JP2021057071A - Program and system for proposing crop cultivation method - Google Patents

Abstract

To determine a cultivation environment and to control an actual growth state of a crop, a future cultivation method and actual cultivation environment on the basis of the determined cultivation environment.SOLUTION: A program for proposing a crop cultivation method comprises: an association acquisition step to acquire a first degree of association with three or more stages among reference target commercial product information on quality and/or quantity of a crop to be a target commercial product, combinations of the reference target commercial product information with reference market information on a market condition for the crop at the time of sales thereof, and cultivation methods corresponding to the combinations; information acquisition step to acquire target commercial product information on the quality and/or quantity of the crop to be the target commercial product to be newly sold and market information on the market condition at the time of sales thereof; and a proposal step to propose a cultivation method of the crop on the basis of the first degree of association acquired in the association acquisition step and the target commercial product information and the market information acquired in the information acquisition step.SELECTED DRAWING: Figure 13

Description

The present invention relates to a crop cultivation method proposal program and a system for proposing a crop cultivation method.

With the aging of the agricultural population, the number of veteran farmers who have farming skills is decreasing, and the shortage of young farmers who pass on their skills has become a problem these days. Under these circumstances, the idea that artificial intelligence limits the cultivation method has been proposed instead of passing on skills by veteran farmers.

In order to assist the advice on cultivation methods for agricultural work with artificial intelligence, it is necessary to carry out more optimal cultivation at the present time after forecasting the future yield and quality of the crops being cultivated. However, in the past, there has been no particular proposal for artificial intelligence that gives optimal advice for improving the yield and quality of this crop.

In addition to this, when considering actual agricultural management, sales depend on the amount and unit price of agricultural products to be sold. The unit price of a crop depends on the quality of the crop. The higher the quality of the crop, the higher the unit price, and conversely, the lower the quality, the lower the unit price. In addition to these, sales are also affected by the market conditions of agricultural products. For this reason, it is necessary to search for a cultivation method aiming at the quantity and quality of agricultural products that will produce the highest sales in terms of agricultural management, but the current situation is that artificial intelligence that gives optimal advice from this perspective has not been specifically proposed. Met.

Therefore, the present invention was devised in view of the above-mentioned problems, and the purpose of the present invention is to give advice on cultivation methods aiming at the quantity and quality of agricultural products that have the highest sales in terms of agricultural management. The purpose is to provide a program and system for proposing a method for cultivating agricultural products that can be performed with high accuracy.

The crop cultivation method proposal program according to the present invention is a reference sales target information regarding the quality and / or quantity of the crop to be sold in the crop cultivation method proposal program that proposes the cultivation method of the crop, and the relevant information at the time of sale. A combination with reference market information regarding the market conditions of agricultural products, a degree of association acquisition step for acquiring the first degree of association of three or more stages with the cultivation method of agricultural products for the combination in advance, and the quality of the newly sold agricultural products to be sold. With reference to the information acquisition step of acquiring the sales target information regarding the quantity and / or the market condition information regarding the market condition at the time of the new sale and the first association degree acquired in the above association degree acquisition step, the above information It is characterized in that a computer executes a proposal step of proposing a cultivation method of agricultural products based on the sales target information acquired through the acquisition step and the market condition information.

Even if you do not have any special skills or experience, it is possible to give highly accurate advice on cultivation methods aiming at the quantity and quality of agricultural products that will produce the highest sales in terms of agricultural management.

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. 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 who uses 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 agricultural products 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 converted. The user can obtain a search solution by the discrimination device 2.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discrimination device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, 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. Reference equipment data is arranged on the left side via this degree of association, and each cultivation environment is arranged on the right side via 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, assume 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 any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, 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 degree of association of "normal" is w17 and the degree of association of "abnormal (less light)" 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 association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

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 in which the reference equipment data P01 is combined with 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 agricultural product state data corresponds to the reference agricultural product 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, it will have 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 and 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 agricultural product growing phase data corresponds to the reference agricultural product growing 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 agricultural product state data P19 is associated with the association degree w11. Similarly, in the node 61e, the reference agricultural product data P03 is associated with the association degree w5, the reference environment data P15 is associated with the association degree w8, and the reference agricultural product 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 is 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 with respect to 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 discriminant device 2 accumulates the reference supply amount data and the past data on how much the supply and demand environment was in that case in discriminating 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 a new 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 any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to grasp the details of the state of the supply and demand environment from the newly acquired supply amount data. In addition, it is possible not only to determine the bad supply and demand environment that is 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 can be sent to call attention.

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 quantity. 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-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 data on the supply and demand environment 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. In addition, it is possible not only to determine the bad supply and demand environment that is 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 can be sent to call attention.

The gas supply and demand environment is not limited to the case where the determination is made through the reference supply amount data, and the gas supply / demand environment may be determined through the reference exhaust amount 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. .. When newly determining the supply and demand environment, the gas displacement data is acquired. Next, the supply-demand environment is determined based on the acquired displacement data using the acquired degree of association. Even when gas is exhausted, it is possible to grasp the details 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 "the displacement is too large" or "the displacement is too small", 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 a 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, the temperature is low. There is less fertilizer, less 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 discrimination 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 newly determine the 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 a 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 "spray fertilizer 〇〇 twice a week, spray pesticide △△ once a month", "spray water twice a day, weed once a week", etc. 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 based on 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, and then this association. 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 is 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 think of 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 be performed for 1 minute, or if control instruction information such as stopping heating in the greenhouse is extracted, various equipment, plants, equipment, etc. corresponding to the information 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, the reference supply and demand environment and the growth status in that case are shown in FIG. 16 by accumulating past data and analyzing and analyzing these. Create a degree of association.

For example, if the supply and demand environment for reference continues to be low in water and 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 the second degree of association shown in FIG. 16 is formed in advance, the growth status of the crop is estimated via the reference supply / demand environment corresponding to the supply / demand environment determined in the second embodiment.

This supply and demand environment is the same when the supply and demand environment of not only fertilizer, pesticides and water but also gases such as oxygen, hot air and cold air is determined, and the reference supply and demand environment corresponding to this is referred to as the growth status of agricultural products. 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 is true 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 / demand environment is formed in the same manner. It is possible to search for a solution.

Fourth Embodiment In the present invention, it may be applied as a yield prediction system. The fourth embodiment includes not only cultivation of agricultural products in a greenhouse, a greenhouse, or a plant factory, but also cultivation of agricultural products in an outdoor field. For example, as shown in FIG. 17, it is premised that a combination of reference image information and reference soil information is formed. The reference image information is an image taken by a camera of a crop in the process of growing. The photographed images of the crops may be sequentially photographed in chronological order from the stage of sowing seeds and seedlings to harvesting. In addition, the captured image of the crop may be composed of an image of the shooting range that captures the entire field or paddy field, or includes an image of the leaves, stems, fruits, etc. of the crop in the field or paddy field taken at a close distance. .. Further, the captured image may be captured through an unmanned aerial vehicle such as a drone, or may be captured by a camera installed on the ground. With such a camera, it is possible to detect the growth state of the crop, the damage caused by the pests on the crop, the disease on the crop, and the like. By the way, for this reference image information, the raw image taken by the camera may be acquired as it is as image information, or only the characteristic part of the image is extracted by utilizing the well-known deep learning technology. This may be used as reference image information.

The reference soil information also includes all information about the soil in which the crop is planted. Examples of this reference soil information include soil components, pH, water content, temperature and the like. The result of actually collecting the components of the soil and analyzing it based on the chemical analysis method may be used, or the data detected by a well-known soil sensor may be used. Further, an image obtained by capturing the soil with a camera, and an image obtained by extracting only a characteristic part of the image by utilizing a well-known deep learning technique may be used.

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

Each combination (intermediate node) of the reference image information and the reference soil information is associated with each other through three or more levels of association with the crop yield as this output solution. The reference image information and the reference soil information are arranged on the left side through this degree of association, and the yield of each crop is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the yield of any crop with respect to the reference image information and the reference soil information arranged on the left side. In other words, this degree of association is an index indicating what kind of crop yield is likely to be associated with each reference image information and reference soil information, and the reference image information and reference soil information. It shows the accuracy in selecting the most probable crop yield from. In the example of FIG. 17, w13 to w22 are shown as the degree of association. These w13 to w22 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 yield of agricultural products as output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node with the yield of agricultural products as an output.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates the reference image information, the reference soil information, and the past data on how much the crop yield was in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 17 is created.

For example, it is assumed that the reference image information P11 reflects the state of the crop damaged by outsourcing. At this time, when the composition of the soil in which the crop was actually cultivated was investigated and the value was "pH ●●, component ○ ×" corresponding to the reference soil information P14, the actual crop was harvested in the previous data. As a result of the follow-up survey, the amount of harvest was extracted. The yield may be composed of any unit, and for example, the yield of the crop per unit area may be indicated by weight (ton).

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference image information P11 and the reference soil information P16 “pH ▲▲, component □ ○”, the actual yield of the crop is analyzed from the past data. If there are many cases where the yield is 9 tons, the degree of association that leads to this yield of 9 tons is set higher, and if there are many cases where the yield is 3 tons and there are few cases where the yield is 9 tons. The degree of association that leads to a yield of 3 tons is set high, and the degree of association that leads to a yield of 9 tons is set low. For example, in the example of the intermediate node 61a, the yield is linked to the output of 9 tons and the yield of 3 tons, but from the previous case, the degree of association of w13, which leads to the yield of 9 tons, is set to 7 points, and the yield is 3 tons. The degree of association of w14 that leads to 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. 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. 17, the node 61b is a node in which the reference soil information P14 is combined with the reference image information P11, and the degree of association of the yield of 6 tons is w15 and the yield is 1 ton. The degree of association is w16. The node 61c is a node of the combination of the reference soil information P15 and P17 with respect to the reference image information P12, and the degree of association of the yield of 3 tons is w17 and the degree of association of the yield of 6 tons is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the yield of a new crop from now on, the yield of the crop will be determined by using the above-mentioned learned data. In such a case, the image information is newly acquired and the soil information is acquired.

The newly acquired image information is captured by the camera by the information acquisition unit 9 described above. This shooting targets the crops that are about to be cultivated.

The acquisition of soil information is the soil in which the crop to be newly cultivated is actually planted, and the acquisition method is the same as when acquiring the reference soil information described above.

Based on the newly acquired image information and soil information in this way, the yield of the crop that has actually acquired the newly acquired image information and soil information is predicted. 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 image information is the same as or similar to P12 and the soil information is P17, the node 61d is associated through the degree of association, and this node In 61d, "yield 6 tons" is associated with w19, and "yield 7 tons" is associated with a degree of association w20. In such a case, “6 tons of yield”, which has 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 the “yield of 7 tons”, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In addition, 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 association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In FIG. 18, the output is not the yield but the quality of the crop in a similar manner. Similarly, in such a case, the quality of the crop is investigated in advance from the past data with respect to the reference image information and the reference soil information, and the degree of association is set in the same manner to build a trained model. Then, the image information and the soil method are obtained from the growing crop in the same manner, and the quality of the crop is predicted by referring to the learned model (degree of association) from these. This makes it possible to predict the quality of crops as well as the yield.

FIG. 19 shows an example in which a combination of the above-mentioned reference image information and the reference external environment information and a degree of association of three or more levels of the yield of agricultural products with respect to the combination are set.

As the input data, such reference image information and reference external environment information are arranged side by side. The intermediate node shown in FIG. 19 is a combination of reference image information and reference external environment information as such input data.

The reference external environment information is external environment data detected in the process of growing the agricultural product, for example, data on weather conditions such as solar radiation, temperature, humidity, wind direction, and rain, typhoons, floods, droughts, sunshine, and the like. Data related to the disaster situation. Actually, it is desirable, but not limited to, the reference external environment information to acquire the external environment at the time of acquisition of the reference image information. These reference external environment information may be composed of sensing means for acquiring real-time data such as a temperature sensor, a humidity sensor, a light amount sensor, a wind direction meter, and a rain gauge. The situation such as drought and sunshine may be analyzed ex post facto.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the estimation device 2 accumulates reference image information, reference external environment information, and data on how much the yield was in that case in predicting the actual yield of the crop. By analyzing and analyzing these, the degree of association shown in FIG. 19 is created.

In the example of the degree of association shown in FIG. 19, the node 61b is a node in which the reference external environment information P18 “rainfall ●●, temperature ○ ×” is combined with the reference image information P11, and the yield is 6 tons. The degree of association is w15, and the degree of association of 1 ton of yield is w16.

Similarly, when such a degree of association is set, image information is newly acquired and external environment information is acquired. The image information corresponds to the reference image information, and the external environment information corresponds to the reference external environment information.

The method of acquiring the external environment information is the same as the method of acquiring the reference external environment information described above.

In determining the yield, the degree of association shown in FIG. 19 obtained in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12 and the acquired external environment information corresponds to the reference external environment information P19, the combination is associated with the node 61c. The node 61c is associated with a yield of 3 tons by a degree of association w17 and a yield of 7 tons by a degree of association w18. As a result of such a degree of association, based on w17 and w18, the yield of agricultural products at the time when the image information and the external environmental information are newly acquired is obtained.

In FIG. 20, the output is not the yield but the quality of the crop in a similar manner. Similarly, in such a case, the quality of the crop is investigated in advance from the past data with respect to the reference image information and the reference external environment information, and the degree of association is set in the same manner to build a trained model. Then, the image information and the soil method are obtained from the growing crop in the same manner, and the quality of the crop is predicted by referring to the learned model (degree of association) from these. This makes it possible to predict the quality of crops as well as the yield.

FIG. 21 shows an example in which a combination of the above-mentioned reference image information and reference history information and a degree of association of three or more levels of association with the yield of agricultural products with respect to the combination are set.

As the input data, such reference image information and reference history information are arranged side by side. The intermediate node 62 shown in FIG. 21 is a combination of reference image information and reference history information as such input data.

The reference history information is the history of the agricultural work actually performed in the process of growing the crop. It summarizes what kind of farming work has been done from sowing to planting seedlings to harvesting. For example, when, how much, and how the agricultural work such as sprinkling water, fertilizing, spraying pesticides, and exterminating weeds was performed is reflected in this reference history information. .. Actually, this reference history information may be composed of an electronic data of the farm work diary kept by the farmer, or it may be obtained through a PC, a smartphone, etc. in which the record of the actual farm work is recorded. You may.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 21. That is, the estimation device 2 accumulates reference image information, reference history information, and data on how much the yield was in that case in predicting the actual yield of the crop, and these By analyzing and analyzing the above, the degree of association shown in FIG. 21 is created.

In the example of the degree of association shown in FIG. 21, the node 61b is a node in which the reference image information P11 and the reference external environment information P22 are combined, and the degree of association of the yield of 6 tons is w15 and the yield is 1 ton. The degree of association is w16.

Similarly, when such a degree of association is set, image information is newly acquired and history information is acquired. The image information corresponds to the reference image information, and the history information corresponds to the reference history information.

The method of acquiring the history information is the same as the method of acquiring the reference history information described above.

In determining the yield, the degree of association shown in FIG. 21 obtained in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12 and the acquired history information corresponds to the reference history information P23, the combination is associated with the node 61c, and the node 61c is associated with the combination. The yield of 3 tons is associated with the degree of association w17, and the yield of 7 tons is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the image information and the history information are actually newly acquired, and the yield of the agricultural product is obtained.

In FIG. 22, the output is not the yield but the quality of the crop in a similar manner. Similarly, in such a case, the quality of the crop is investigated in advance from the past data with respect to the reference image information and the reference history information, and the degree of association is set in the same manner to build a trained model. Then, the image information and the soil method are obtained from the growing crop in the same manner, and the quality of the crop is predicted by referring to the learned model (degree of association) from these. This makes it possible to predict the quality of crops as well as the yield.

FIG. 23 shows an example in which, in addition to the above-mentioned reference image information and reference soil information, a combination of reference timing information and a harvest amount of the combination are set to three or more levels of association. Shown.

The reference time information is information indicating when the reference image information is actually acquired.

In such a case, as shown in FIG. 23, the degree of association is expressed as a set of combinations of reference image information, reference soil information, and reference time information as nodes 61a to 61e of intermediate nodes as described above. Will be done.

For example, in FIG. 23, in the node 61c, the reference image information P12 is associated with the association degree w3, the reference soil information P15 is associated with the association degree w7, and the reference time information P27 is associated with the association degree w11. Similarly, in the node 61e, the reference image information P13 is associated with the association degree w5, the reference soil information P15 is associated with the association degree w8, and the reference time information P26 is associated with the association degree w10.

Similarly, when such a degree of association is set, the yield is predicted based on the newly acquired image information, the soil information, and the timing information at the time of acquisition of the image information.

In determining the yield, the degree of association shown in FIG. 23 obtained in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12, the acquired soil information corresponds to the reference soil information P15, and the acquired time information corresponds to the reference time information P27, the combination is A node 61c is associated, and the node 61c is associated with a yield of 3 tons with a degree of association w17 and a yield of 7 tons 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 further combining such reference timing information, in addition to the combination of the reference image information and the reference soil information described above, the combination of the reference image information and the reference external environment information, and the reference image information It can also be applied in combination with history information for reference.

Further, by further combining the reference timing information, it can be similarly applied when outputting the quality of agricultural products as an output solution.

FIG. 24 shows an example in which, in addition to the above-mentioned reference image information and reference soil information, a combination of reference type information and a yield of three or more levels for the combination are set. Shown.

The reference type information is information on the types and varieties of agricultural products. The type of crop is obtained by listening to the farmer who grows the crop or by discriminating it from the image.

In such a case, as shown in FIG. 24, the degree of association is expressed as a set of combinations of reference image information, reference soil information, and reference type information as nodes 61a to 61e of intermediate nodes as described above. Will be done.

For example, in FIG. 24, in the node 61c, the reference image information P12 is associated with the association degree w3, the reference soil information P15 is associated with the association degree w7, and the reference type information P29 is associated with the association degree w11. Similarly, in the node 61e, the reference image information P13 is associated with the association degree w5, the reference soil information P15 is associated with the association degree w8, and the reference type information P28 is associated with the association degree w10.

Similarly, when such a degree of association is set, the yield is predicted based on the newly acquired image information, the soil information, and the type of the crop that can be said to be the acquisition target of the image information.

In determining the yield, the degree of association shown in FIG. 24 obtained in advance is referred to. For example, when the acquired image is the same as or similar to the reference image information P12, the acquired soil information corresponds to the reference soil information P15, and the acquired type information corresponds to the reference type information P29, the combination is A node 61c is associated, and the node 61c is associated with a yield of 3 tons with a degree of association w17 and a yield of 7 tons 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 further combining such reference type information, in addition to the combination of the above-mentioned reference image information and the reference soil information, the combination of the reference image information and the reference external environment information and the reference image information It is also applicable to the combination of the reference history information and the reference history information, and further to the combination of the reference time information.

Further, by further combining the reference type information, it can be similarly applied to the case where the quality of the agricultural product is output as the output solution.
The fourth embodiment is not limited to the above-described embodiment. FIG. 25 shows an example in which a combination of reference growth data and reference external environmental information and a degree of association of three or more levels of association between the crop yield and the combination are set.

As input data, such reference growth data and reference external environment information are arranged side by side. The intermediate node shown in FIG. 25 is a combination of the reference growth data and the reference external environment information as such input data.

The reference growth data is the amount of photosynthesis actually detected from the crop, sugar content, water content, size, shape and color of the fruit, infrared, far infrared, visible light, near-ultraviolet, ultraviolet light applied to the crop. It is data such as information of each spectrum of. As the reference growth data, sampling may be performed each time during the growth process of the crop, and these data may be actually detected from the fruits and leaves.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 25. That is, the estimation device 2 accumulates reference growth data, reference external environmental information, and data on how much the yield was in that case in predicting the actual yield of the crop. By analyzing and analyzing these, the degree of association shown in FIG. 25 is created.

In the example of the degree of association shown in FIG. 25, the node 61b is a node in which the reference external environmental information P18 “rainfall ●●, temperature ○ ×” is combined with the reference growth data P11, and the yield is 6 tons. The degree of association is w15, and the degree of association of 1 ton of yield is w16.

Similarly, when such a degree of association is set, growth data is newly acquired and external environment information is acquired. The growth data corresponds to the reference growth data, and the external environment information corresponds to the reference external environment information.

The method of acquiring the external environment information is the same as the method of acquiring the reference external environment information described above.

In determining the yield, the degree of association shown in FIG. 25 obtained in advance is referred to. For example, when the acquired growth data is the same as or similar to the reference growth data P12 and the acquired external environment information corresponds to the reference external environment information P19, the combination is associated with the node 61c. In this node 61c, a yield of 3 tons is associated with a degree of association w17, and a yield of 7 tons is associated with a degree of association w18. As a result of such a degree of association, based on w17 and w18, the yield of the crop at the time when the new growth data and the external environmental information are actually acquired will be obtained.

In FIG. 26, the output is not the yield but the quality of the crop in a similar manner. Similarly, in such a case, the quality of the crop is investigated in advance from the past data with respect to the reference growth data and the reference external environmental information, and the degree of association is set in the same manner to build a trained model. Then, the growth data and the soil method are obtained from the growing crop in the same manner, and the quality of the crop is predicted by referring to the learned model (degree of association) from these. This makes it possible to predict the quality of crops as well as the yield.

When combining the reference growth data, the above-mentioned reference image information is similarly combined with any one or more of the reference soil information, the reference history information, and the reference time information to form the degree of association, and the yield is obtained. Or quality may be searched.

Further, in the fourth embodiment, as the search solution, not only one of the yield amount and the quality may be output, but both the yield amount and the quality may be output as the search solution. In such a case, the yield and quality are learned in a data set between the above-mentioned reference growth data, reference image information, reference soil information, reference history information, reference time information, and the like. By doing so, the above-mentioned degree of association is formed. As a result, by inputting the input parameters in the same manner, it is possible to output the yield and quality as a search solution in one set.

Fifth Embodiment FIG. 27 shows an example of the degree of association in the fifth embodiment. In the fifth embodiment, an example is shown in which a combination of the reference sales target information and the reference market condition information and a cultivation method for the combination are set to have three or more levels of association with each other.

As the input data, such reference sales target information and reference market condition information are arranged side by side. The intermediate node shown in FIG. 27 is a combination of reference market condition information and reference sales target information as such input data.

The reference sales target information is information on the sales target agricultural products that are harvested by the farmer and shipped as agricultural products, and is basically expressed by the quantity and quality of the sales target agricultural products. The amount of the crop corresponds to the yield as the output solution in the fourth embodiment described above, and the quality of the crop corresponds to the quality as the output solution in the fourth embodiment. In addition, the reference sales target information may be obtained from the growth status searched through the third embodiment described above. Through the third embodiment, it is possible to predict the yield and the like as the growth situation from the supply and demand environment and the cultivation environment. The yield that can be searched for in this way may be used as reference sales target information.

In the fourth embodiment, not only one of the yield and the quality is output, but both the yield and the quality may be output as a search solution. Therefore, the reference sales target information also corresponds to this, and may be represented by the quantity or quality of the crops to be sold, or the quantity and quality of the crops to be sold.

The reference market information is various information regarding the market conditions of agricultural products. As an example of this reference market information, price movements for each type of crop are targeted. This reference market information may be displayed as a time-series chart, a line graph, or the like for each type of crop. In addition, information such as Bollinger band, volume, MACD, and moving average line may be attached.

In addition, the cultivation method as an output solution is information on the optimum cultivation method for the crop currently being cultivated. The cultivation method referred to here includes the timing of sowing seeds, planting seedlings, the timing of harvesting, and various scheduling of agricultural work. This cultivation method also includes information on how to actually grow which variety of crops. Further, since the details of this cultivation method are the same as those described in the second embodiment, the following description will be omitted by quoting this.

The estimation device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 27. That is, the estimation device 2 accumulates reference sales target information, reference market condition information, and data on which cultivation method was appropriate in the case of proposing the cultivation method, and analyzes these. , The degree of association shown in FIG. 25 is created by analysis.

When considering actual farm management, sales depend on the quantity and unit price of the crops to be sold. The unit price of a crop depends on the quality of the crop. The higher the quality of the crop, the higher the unit price, and conversely, the lower the quality, the lower the unit price. In addition to these, sales are also affected by the market conditions of agricultural products.

In the present invention, we seek a cultivation method aiming at the quantity and quality of agricultural products that have the highest sales in terms of agricultural management. Rather than simply aiming to produce a large amount of high-quality crops, considering market conditions, there are cases where it sells higher if it is sold earlier, and conversely, it sells higher if it is sold later. In consideration of such market information, various cultivation methods were implemented, such as delaying growth, sometimes accelerating growth, or harvesting earlier or later, or adjusting the sowing time earlier or later. I will go.

Therefore, it is desirable that the degree of association shown in FIG. 27 is learned from a data set with a cultivation method for a combination of reference sales target information and reference market condition information when sales and profits are larger. Alternatively, the weighting of the degree of association may be changed according to the amount of sales or profit. In other words, for the data set with the cultivation method for the combination of the reference sales target information with high sales and profit and the reference market condition information, the degree of association is set higher, and conversely, the reference sales target information with low sales and profit, reference. The degree of association may be set lower for the data set with the cultivation method for the combination of market information.

Under the premise that such a degree of association is set, new sales target information and market condition information are acquired. The information to be sold is the quality and / or quantity of the crops to be sold, or the quality and / or quantity predicted for the crops that are planned to be sold in the future and are currently in the process of being cultivated.

The market information is the same as the above-mentioned reference market information, but this is also newly acquired. The market information to be acquired may be acquired within a certain period (daily unit, weekly unit, monthly unit, etc.) retroactive from the present time.

In determining the cultivation method, the degree of association shown in FIG. 27 obtained in advance is referred to. For example, when the acquired sales target information is the same as or similar to the reference sales target information P12 and the acquired market condition information corresponds to the reference market condition information P19, the combination is associated with the node 61c. This node 61c is associated with "spraying water twice a day, weeding once a week" with a degree of association w17, and "putting an insect repellent net 3 months after sowing" with a degree of association w18. .. As a result of such a degree of association, based on w17 and w18, the cultivation method of the agricultural product at the time when the new sales target information and the market condition information are actually acquired will be obtained.

At this time, the reference sales target information may correspond to the yield or quality searched in the fourth embodiment, or the yield and quality of the agricultural product. In such a case, when inputting the sales target information, the harvest amount or quality as the output solution searched in this fourth embodiment, or the harvest amount and quality may be input as it is as input data.

That is, by inputting growth data, image information, soil information, history information, time information, etc. through the fourth embodiment, the yield or quality of the crop, or the yield and quality of the crop can be obtained as an output solution. By inputting this output solution as it is as input data and inputting the market condition information separately, it is possible to feed back to the optimum cultivation method. In addition, the sales target information may be obtained from the growth status searched through the third embodiment described above. Through the third embodiment, it is possible to predict the yield and the like as the growth situation from the supply and demand environment and the cultivation environment. The yield that can be searched for in this way may be used as sales target information.

At this time, as shown in FIG. 28, the reference information U is composed of reference growth data, reference image information, reference soil information, reference history information, reference time information, etc., and is used as reference information V. , Reference market information may be used for determination based on the degree of association of these combinations. The output solution corresponds to each cultivation method described above.

At this time, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output through the fourth embodiment, this is used as an input as it is, and the output is searched by using the degree of association with other reference information V. May be good.

FIG. 29 shows an example in which control instruction information is included in a data set and trained as an output solution. That is, it is an example in which the combination of the reference sales target information and the reference market condition information and the degree of association between the combination and the control instruction information are formed. Even in such a case, the control instruction information can be output by inputting the target information and the market condition information, and the cultivation environment can be controlled based on the control instruction information. These control instruction information and its control method are the same as those in the third embodiment.

The fourth embodiment is not limited to the above-mentioned example. As the output solution shown in FIG. 29, the sales strategy may be included in the data set and learned as an alternative to the control instruction information. That is, the combination of the reference sales target information and the reference market condition information, and the degree of association between the combination and the sales strategy are formed. The sales strategy is which timing to sell the crops, which distribution route to sell the crops, what kind of crops to sell at what timing, what kind of crops to sell to which wholesaler, which. It includes policies and strategies for when to sell different types of crops to which stores, department stores, supermarkets, or consumers.

Such a data set can be created by actually acquiring past sales timings, sales routes, and sales destinations in relation to the types of agricultural products and converting them into data. Even in such a case, the sales strategy can be output by inputting the target information and the market condition information, and it is possible to make a proposal regarding sales based on this sales strategy.

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 make a judgment of this search solution with higher accuracy than a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Further, according to the present invention, it is characterized in 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 levels, but the degree of association is not limited to this and can be described by a numerical value of 3 or more levels at any stage. 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. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable that it appears once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition, when 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 done by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

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.

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 (9)

In the crop cultivation method proposal program that proposes crop cultivation methods,
A combination of reference sales target information regarding the quality and / or quantity of the crop to be sold, reference market information regarding the market condition of the crop at the time of sale, and a method of cultivating the crop for the combination. 1 Linkage acquisition step to acquire the linkage degree in advance, and
An information acquisition step to acquire sales target information regarding the quality and / or quantity of newly sold agricultural products, and to acquire market information regarding market conditions at the time of new sales.
With reference to the first degree of association acquired in the above-mentioned degree of association acquisition step, the computer is made to execute the proposal step of proposing a cultivation method of agricultural products based on the sales target information acquired through the above-mentioned information acquisition step and the market condition information. A program that proposes cultivation methods for agricultural products. In the crop cultivation method proposal program that proposes crop cultivation methods,
A combination of reference sales target information regarding the quality and / or quantity of the crop to be sold, reference market information regarding the market condition of the crop at the time of sale, and control instruction information for cultivating the crop for the combination. The association degree acquisition step to acquire the first association degree of three or more stages in advance, and
An information acquisition step to acquire sales target information regarding the quality and / or quantity of newly sold agricultural products, and to acquire market information regarding market conditions at the time of new sales.
The control instruction information is extracted based on the sales target information and the market condition information acquired through the information acquisition step with reference to the first association degree acquired in the above-mentioned association degree acquisition step, and based on the extracted control instruction information. A crop cultivation method proposal program characterized by having a computer execute control steps that control the cultivation of crops. In the crop sales strategy proposal program that proposes crop sales strategies
The first of three or more stages of the reference sales target information regarding the quality and / or quantity of the crop to be sold, the reference market information regarding the market condition of the crop at the time of sale, and the sales strategy of the crop for the combination. The association degree acquisition step to acquire the association degree in advance, and
An information acquisition step to acquire sales target information regarding the quality and / or quantity of newly sold agricultural products, and to acquire market information regarding market conditions at the time of new sales.
Refer to the first degree of association acquired in the above-mentioned degree of association acquisition step, and let the computer execute the proposal step of proposing a sales strategy of agricultural products based on the sales target information acquired through the above-mentioned information acquisition step and the market condition information. A farm product sales strategy proposal program that is characterized by this. In the above-mentioned degree of association acquisition step, the combination of the reference image information obtained by imaging the growing crop and the reference soil information regarding the soil in which the growing crop is cultivated and the above-mentioned growing crop for the combination are harvested. Obtain the second degree of association with the yield and / or quality of 3 or more levels in advance,
Prediction information acquisition step to acquire image information by taking an image of a crop and to acquire soil information about the soil in which the crop is planted,
With reference to the second degree of association acquired in the above-mentioned degree of association acquisition step, the yield and / or quality of the newly grown crop is predicted based on the image information and soil information acquired through the above-mentioned prediction information acquisition step. With more predictive steps to
The crop cultivation method proposal program according to claim 1 or 2, wherein the information acquisition step acquires sales target information according to the yield and / or quality predicted in the forecast information acquisition step. In the above-mentioned degree of association acquisition step, when the combination of the reference image information obtained by imaging the growing crop and the reference external environment information regarding the external environment in the growing process of the crop and the above-mentioned growing crop for the combination are harvested. Obtained in advance the second degree of association with the yield and / or quality of 3 or more levels.
Prediction information acquisition step to acquire image information by taking an image of a crop and to acquire external environment information about the external environment in the growing process of the crop.
With reference to the second degree of association acquired in the above-mentioned degree of association acquisition step, the yield and / or quality of the newly cultivated crop is determined based on the image information acquired through the above-mentioned prediction information acquisition step and the external environment information. It also has a prediction step to predict,
The crop cultivation method proposal program according to claim 1 or 2, wherein the information acquisition step acquires sales target information according to the yield and / or quality predicted in the forecast information acquisition step. In the above-mentioned degree of association acquisition step, the combination of the reference image information obtained by imaging the growing crop and the reference history information regarding the farm work history of the crop, the yield when the above-mentioned growing crop is harvested for the combination, and the yield / Or obtain the second degree of association with quality at least 3 levels in advance,
Prediction information acquisition step to acquire image information by taking an image of a crop and to acquire a farm work history image in the growing process of the crop,
With reference to the second degree of association acquired in the above-mentioned degree of association acquisition step, the yield and / or quality of the newly cultivated crop is determined based on the image information acquired through the above-mentioned prediction information acquisition step and the agricultural work history image. It also has a prediction step to predict,
The crop cultivation method proposal program according to claim 1 or 2, wherein the information acquisition step acquires sales target information according to the yield and / or quality predicted in the forecast information acquisition step. In the crop cultivation method proposal system that proposes crop cultivation methods,
A combination of reference sales target information regarding the quality and / or quantity of the crop to be sold, reference market information regarding the market condition of the crop at the time of sale, and a method of cultivating the crop for the combination. 1 Correlation degree acquisition means for acquiring the degree of association in advance,
Information acquisition means for acquiring sales target information on the quality and / or quantity of newly sold agricultural products, and market information on market conditions at the time of new sales.
It is necessary to provide a proposal means for proposing a cultivation method of agricultural products based on the sales target information and the market condition information acquired through the information acquisition means with reference to the first association degree acquired by the association degree acquisition means. A system that proposes a cultivation method for the characteristic agricultural products. In the crop cultivation method proposal system that proposes crop cultivation methods,
A combination of reference sales target information regarding the quality and / or quantity of the crop to be sold, reference market information regarding the market condition of the crop at the time of sale, and control instruction information for cultivating the crop for the combination. The association degree acquisition means for acquiring the first association degree of three or more stages in advance, and
Information acquisition means for acquiring sales target information on the quality and / or quantity of newly sold agricultural products, and market information on market conditions at the time of new sales.
The control instruction information is extracted based on the sales target information and the market condition information acquired through the information acquisition means with reference to the first association degree acquired by the association degree acquisition means, and the extracted control instruction information is used. A crop cultivation method proposal system characterized by being provided with a control means for controlling the cultivation of crops based on the basis. In the crop sales strategy proposal system that proposes crop cultivation methods,
The first of three or more stages of the reference sales target information regarding the quality and / or quantity of the crop to be sold, the reference market information regarding the market condition of the crop at the time of sale, and the sales strategy of the crop for the combination. The means of acquiring the degree of association and the means of acquiring the degree of association in advance,
Information acquisition means for acquiring sales target information on the quality and / or quantity of newly sold agricultural products, and market information on market conditions at the time of new sales.
It is necessary to provide a proposal means for proposing a sales strategy of agricultural products based on the sales target information and the market condition information acquired through the above information acquisition means with reference to the first degree of association acquired by the above-mentioned association degree acquisition means. A characteristic agricultural product sales strategy proposal system.

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