fish meat whose quality is to be actually measured, and the solution search may be performed by inputting this as input data.

At this time, the air conditioning equipment that creates the storage environment may be controlled based on the normality of the searched storage environment. For example, if the searched storage environment is hot and the humidity is optimal, the air conditioning equipment is controlled to lower only the temperature. If the searched storage environment is high in both temperature and humidity, the air conditioning equipment may be controlled so as to reduce both of them. In addition, when the searched storage environment is abnormal, the specific control may be determined from the degree of deviation between the temperature and humidity compared to the normal state, and the set temperature, wind power, wind direction, etc. of the air conditioning equipment may be used as this. It may be controlled accordingly.

Further, FIG. 22 shows an example in which three or more levels of association between the reference equipment data acquired from the equipment for creating the storage environment for storing fish meat and the improvement control content of the storage environment are formed in advance. The improvement control content of the storage environment referred to here is, for example, the specific control content of the air conditioning equipment if it is the air conditioning equipment that creates the storage environment, and the set temperature, wind power, wind direction, etc. correspond to this. In addition, if it is the air circulation equipment in the wholesale market that creates the storage environment, it is the specific control content of the equipment, and the wind force, wind direction, etc. correspond to this.

When proposing a new control for improving the storage environment, the equipment data is acquired from the equipment that creates the storage environment in the same manner as described above. Next, using the degree of association shown in FIG. 22, the improvement control contents of the storage environment are proposed based on the newly acquired equipment data.

FIG. 23 is an example in which reference atmosphere information is set on the input side as a substitute for the reference equipment data on the input side of FIG. 22. In such a case as well, the degree of association between the reference atmosphere information consisting of the temperature or humidity that creates the storage environment for storing fish meat and the content of improvement control of the storage environment is acquired. Then, the improvement control content is determined based on the newly acquired atmosphere information. As for the specific method, the description below will be omitted by quoting the description of FIG.

Also in the form shown in FIG. 23, it is not essential to use the reference atmosphere information corresponding to the temperature or humidity defined in the atmosphere information searched by FIG. 18, and the atmosphere information is directly acquired and input. You may. Atmosphere information may be acquired by measuring the temperature or humidity of the storage environment of the fish meat whose quality is to be actually measured, and the solution search may be performed by inputting this as input data.

When searching for the quality of fish meat as shown in FIGS. 17 to 19, as shown in FIG. 24, the quality of fish meat with respect to the degree of association in which other reference information is combined in addition to the reference equipment data. May be used. Examples of reference information here include image information for reference, analysis information for reference, production area information for reference, type information for reference, elasticity information for reference, fat ratio information for reference, and odor for reference in the case of fish. Information, storage information for reference, growth environment information for reference, etc.

The reference image information is obtained from the image information obtained by capturing an image of the appearance of the fish, and can be obtained by analyzing the image information. This image may be a moving image as well as a still image. The reference image information may be used to identify the quality of the fish by analyzing the image captured of the fish. The fish imaged as reference image information are all fish immediately after landing at the fish market, fish traded at the fish market, fish being transported to stores and supermarkets, and fish displayed at stores and supermarkets. included. The fish referred to here also includes all kinds of fish and shellfish such as squid, octopus, sea urchin, salmon roe, and shellfish.

Reference analysis information is any information regarding the results of chemical and physical analysis performed on fish. This reference analysis information may be any one or more of histamine, ammonia, trimethylamine, indol, hydrogen sulfide, volatile organic acid, and volatile reducing substance. Since all of these affect the umami, quality, and freshness of fish, the discrimination accuracy can be improved by combining with reference image information and discriminating the quality of fish through the degree of association. Further, the reference analysis information may be IMP (inosinic acid), which is a umami component of fish, and K value, which is the ratio of HxR and Hx to ATP-related compounds.

Reference production area information is information on the production area of the fish, for example, national level such as the United States and Japan, regional level such as Tohoku region and Kyushu region, prefectural level such as Hokkaido and Kagoshima prefecture, and even groups and towns, and even fishing grounds. It may be indicated by a level. Since the production area of the fish included in the reference production area information also affects the taste of the fish, the discrimination accuracy can be improved by discriminating the quality of the fish through the degree of association in combination with the reference image information.

The reference type information defines the type of fish, such as tuna, yellowtail, salmon, horse mackerel, scallop, abalone, and saury. Information on the type of fish included in the reference type information also affects the taste of the fish in combination with the image information. Therefore, by combining it with the reference image information and determining the quality of the fish through the degree of association, the discrimination accuracy should be improved. Can be done.

The reference elasticity information is based on the measured value obtained by measuring the elasticity when the fish is pressed. The elasticity of the fish may be measured via an elasticity sensor for measuring the elasticity of the object. Since the elasticity of such a fish is also a factor that affects the freshness of the fish, it is possible to improve the discrimination accuracy by combining this with the reference image information and discriminating the quality of the fish through the degree of association.

An existing fat ratio measuring device may be used for the reference fat ratio information and the fat ratio information. In such a case, a weak electric current may be passed from the electrode to the fish, and the ratio of water and fat contained in the body may be measured to obtain the fat ratio.

The reference odor information is based on the odor of the fish. The odor of the fish may be measured through an odor emitted from the fish, an odor meter for measuring the odor, or an odor sensor. Since such a fish odor is also a factor that affects the freshness and quality of the fish, the discrimination accuracy can be improved by combining this with the reference image information and discriminating the quality of the fish through the degree of association.

The reference growth environment information is based on the growth environment of the fish. The growth environment of a fish contains all the information about the environment in which the fish grows, for example, it may indicate whether it is natural or farmed, or if it is farmed, it may indicate its growth environment. good. In particular, in the case of aquaculture, all information indicating the environment of the aquaculture (for example, the content and frequency of food, the environment of the aquaculture, water quality, the treatment status of manure, etc.) may be included. Since such a fish growth environment is also a factor that affects the freshness and quality of fish, it is possible to improve the discrimination accuracy by combining this with reference image information and discriminating the quality of fish through the degree of association.

In the case of meat, reference information includes reference image information, reference ultrasonic image information, reference analysis information, reference production area information, reference biological information, reference breeding environment information, and reference food information. Is done.

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 reference ultrasonic image information is ultrasonic image data of a meat portion imaged in advance in a living body of a livestock that provides meat.

Reference analysis information is any 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 disassembly 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.

The reference production area information is information on the production area of the meat, for example, the national level such as the United States and Japan, the regional level such as the Tohoku region and the Kyushu region, the prefectural level such as Hokkaido and Kagoshima prefecture, and the group and town of Hokkaido. Furthermore, it may be indicated 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.

Reference biometric information includes all biometric data measured with respect to the living body of the livestock that provides the meat. The types of biometric data of livestock include all biometric data such as heart rate, body temperature, electrocardiogram, blood pressure, blood test results, 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.

The reference breeding environment information includes all data regarding the environment in which the livestock that provide the meat are raised. The types of data for this reference breeding environment information include temperature, humidity, wind direction, diurnal illumination, indoor lighting, voice data, pest control 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.

Reference feed information is information about the feed of livestock that provide meat.

A storage environment in which any one or more of such reference information, a combination of reference equipment data, and the quality of fish meat are learned, and the fish meat for which quality is newly searched is stored. In addition to acquiring the equipment data of the above, new information corresponding to the learned reference information is acquired. If the learned reference information is reference analysis information, the information analyzed for fish and meat is newly acquired and input accordingly. This makes it possible to search for the quality of fish meat as well. In either case, data is input according to the reference information of the degree of association, and the quality of the fish is obtained using the degree of association.

The above-mentioned storage environment is not limited to the environment for storing meat. For example, it can be applied to each indoor or indoor facility of a living space, an office, an amusement facility, a building structure such as a school or a hospital, or an indoor environment such as an event venue. As an alternative to the storage environment as an output solution, it is possible to make the same judgment by substituting the living environment and work environment as a living space or office space for learning, and the improvement control of the environment can be performed in the same way. It becomes possible to do.

1 Cultivation environment discrimination system 2 Estimator 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

Claims (23)

In the cultivation environment determination program that determines the normality of the cultivation environment in which crops are cultivated
The first of three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates the cultivation environment for cultivating crops, and the cultivation environment consisting of temperature or humidity. In addition to acquiring the degree of association in advance, the degree of association acquisition step of acquiring in advance the second degree of association of three or more stages of the reference cultivation environment consisting of temperature or humidity and the growth status of the crop,
Information to acquire equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the cultivation environment when newly determining the cultivation environment of agricultural products. Acquisition step and
A discrimination step for discriminating the cultivation environment consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the above-mentioned association degree acquisition step, and a discrimination step.
A cultivation environment characterized in that a computer executes an estimation step of estimating the growth status of a crop based on the cultivation environment determined by the determination step by using the second association degree acquired in the association degree acquisition step. Discrimination program. In the cultivation environment determination program that determines the normality of the cultivation environment in which crops are cultivated
The first of three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates the cultivation environment for cultivating crops, and the cultivation environment consisting of temperature or humidity. The degree of association acquisition step of acquiring the degree of association in advance and the second degree of association of three or more stages of the reference cultivation environment consisting of temperature or humidity and the cultivation method of the crop, and the step of acquiring the degree of association in advance.
Information to acquire equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the cultivation environment when newly determining the cultivation environment of agricultural products. Acquisition step and
A discrimination step for discriminating the cultivation environment consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the above-mentioned association degree acquisition step, and a discrimination step.
A cultivation environment characterized in that a computer is made to execute a proposal step of proposing a cultivation method of an agricultural product based on the cultivation environment determined by the determination step by using the second association degree acquired in the association degree acquisition step. Discrimination program. In the cultivation environment determination program that determines the normality of the cultivation environment in which crops are cultivated
The first of three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates the cultivation environment for cultivating crops, and the cultivation environment consisting of temperature or humidity. In addition to acquiring the degree of association in advance, the degree of association acquisition step of acquiring in advance the second degree of association of three or more stages of the reference cultivation environment consisting of temperature or humidity and the control instruction information for cultivating the crop.
Information to acquire equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the cultivation environment when newly determining the cultivation environment of agricultural products. Acquisition step and
A discrimination step for discriminating the cultivation environment consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the above-mentioned association degree acquisition step, and a discrimination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, control instruction information is extracted based on the cultivation environment determined by the above-mentioned determination step, and crops are cultivated based on the extracted control instruction information. A cultivation environment determination program characterized by having a computer execute control steps and controls in. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying one or more of fertilizer, pesticide, and water to crops
Three stages of reference supply amount data consisting of one or more of fertilizer supply amount, pesticide supply amount, and water supply amount obtained from plant equipment that creates a cultivation environment for cultivating agricultural products, and these supply and demand environments. In addition to acquiring the above first degree of association in advance, the association degree acquisition step of acquiring in advance the second degree of association of three or more stages of the supply and demand environment for reference and the growth status of agricultural products,
When newly determining the supply and demand environment, the supply amount data corresponding to the reference supply amount data of any one or more of the fertilizer supply amount, the pesticide supply amount, and the water supply amount is acquired from the above plant equipment. Information acquisition steps and
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by having a computer execute an estimation step of estimating the growth status of crops based on the supply and demand environment determined by the determination step using the second association degree acquired in the above association degree acquisition step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying one or more of fertilizer, pesticide, and water to crops
Three stages of reference supply amount data consisting of one or more of fertilizer supply amount, pesticide supply amount, and water supply amount obtained from plant equipment that creates a cultivation environment for cultivating agricultural products, and these supply and demand environments. In addition to acquiring the above first degree of association in advance, the association degree acquisition step of acquiring in advance the second degree of association of three or more stages of the reference supply and demand environment and the cultivation method of agricultural products,
When newly determining the supply and demand environment, the supply amount data corresponding to the reference supply amount data of any one or more of the fertilizer supply amount, the pesticide supply amount, and the water supply amount is acquired from the above plant equipment. Information acquisition steps and
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by using the second degree of association acquired in the above-mentioned degree of association acquisition step and causing a computer to execute a proposal step of proposing a cultivation method of agricultural products based on the supply and demand environment determined by the above-mentioned determination step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying one or more of fertilizer, pesticide, and water to crops
Three stages of reference supply amount data consisting of one or more of fertilizer supply amount, pesticide supply amount, and water supply amount obtained from plant equipment that creates a cultivation environment for cultivating agricultural products, and these supply and demand environments. In addition to acquiring the above first degree of association in advance, the association degree acquisition step of acquiring in advance the second degree of association of three or more stages of the reference supply and demand environment and the control instruction information for cultivating crops,
When newly determining the supply and demand environment, the supply amount data corresponding to the reference supply amount data of any one or more of the fertilizer supply amount, the pesticide supply amount, and the water supply amount is acquired from the above plant equipment. Information acquisition steps and
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, control instruction information is extracted based on the cultivation environment determined by the above-mentioned determination step, and crops are cultivated based on the extracted control instruction information. A cultivation supply-demand environment determination program characterized by having a computer execute control steps and controls in. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying gas to crops
The reference supply amount data consisting of the gas supply amount acquired from the pipeline that creates the cultivation environment for cultivating agricultural products and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the degree of association acquisition step to acquire the second degree of association of three or more stages with the growth status of the crop in advance,
Information acquisition step to acquire gas supply amount data when newly determining the supply and demand environment,
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by having a computer execute an estimation step of estimating the growth status of crops based on the supply and demand environment determined by the determination step using the second association degree acquired in the above association degree acquisition step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying gas to crops
The reference supply amount data consisting of the gas supply amount acquired from the pipeline that creates the cultivation environment for cultivating agricultural products and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the degree of association acquisition step to acquire the second degree of association of three or more stages with the cultivation method of agricultural products in advance, and
Information acquisition step to acquire gas supply amount data when newly determining the supply and demand environment,
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by using the second degree of association acquired in the above-mentioned degree of association acquisition step and causing a computer to execute a proposal step of proposing a cultivation method of agricultural products based on the supply and demand environment determined by the above-mentioned determination step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for supplying gas to crops
The reference supply amount data consisting of the gas supply amount acquired from the pipeline that creates the cultivation environment for cultivating agricultural products and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the step of acquiring the degree of association in advance to acquire the second degree of association of three or more stages with the control instruction information for cultivating the crop.
Information acquisition step to acquire gas supply amount data when newly determining the supply and demand environment,
A determination step for determining the supply and demand environment based on the supply amount data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, control instruction information is extracted based on the supply and demand environment determined by the above-mentioned determination step, and agricultural products are cultivated based on the extracted control instruction information. Cultivation supply and demand environment determination program characterized by having a computer execute control steps and control steps. In the cultivation supply and demand environment determination program that determines the supply and demand environment for irradiating crops with light
The reference light quantity data consisting of the amount of light to be irradiated, which is acquired from the lighting equipment that creates the cultivation environment for cultivating agricultural products, and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the step of acquiring the degree of association to acquire the second degree of association of three or more stages with the growth status of the crop in advance, and
When newly determining the supply and demand environment, the information acquisition step of acquiring the light amount data consisting of the light amount of the emitted light from the above lighting equipment, and the information acquisition step.
A determination step for determining the supply and demand environment based on the light intensity data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by having a computer execute an estimation step of estimating the growth status of crops based on the supply and demand environment determined by the determination step using the second association degree acquired in the above association degree acquisition step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for irradiating crops with light
The reference light quantity data consisting of the amount of light to be irradiated, which is acquired from the lighting equipment that creates the cultivation environment for cultivating agricultural products, and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the degree of association acquisition step to acquire the second degree of association of three or more stages with the cultivation method of agricultural products in advance, and
When newly determining the supply and demand environment, the information acquisition step of acquiring the light amount data consisting of the light amount of the emitted light from the above lighting equipment, and the information acquisition step.
A determination step for determining the supply and demand environment based on the light intensity data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Cultivation supply and demand, which is characterized by using the second degree of association acquired in the above-mentioned degree of association acquisition step and causing a computer to execute a proposal step of proposing a cultivation method of agricultural products based on the supply and demand environment determined by the above-mentioned determination step. Environment determination program. In the cultivation supply and demand environment determination program that determines the supply and demand environment for irradiating crops with light
The reference light quantity data consisting of the amount of light to be irradiated, which is acquired from the lighting equipment that creates the cultivation environment for cultivating agricultural products, and the first degree of association with the supply and demand environment in three or more stages are acquired in advance, and the reference supply and demand environment. And the step of acquiring the degree of association in advance to acquire the second degree of association of three or more stages with the control instruction information for cultivating the crop.
When newly determining the supply and demand environment, the information acquisition step of acquiring the light amount data consisting of the light amount of the emitted light from the above lighting equipment, and the information acquisition step.
A determination step for determining the supply and demand environment based on the light intensity data acquired through the information acquisition step using the association degree acquired in the association degree acquisition step, and a determination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, control instruction information is extracted based on the supply and demand environment determined by the above-mentioned determination step, and agricultural products are cultivated based on the extracted control instruction information. Cultivation supply and demand environment determination program characterized by having a computer execute control steps and control steps. In the fish meat quality determination program that determines the quality of fish meat stored in the storage environment
The reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates the storage environment for storing fish meat and the quality of fish meat are linked to three or more levels. The association degree acquisition step to be acquired in advance and
Equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates a storage environment for storing fish meat when newly determining the quality of fish meat. Information acquisition steps to acquire and
A storage environment characterized in that the computer is made to execute a discrimination step for discriminating the quality of fish meat based on the equipment data acquired through the information acquisition step by using the linkage degree acquired in the association degree acquisition step. Discrimination program. In the fish meat quality determination program that determines the quality of fish meat stored in the storage environment
Three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates a storage environment for storing fish meat, and atmosphere information consisting of temperature or humidity. In addition to acquiring the first degree of association in advance, the association degree acquisition step of acquiring in advance the second degree of association of three or more stages of the reference atmosphere information consisting of temperature or humidity and the quality of fish meat,
Information to acquire equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the storage environment when newly determining the quality of fish meat. Acquisition step and
A discrimination step for discriminating atmosphere information consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the association degree acquisition step, and a discrimination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, a computer is made to execute an estimation step of estimating the quality of fish meat based on the atmosphere information determined by the above-mentioned determination step. Quality determination program. In the fish meat quality determination program that determines the quality of fish meat stored in the storage environment
The reference atmosphere information consisting of the temperature or humidity at which the fish meat is stored, and the degree of association acquisition step of acquiring the second degree of association of three or more stages of the quality of the fish meat in advance, and the step of acquiring the degree of association.
When newly determining the quality of fish meat, the information acquisition step of acquiring atmosphere information consisting of temperature or humidity that creates a storage environment for storing fish meat, and
Using the degree of association acquired in the above-mentioned degree of association acquisition step, a computer is made to execute a determination step of determining the quality of fish meat based on the atmosphere information acquired through the above-mentioned information acquisition step. Quality determination program. The fish quality determination program according to any one of claims 13 to 15, wherein the degree of association is composed of nodes of a neural network in artificial intelligence. In the storage environment determination program that determines the normality of the storage environment for storing fish meat
The reference equipment data acquired from the equipment that creates the storage environment for storing fish meat, and the association degree acquisition step that acquires in advance the degree of association between the normality of the storage environment and three or more levels.
When newly determining the storage environment for fish meat, the information acquisition step to acquire equipment data from the equipment that creates the storage environment, and
The storage is characterized in that the computer is made to execute a determination step for determining the normality of the storage environment based on the equipment data acquired through the information acquisition step by using the association degree acquired in the association degree acquisition step. Environment determination program. In the storage environment determination program that determines the normality of the storage environment for storing fish meat
Three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates a storage environment for storing fish meat, and atmosphere information consisting of temperature or humidity. In addition to acquiring the first degree of association in advance, the association degree acquisition step of acquiring in advance the second degree of association of three or more stages of the reference atmosphere information consisting of temperature or humidity and the normality of the storage environment,
When newly determining the storage environment for fish meat, acquire the equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the storage environment. Information acquisition step and
A discrimination step for discriminating atmosphere information consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the association degree acquisition step, and a discrimination step.
The storage is characterized in that the computer is made to execute an estimation step for estimating the normality of the storage environment based on the atmosphere information determined by the determination step by using the second association degree acquired in the association degree acquisition step. Environment determination program. In the storage environment determination program that determines the normality of the storage environment for storing fish meat
A degree of association acquisition step for acquiring in advance a second degree of association of three or more stages between the reference atmosphere information consisting of the temperature or humidity at which the fish meat is stored and the normality of the storage environment.
When newly determining the quality of fish meat, the information acquisition step of acquiring atmosphere information consisting of temperature or humidity that creates a storage environment for storing fish meat, and
The storage is characterized in that the computer is made to execute a determination step for determining the normality of the storage environment based on the atmosphere information acquired through the information acquisition step by using the association degree acquired in the association degree acquisition step. Environment determination program. The storage according to any one of claims 17 to 19, further comprising a control step for controlling the air conditioning equipment that creates the storage environment based on the normality of the storage environment determined in the determination step. Environment determination program. In the preservation environment improvement control proposal program that proposes improvement control of the preservation environment for preserving fish meat
A degree of association acquisition step for acquiring in advance three or more levels of association between the reference equipment data acquired from the equipment for creating the storage environment for storing fish meat and the improvement control contents of the above storage environment.
When proposing a new control for improving the storage environment, the information acquisition step to acquire equipment data from the equipment that creates the storage environment, and
Using the degree of association acquired in the above-mentioned degree of association acquisition step, the computer is made to execute a proposal step of proposing improvement control contents of the storage environment based on the equipment data acquired through the above-mentioned information acquisition step. Storage environment improvement control proposal program. In the preservation environment improvement control proposal program that proposes improvement control of the preservation environment for preserving fish meat
Three or more stages of reference equipment data consisting of one or more of electric power, electricity, voltage, vibration, and sound acquired from the air conditioning equipment that creates a storage environment for storing fish meat, and atmosphere information consisting of temperature or humidity. The association degree acquisition step of acquiring the first association degree in advance and acquiring the second association degree of three or more stages of the reference atmosphere information consisting of temperature or humidity and the improvement control content of the storage environment in advance.
When proposing a new control for improving the storage environment, acquire the equipment data corresponding to the above reference equipment data from any one or more of electric power, electricity, voltage, vibration, and sound from the air conditioning equipment that creates the storage environment. Information acquisition step and
A discrimination step for discriminating atmosphere information consisting of temperature or humidity based on the equipment data acquired through the information acquisition step using the first correlation degree acquired in the association degree acquisition step, and a discrimination step.
Using the second degree of association acquired in the above-mentioned degree of association acquisition step, the computer is made to execute a proposal step that proposes improvement control of the storage environment based on the atmosphere information determined by the above-mentioned determination step. Environmental improvement control proposal program. In the preservation environment improvement control proposal program that proposes improvement control of the preservation environment for preserving fish meat
A degree of association acquisition step for acquiring in advance a second degree of association of three or more stages between the reference atmosphere information consisting of the temperature or humidity at which the fish meat is stored and the content of improvement control of the storage environment.
When proposing a new control for improving the storage environment, an information acquisition step for acquiring atmosphere information consisting of temperature or humidity that creates a storage environment for storing fish meat, and
The preservation is characterized in that the computer is made to execute the proposal step that proposes the improvement control of the preservation environment based on the atmosphere information acquired through the information acquisition step by using the association degree acquired in the association degree acquisition step. Environmental improvement control proposal program.

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