Pesticide composition, information processing device and computer program

Abstract

To provide agrochemical compositions indispensable for efficient agricultural work by applying an appropriate agrochemical at an appropriate time and in a required quantity according to situations of fields, information processing device and program.SOLUTION: A determination system including a server device, a mobile spraying device and a terminal device connectable to each other via a network, executes: regarding a specific area included in a target area in which paddy rice is cultivated acquiring type information representing a type of at least one damage having occurred in the specific area and quantity information representing a quantity of each of the at least one damage having occurred in the specific area 100; determining a plurality of agrochemical compositions constituting an agrochemical to be sprayed in the specific area and a quantity of each of the plurality of agrochemical compositions based on the type information and quantity information 200; and spraying the agrochemical prepared by mixing each of the determined plurality of agrochemical compositions by the determined quantity in the target area or the specific area included in the target area 300.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to an agrochemical composition, an information processing device, and a computer program. [Background technology]

[0002] Currently, a wide variety of pesticide formulations developed for use in paddy rice are available on the market, including, for example, herbicides, insecticides and fungicides.

[0003] As for herbicides, many agents have been developed and sold according to the active ingredient and the optimum spraying time, such as early-stage agents that are sprayed before weeds emerge, before sowing rice seeds or transplanting rice, or immediately after sowing or transplanting, early-middle stage one-shot agents that are sprayed after transplanting, and late-stage and mid-late stage agents that are sprayed from the middle to late stages of rice growth to control weeds that could not be controlled with these early-stage and early-middle stage one-shot agents and difficult-to-control weeds. As for insecticides and fungicides, seed treatment agents, box application agents, and field application agents have been developed and sold according to the characteristics of the active ingredients.

[0004] From the viewpoint of efficient control, mixtures containing multiple active ingredients have been developed, and by using such mixtures, a wide variety of diseases, pests, and / or weeds can be efficiently controlled. On the other hand, products that match the occurrence conditions of diseases, pests, and / or weeds specific to a field are not necessarily sold, and sometimes more active ingredients than necessary are sprayed, which is a concern for the burden on the environment. In view of this situation, applying an appropriate amount of agrochemical formulations containing single or multiple active ingredients according to the field conditions will lead to a reduction in the burden on the environment.

[0005] Furthermore, if the occurrence of diseases, pests and / or weeds in a field cannot be accurately understood, it is possible that appropriate pesticides cannot be selected and diseases, pests and / or weeds cannot be completely controlled. As a result, additional pesticides will be sprayed to deal with the diseases, pests and / or weeds that could not be controlled, which may increase the producer's efforts, costs and environmental burden. Furthermore, in fields where diseases, pests and / or weeds cannot be properly controlled, the growth of crop plants may be insufficient, leading to reduced yields. In other words, applying the appropriate pesticide at the right time and in the required amount is essential for efficient farming.

[0006] A technology has been known in the past in which an unmanned flying device operates a unit that sprays a liquid agent at a location determined by a processing unit based on the results of analyzing images of farmland captured by a camera (Patent Document 7). However, this technology does not select an appropriate agent depending on the occurrence of diseases, pests, and / or weeds in paddy fields.

[0007] From the viewpoint of efficient application of pesticides, it is an effective method to select multiple pesticide formulations containing a single active ingredient, mix them in appropriate amounts, and apply them in one go. However, there are various types of pesticide formulations, such as granules, emulsifiable concentrates, flowable formulations, and water dispersible granules, and there are several problems when they are mixed and used. For example, (1) even if the pesticide formulation type (emulsifiable concentrate, flowable formulation, water dispersible granule, etc.) can be diluted and sprayed, they are formulations optimized for each active ingredient and have different compositions, so when the mixture prepared by mixing them is applied, more chemicals, which are auxiliary ingredients of the formulation, than necessary, are released into the environment. In addition, (2) when multiple selected pesticide formulations are mixed, there are cases where spraying becomes difficult due to changes in the physical and chemical properties of the diluted solution (for example, instability of the diluted solution that does not occur normally, decomposition of the active ingredient in the diluted solution, clogging of the spray nozzle due to precipitation and aggregation of solids, etc.). To avoid these problems, it is necessary to develop a formulation that uses standardized auxiliary ingredients for active ingredients that have various physical and chemical properties.

[0008] Furthermore, from the viewpoint of efficient application of pesticides to fields, for example, when the field is a paddy field, it is effective to apply the pesticide locally and use the surface water to diffuse the active ingredient throughout the paddy field. Several techniques for diffusing active ingredients have already been proposed. For example, as paddy field herbicides, flowable agents with excellent diffusibility that can be applied from the ridge without dilution (Patent Document 1), and jumbo agents in which floating granules packed in a water-soluble film are thrown from the ridge (Patent Document 2) have been developed and sold. Furthermore, these formulations applied for local application may contain a dispersant so that the active ingredient can be sufficiently diffused in water after application (Patent Document 4, Patent Document 5, and Patent Document 6). In addition, as an application method, application of water dispersible granules through the water inlet (Patent Document 3) has been proposed. However, many of the pesticides used in these are mixtures prepared by mixing multiple pesticide formulations, which is not preferable from the viewpoint of the above-mentioned environmental load. In addition, insecticides such as surf agents have been developed that are dropped onto the water surface and spread an oil film to diffuse the active ingredients, but because they use organic solvents, these insecticides are also undesirable from the perspective of environmental impact.

[0009] Furthermore, when multiple pesticide formulations containing a single active ingredient are selected, mixed in appropriate amounts, and applied, it is important to ensure the diffusibility of the pesticide formulation in order to use the surface water to diffuse the active ingredient throughout the entire paddy field. [Prior art documents] [Non-patent literature]

[0010] [Non-Patent Document 1] Pesticide Formulation Guide / Japan Pesticide Science Society, Pesticide Formulation and Application Method Research Group / 1997 [Patent documents]

[0011] [Patent Document 1] Special Publication No. 7-47522 [Patent Document 2] Patent No. 2957751 [Patent Document 3] JP 2010-083869 A [Patent Document 4] JP 2011-126786 A [Patent Document 5] JP 2002-338403 A [Patent Document 6] JP 2012-077097 A [Patent Document 7] International Publication No. 2020 / 225278 Summary of the Invention [Problem to be solved by the invention]

[0012] An object of the present invention is to provide an agrochemical composition, an information processing device, and a computer program. [Means for solving the problem]

[0013] The pesticide composition according to one embodiment is a liquid pesticide composition comprising the following components (a) and (b1), or a solid pesticide composition comprising the following components (a) and (b2): (a) at least one active ingredient selected from the group consisting of an active ingredient having an insecticidal effect, an active ingredient having a fungicidal effect, and an active ingredient having a herbicidal effect; (b1) 1 to 200 g / L of an anionic dispersant; (b2) 1 to 20% w / w of an anionic dispersant.

[0014] An information processing device according to one embodiment is characterized in that it "has at least one processor, and is configured to acquire, for a specific area included in a target area in which rice is cultivated, type information indicating the type of at least one damage that has occurred in the specific area and amount information indicating the amount of each of the at least one damage that has occurred in the specific area, and to determine, based on the type information and the amount information, a plurality of pesticide compositions that constitute a drug to be sprayed in the specific area and the amount of each of the plurality of pesticide compositions."

[0015] The method according to one embodiment is characterized as "a method executed by at least one processor that executes computer-readable instructions, comprising the steps of: acquiring, for a specific area included in a target area in which rice is grown, type information indicating the type of at least one damage that has occurred in the specific area, and amount information indicating the amount of each of the at least one damage that has occurred in the specific area; and determining, based on the type information and the amount information, a plurality of pesticide compositions that constitute the agent to be sprayed in the specific area, and the amount of each of the plurality of pesticide compositions."

[0016] The computer program of one embodiment is characterized in that, "when executed by at least one processor, it causes the at least one processor to function to obtain, for a specific area included in a target area in which rice is grown, type information indicating the type of at least one damage that has occurred in the specific area and amount information indicating the amount of each of the at least one damage that has occurred in the specific area, and to determine, based on the type information and the amount information, a plurality of pesticide compositions that constitute a drug to be sprayed in the specific area and the amount of each of the plurality of pesticide compositions."

[0017] Another aspect of the information processing device is characterized in that it "has at least one processor, and the at least one processor is configured to acquire type information indicating the type of at least one damage that has occurred in a specific area included in a target area in which seed rice is to be directly sown or seedlings grown from a seedling box in which the seed rice is sown are to be transplanted, and quantity information indicating the amount of each of the at least one damage that has occurred in the specific area, and to determine, based on the type information and the quantity information, a plurality of pesticide compositions that constitute a drug to be applied to the seed rice to be directly sown in the specific area or the seed rice to be sown in a seedling box for transplanting the seedlings in the specific area, and the amount of each of the plurality of pesticide compositions."

[0018] A method according to another embodiment is characterized in that it is "a method executed by at least one processor that executes computer-readable instructions, comprising the steps of: acquiring type information indicating the type of at least one damage that has occurred in a specific area included in a target area in which seeds are to be directly sown or seedlings grown from a seedling box in which the seeds are sown are to be transplanted, and quantity information indicating the amount of each of the at least one damage that has occurred in the specific area; and determining, based on the type information and the quantity information, a plurality of pesticide compositions constituting an agent to be applied to the seeds to be directly sown in the specific area or the seeds to be sown in a seedling box for transplanting the seedlings in the specific area, and the amount of each of the plurality of pesticide compositions."

[0019] Another embodiment of the computer program is characterized in that, "when executed by at least one processor, it causes the at least one processor to function as follows: for a specific area included in a target area in which rice seeds are to be directly sown or seedlings grown from a seedling box in which the rice seeds have been sown are to be transplanted, type information indicating the type of at least one damage that has occurred in the specific area and amount information indicating the amount of each of the at least one damage that has occurred in the specific area are obtained; and, based on the type information and the amount information, it determines a plurality of pesticide compositions constituting a drug to be applied to the rice seeds to be directly sown in the specific area or the rice seeds to be sown in a seedling box for transplanting the seedlings in the specific area, and the amount of each of the plurality of pesticide compositions." Effect of the Invention

[0020] According to the present invention, it is possible to provide a pesticide composition, an information processing device, and a computer program. [Brief description of the drawings]

[0021] [Figure 1] FIG. 1 is a block diagram illustrating an example of an overall configuration of a determination system according to an embodiment. [Diagram 2]FIG. 2 is a block diagram showing an example of the hardware configuration of the information processing device 10A mounted on the server device 10, the mobile spraying device 20, and the terminal device 30 in the determination system shown in FIG. [Diagram 3] FIG. 3 is a flow diagram showing an example of an operation executed by the determination system 1 shown in FIG. [Figure 4] FIG. 4 is a flow diagram specifically illustrating an example of a part (ST100) of the operations executed by the decision system 1 shown in FIG. [Diagram 5] FIG. 5 is a schematic diagram conceptually illustrating an example of information generated by the information processing device 10A using a learning model in the determination system illustrated in FIG. [Figure 6] FIG. 6 is a schematic diagram showing an example of a zone determination method executed by the information processing device 10A in the determination system shown in FIG. [Figure 7] FIG. 7 is a flow diagram specifically illustrating an example of a part (ST200) of the operations executed by the decision system 1 shown in FIG. [Figure 8] FIG. 8 is a block diagram showing an example of the hardware configuration of the mobile sprinkling device 20 shown in FIG. [Figure 9] FIG. 9 is a flow diagram specifically illustrating an example of a part (ST300) of the operations executed by the determination system 1 shown in FIG. [Figure 10] FIG. 10 is a flow diagram illustrating another example of the operations performed by the decision system 1 shown in FIG. [Figure 11] FIG. 11 is a flow diagram specifically showing an example of a part (ST1000) of the operations executed by the decision system 1 shown in FIG. [Figure 12] FIG. 12 is a flow diagram specifically showing an example of a part (ST2000) of the operations executed by the decision system 1 shown in FIG. [Figure 13] FIG. 13 is a diagram showing the application location of the pesticide composition by drone spraying. [Figure 14]FIG. 14 is a graph showing the relationship between the amount of anionic dispersant added and heat generation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] The following section <1> In this section, we will mainly explain the system for mixing the pesticide composition to prepare the drug, and in Section <2> and <3> Next, an explanation will be given of an agricultural chemical composition that can be prepared by this system, etc., and a method for controlling diseases, pests and / or weeds in which this system, etc. can be used.

[0023] <1> Decision-making system for determining a plurality of pesticide compositions to be mixed Various embodiments of the present invention will be described below with reference to the accompanying drawings. Note that common components in the drawings are given the same reference numerals. Also, please note that components shown in one drawing may be omitted in another drawing for convenience of explanation. Furthermore, please note that the attached drawings are not necessarily drawn to scale.

[0024] The various systems, methods, and devices described herein should not be construed as being limited in any way. Indeed, the present disclosure is directed to all novel features and aspects of each of the various disclosed embodiments, combinations of these various embodiments with each other, and combinations of parts of these various embodiments with each other. The various systems, methods, and devices described herein are not limited to a specific aspect, a specific feature, or a combination of such a specific aspect and a specific feature, and the products and methods described herein do not require that one or more specific advantages exist or problems be solved. Moreover, various features or aspects of the various embodiments described herein, or parts of such features or aspects, may be used in combination with each other.

[0025] Although operations of some of the various methods disclosed herein have been described in a particular order for convenience, it should be understood that description in such a manner encompasses rearranging the order of the operations, unless a particular order is required by specific text below. For example, operations described in a sequence may in some cases be rearranged or performed simultaneously. Moreover, for purposes of simplicity, the accompanying drawings do not show various ways in which the various items and methods described herein may be used in conjunction with other items and methods.

[0026] Any theories of operation, scientific principles or other theoretical descriptions presented herein in connection with the devices or methods of the present disclosure are provided for the purpose of better understanding and are not intended to limit the scope of the technology, and the devices and methods in the appended claims are not limited to devices and methods that operate in a manner described by such theories of operation.

[0027] Any of the various methods disclosed herein may be implemented using computer-executable instructions stored in one or more computer-readable media and executed in a computer. The one or more media may be non-transitory computer-readable storage media, such as at least one optical media disk, volatile memory components, or non-volatile memory components, where the volatile memory components include, for example, DRAM or SRAM. Also, the non-volatile memory components include, for example, hard drives and solid-state drives (SSD). Furthermore, the computer may include any computer available in the market, including, for example, smartphones and other mobile devices having hardware to perform calculations.

[0028] Any such computer-executable instructions for implementing the techniques disclosed herein, together with any data generated and used during the implementation of the various embodiments disclosed herein, may be stored in one or more computer-readable media (e.g., non-transitory computer-readable storage media). Such computer-executable instructions may be, for example, part of a separate software application, or part of a software application accessed or downloaded via a web browser or other software application (such as a remote computing application). Such software may be executed, for example, on a single local computer (e.g., as a process executed on any suitable computer available on the market) or in a network environment (e.g., the Internet, a wide area network, a local area network, a client-server network (such as a cloud computing network), or other such network) using one or more networked computers.

[0029] For clarity, only certain selected aspects of various software-based implementations are described. Other details that are well known in the art are omitted. For example, the techniques disclosed herein are not limited to a specific computer language or program. For example, the techniques disclosed herein may be implemented by software written in C, C++, Java, or any other suitable programming language. Similarly, the techniques disclosed herein are not limited to a specific computer or a specific type of hardware. Specific details of suitable computers and hardware are well known and need not be described in detail herein.

[0030] Moreover, any of the various such software-based embodiments (e.g., including computer-executable instructions for causing a computer to perform any of the various methods disclosed herein) may be uploaded, downloaded, or accessed in a remote manner by suitable communications means including, for example, the Internet, the World Wide Web, an intranet, a software application, cable (including fiber optic cable), magnetic communication, electromagnetic communication (including RF, microwave, infrared), electronic communication, or other such communications means.

[0031] 1. Overview of the decision system Fig. 1 is a block diagram showing an example of the overall configuration of a determination system according to an embodiment. As shown in Fig. 1, the determination system 1 can include, for example, at least one server device 10, at least one mobile spraying device 20, and at least one terminal device 30. These devices 10, 20, and 30 can be connected to each other via a network 2.

[0032] In addition, in Fig. 1, as an example of at least one server device 10, only one server device 10 is shown, but it is also possible to use multiple server devices 10. In addition, in Fig. 1, as an example of at least one mobile spraying device 20, only one mobile spraying device 20 is shown, but it is also possible to use multiple mobile spraying devices 20. Similarly, in Fig. 1, as an example of at least one terminal device 30, only one terminal device 30 is shown, but it is also possible to use multiple terminal devices 30.

[0033] Network 2 may include, but is not limited to, a cellular network, a wireless network, a landline network, the Internet, an intranet, a local area network (LAN), a wide area network (WAN), and / or an Ethernet network. The wireless network may include, but is not limited to, Bluetooth, WiFi (such as IEEE 802.11a / b / n), WiMax, cellular, satellite, laser, infrared, or RF connections.

[0034] The server device 10 is a device equipped with an information processing device (not shown). Such a server device 10 may be, for example, a web server, a cloud server, a personal computer, a workstation, or a supercomputer.

[0035] The mobile spraying device 20 is equipped with an information processing device (not shown) and is controlled by the information processing device to spray the agent. Such a mobile spraying device 20 may be a traveling type spraying device (e.g., a spraying device that travels on the ground or water surface) or a flying type spraying device (e.g., a drone type spraying device).

[0036] The terminal device 30 is a device equipped with an information processing device (not shown). Such a terminal device 30 may be a smartphone, a feature phone, a mobile phone, a portable information terminal, a personal computer, a tablet, or the like.

[0037] 1, in brief, the server device 10 can receive and acquire type information indicating the type of at least one damage that has occurred in a specific area (e.g., one area in a farm field) included in a certain farm field (target area) from various devices. Such various devices may include the mobile spraying device 20, the terminal device 30, and / or other devices (not shown) that can be connected to the network 2.

[0038] Furthermore, the server device 10 can determine the multiple pesticide compositions constituting the agent to be sprayed in the specific area and the amount of each of the multiple pesticide compositions based on the type information and amount information received in this way. Such a determination can be executed by an information processing device (not shown) mounted on the server device 10. Note that such a determination can also be executed by an information processing device (not shown) mounted on the mobile spraying device 20, an information processing device (not shown) mounted on the terminal device 30, and / or an information processing device (not shown) mounted on other devices that can be connected to the network 2, instead of or in addition to the information processing device mounted on the server device 10. To achieve this, the device that makes the determination can receive type information and amount information from other devices (10, 20, 30, etc.) via the network 2. Alternatively, the device that makes the determination can also receive type information and amount information via a user interface or the like provided on this device.

[0039] Furthermore, as an option, the mobile spraying device 20 can prepare a drug by mixing the amounts determined as above for each of the plurality of pesticide compositions determined as above. Alternatively, the mobile spraying device 20 can obtain the prepared drug itself from a preparation device (not shown) that can similarly perform such preparation. In this way, the mobile spraying device 20 can move to the specific area (a certain area in the field) in the target area (field, etc.) and spray the drug on the rice plants present in this specific area.

[0040] 2. Configuration of the information processing device installed in the server device 10, etc. The server device 10, the mobile spraying device 20, and the terminal device 30 can be equipped with an information processing device 10A. Fig. 2 is a block diagram showing an example of the hardware configuration of the information processing device 10A equipped in the server device 10, the mobile spraying device 20, and the terminal device 30 in a determination system according to an embodiment.

[0041] 2, the information processing device 10A can mainly include a central processing unit 11, a main memory device 12, an input / output interface device 13, an input device 14, an auxiliary memory device 15, and an output device 16. These devices are connected to each other by a data bus and / or a control bus.

[0042] The central processing unit 11 is a single processor referred to as a "CPU (Central Processing Unit)." The central processing unit 11 can perform calculations on instructions and data stored in the main memory device 12, and store the results of the calculations in the main memory device 12. Furthermore, the central processing unit 11 can control an input device 14, an auxiliary memory device 15, an output device 16, and the like, via an input / output interface device 13. The information processing device 10A can include one or more such central processing units 11.

[0043] The main memory device 12 is referred to as a "memory" and can store instructions and data received from the input device 14, the auxiliary memory device 15, and the network 400 via the input / output interface device 13, as well as the results of calculations by the central processing unit 11. The main memory device 12 can include, but is not limited to, a random access memory (RAM), a read only memory (ROM), and / or a flash memory.

[0044] The auxiliary storage device 15 is a storage device having a larger capacity than the main storage device 12. The auxiliary storage device can store instructions and data (computer programs) constituting an operating system and a specific application, etc. This specific application can be executed by the information processing device 10A mounted on the server device 10 (or the mobile spraying device 20, the terminal device 30), thereby making the information processing device 10 function as a whole as the server device 10 (or the mobile spraying device 20, the terminal device 30). Furthermore, the auxiliary storage device 15 can transmit these instructions and data (computer programs) to the main storage device 12 via the input / output interface device 13 by being controlled by the central processing device 11. The auxiliary storage device 15 can include, without being limited to, a magnetic disk device and / or an optical disk device, etc.

[0045] The input device 14 is a device that inputs data from the outside, and may include, but is not limited to, at least one sensor, a touch panel, a button, a keyboard, and / or a mouse, etc. The at least one sensor may include, for example, an RGB camera, a multispectral camera, a hyperspectral camera, and / or a high wavelength spectrum camera, etc.

[0046] The output device 16 may include, but is not limited to, a display device, a touch panel, and / or a printer device.

[0047] In such a hardware configuration, the central processing unit 11 can sequentially load instructions and data (computer programs) constituting a specific application stored in the auxiliary storage device 15 into the main storage device 12 and perform operations on the loaded instructions and data. This allows the central processing unit 11 to control the input device 14 and / or the output device 16 via the input / output interface device 13, or to transmit and receive various information to and from other devices (e.g., the server device 10, the mobile spraying device 20, and / or the terminal device 30, etc.) via the input / output interface device 13 and the network 2.

[0048] Note that the information processing device 10A may include, instead of the central processing unit 11 or in addition to the central processing unit 11, one or more microprocessors and / or a graphics processing unit (GPU) or the like.

[0049] 3. Operation of the decision system (part 1) Next, a specific example of the operation executed by the determination system 1 having the above configuration will be described with reference to Fig. 3. Fig. 3 is a flow diagram showing an example of the operation executed by the determination system 1 shown in Fig. 1.

[0050] As shown in Fig. 3, the operation performed by the determination system 1 can broadly include a step (hereinafter referred to as "ST") 100 of acquiring information about a target area (or a specific area included in the target area), and a step ST200 of determining a plurality of pesticide compositions constituting a drug and the amount of each pesticide composition based on the information acquired in ST100. Furthermore, as an option, the operation performed by the determination system 1 can include a step ST300 of spraying a drug prepared by mixing a determined amount of each of the plurality of pesticide compositions determined in ST200 to the target area (or a specific area included in the target area). Specific examples of ST100, ST200, and ST300 will be described below in turn.

[0051] 3-1. Operations performed in ST100 FIG. 4 is a flow diagram specifically illustrating an example of a part (ST100) of the operations executed by the decision system 1 shown in FIG. Here, the target area refers to the area where rice is cultivated (including, for example, an area where seedlings are transplanted and / or an area where seed rice is directly sown), and may be one field, a part of one field, and / or multiple fields. In addition, damage may include, but is not limited to, diseases that have occurred in the target area (or rice grown in the target area), pests that have occurred in the target area (or rice grown in the target area), and / or weeds that have occurred in the target area.

[0052] First, in ST102, the information processing device 10A can acquire an image of the target area. As an example, such an image may be an image acquired by the mobile spraying device 20 or the like capturing an image of the target area from above the target area (such an image may be transmitted to the information processing device 10A via the network 2). As another example, such an image may be an image acquired by a user located in a building, helicopter, airplane, or the like capturing an image of the target area using a camera that is an input device 14 such as a terminal device 30 (such an image may be transmitted to the information processing device 10A via the network 2). As yet another example, such an image may be an image acquired by the information processing device 10A via the network 2 from some device (such as a server device that provides images captured by aerial photographs of the entire country).

[0053] In ST104, the information processing device 10A inputs the image acquired in ST102 into a trained learning model, thereby being able to identify which pixels contained in the image have any harm occurring or have no harm occurring.

[0054] Such a trained learning model may be a learning model capable of executing machine learning (particularly deep learning) including an input layer, an output layer, and a plurality of intermediate layers arranged between the input layer and the output layer. Each of such learning models has trained in advance using a large number of (e.g., tens of thousands or more) teacher data, including an image (input data) of rice plants suffering from a certain damage, information (output data) indicating which pixel (one or more pixels) in the image the damage is occurring in. Such learning models can perform training by optimizing a large number of coefficients used in the learning model so as to reduce the error between the data output when input data in each teacher data is input and the output data in the teacher data.

[0055] In a first example, the information processing device 10A can have such a trained learning model. In a second example, the information processing device 10A can receive (acquire) such a trained learning model stored in a device other than the device in which the information processing device 10A is installed, via the network 2. In a third example, the information processing device 10A can receive (acquire) output data of the trained learning model by transmitting an image to the trained learning model stored in a device other than the device in which the information processing device 10A is installed, via the network 2.

[0056] By inputting an image to such a trained learning model, the information processing device 10A can acquire information indicating which damage occurs in which pixel in the image from the learning model.

[0057] Fig. 5 is a schematic diagram conceptually illustrating an example of information generated by the information processing device 10A using a learning model in the determination system illustrated in Fig. 1. For the sake of simplicity of explanation, Fig. 5 illustrates an example of information generated for each of a very small number of unit areas in a target area by inputting an image of the target area into the learning model.

[0058] 5 shows only six unit areas 120A to 120I as an example. Each unit area may be represented by any number of pixels, but in this example, it may be represented by 25 pixels (5 pixels x 5 pixels). In this example, each unit area is a square, but in another example, it may be any shape, such as a polygon, a circle, an ellipse, a trapezoid, or a diamond.

[0059] For each unit area, each pixel may be associated with information specific to the damage that occurred at the position corresponding to the pixel. For example, when focusing on unit area 120A, the position corresponding to pixel 120A1 is associated with information "1" specific to the first damage that occurred at this position. Here, as an example, each position corresponding to a total of 10 pixels may be associated with information "1" specific to the first damage that occurred at this position. The first damage is any one of a disease that occurred in the target area (or rice grown in the target area), a pest that occurred in the target area (or rice grown in the target area), and a weed that occurred in the target area.

[0060] In addition, the location corresponding to pixel 120A2 may be associated, in one example, with "null" information specific to the event that no harm has occurred at this location, while in another example, it may be associated with "0" or the like information specific to the event that no harm has occurred at this location.

[0061] Further, for example, when focusing on unit area 120G, the position corresponding to pixel 120G1 is associated with information "4" specific to the fourth damage that has occurred at this position. Here, as an example, each position corresponding to a total of five pixels may be associated with information "4" specific to the fourth damage that has occurred at this position. Note that the fourth damage is any one of a disease that has occurred in the target area (or rice grown in the target area), a pest that has occurred in the target area (or rice grown in the target area), and a weed that has occurred in the target area.

[0062] Furthermore, the position corresponding to pixel 120G2 is associated with information "5" specific to the fifth damage that has occurred at this position. Here, as an example, the information "5" specific to the fifth damage that has occurred at this position may be associated with each of the positions corresponding to a total of three pixels. The fourth damage is any one of a disease that has occurred in the target area (or the rice grown in the target area), a pest that has occurred in the target area (or the rice grown in the target area), and a weed that has occurred in the target area.

[0063] Furthermore, the position corresponding to pixel 120G3 is associated with information "6" specific to the sixth damage that has occurred at this position. Here, as an example, the information "6" specific to the sixth damage that has occurred at this position may be associated with each position corresponding to a total of two pixels. The sixth damage is any one of a disease that has occurred in the target area (or paddy rice grown in the target area), a pest that has occurred in the target area (or paddy rice grown in the target area), and a weed that has occurred in the target area.

[0064] Furthermore, the location corresponding to pixel 120G4 may, in one example, be associated with "null" information specific to the event that no harm has occurred at this location, while in another example, it may be associated with "0" or the like information specific to the event that no harm has occurred at this location.

[0065] The other unit areas will not be described in detail, but the concept is similar to that described for unit areas 120A and 120G.

[0066] Returning to FIG. 4, in ST106, the information processing device 10A can use the information output from the learning model in ST104 (e.g., the information exemplified in FIG. 5) to acquire, for each of a plurality of unit areas included in the target area, "unit type information" indicating at least one type of harm that has occurred in this unit area, and store this information (e.g., in the auxiliary storage device 15).

[0067] 4, the information processing device 10A can acquire, for the unit area 120A, information "1" indicating the type of damage that occurred in this unit area 120A (information "1" corresponding to the first damage), as unit type information. The information processing device 10A can also acquire, for each of the unit areas 120B and 120C, information "1" indicating the type of damage that occurred in this unit area, as unit type information.

[0068] Furthermore, for the unit area 120D, the information processing device 10A can obtain, as unit type information, information "2" and information "3" (information "2" corresponding to the second damage and information "3" corresponding to the third damage) indicating the type of damage that occurred in this unit area 120D. For each of the unit areas 120E and 120F, the information processing device 10A can obtain, as unit type information, information "2" and information "3" indicating the type of damage that occurred in this unit area.

[0069] Furthermore, for the unit region 120G, the information processing device 10A can acquire information "4", information "5", and information "6" (information "4" corresponding to the fourth damage, information "5" corresponding to the fifth damage, and information "6" corresponding to the sixth damage) indicating the type of damage that occurred in this unit region 120G as unit type information. For the unit region 120H, the information processing device 10A can acquire information "4", information "5", and information "6" indicating the type of damage that occurred in this unit region 120H as unit type information.

[0070] Furthermore, for the unit region 120I, the information processing device 10A can acquire, as unit type information, information "null" (or "0" or the like) indicating an event in which no harm has occurred in this unit region 120I.

[0071] In one example, the information processing device 10A is capable of acquiring unit type information for all unit areas included in the target area, while in another example, it is also capable of acquiring unit type information for each of some of the unit areas (one or more unit areas) included in the target area.

[0072] Furthermore, in another example, the information processing device 10A can also acquire "type information" indicating at least one type of damage that occurred in a specific area included in the target area. For example, when the target area is a farm field and the specific area is a part of the farm field, the information processing device 10A can also acquire type information indicating at least one type of damage that occurred in the part of the area. In this case, the information processing device 10A can acquire the type information by searching for information associated with each pixel that constitutes the part of the area in the information acquired from the learning model in ST104. For example, when the specific area is an area constituted by the unit area 120D and the unit area 120G, the information processing device 10A can acquire information "2", information "3", information "4", information "5", and information "6" indicating the type of damage that occurred in these unit areas as type information.

[0073] Note that the information identifying the target region and the information identifying a specific region in this target region can be input by the user via the input device 14 of the information processing device 10A and acquired by the information processing device 10A, for example.

[0074] Returning to Fig. 4, next, in ST108, the information processing device 10A can determine, for each of a plurality of unit areas included in the target area, the amount of at least one harm that has occurred in this unit area, using the information output from the learning model in ST104 (for example, the information exemplified in Fig. 5). This allows the information processing device 10A to acquire, for each of a plurality of unit areas included in the target area, unit amount information indicating the amount of at least one harm that has occurred in this unit area, and store it (for example, in the auxiliary storage device 15).

[0075] 4, the information processing device 10A can acquire, for the unit region 120A, information "1-10" indicating that there are 10 pieces of information "1" indicating the type of harm that has occurred in this unit region 120A (information indicating that there are "10" pieces of information "1" corresponding to the first harm / information indicating that the density of information "1" is "10") as unit amount information. The information processing device 10A can also acquire, for each of the unit regions 120B and 120C, information "1-10" indicating that there are 10 pieces of information "1" indicating the type of harm that has occurred in this unit region 120, as unit amount information.

[0076] The information processing device 10A can obtain, as unit amount information, information "2-4" indicating that there are four pieces of information "2" indicating the type of damage that has occurred in the unit area 120D (information indicating that there are "4" pieces of information "2" corresponding to the second damage / information indicating that the density of information "2" is "4") and information "3-6" indicating that there are six pieces of information "3" indicating the type of damage that has occurred in the unit area 120D (information indicating that there are "6" pieces of information "3" corresponding to the third damage / information indicating that the density of information "3" is "6"). The information processing device 10A can also obtain, as unit amount information, information "2-4" indicating that there are four pieces of information "2" indicating the type of damage that has occurred in the unit area and information "3-6" indicating that there are six pieces of information "3" indicating the type of damage that has occurred in the unit area 120D for each of the unit areas 120E and 120F.

[0077] Furthermore, the information processing device 10A can obtain, as unit amount information, information "4-5" indicating that there are five pieces of information "4" indicating the type of damage that has occurred in the unit area 120G, information "5-3" indicating that there are three pieces of information "5" indicating the type of damage that has occurred in the unit area 120G, and information "6-2" indicating that there are two pieces of information "6" indicating the type of damage that has occurred in the unit area 120G. The information processing device 10A can also obtain, as unit amount information, information "4-5" indicating that there are five pieces of information "4" indicating the type of damage that has occurred in the unit area 120H, information "5-3" indicating that there are three pieces of information "5" indicating the type of damage that has occurred in the unit area 120H, and information "6-2" indicating that there are two pieces of information "6" indicating the type of damage that has occurred in the unit area 120H.

[0078] In one example, the information processing device 10A is capable of acquiring unit quantity class information for all unit areas included in the target area, while in another example, it is also capable of acquiring unit quantity information for each of some of the unit areas (one or more unit areas) included in the target area.

[0079] Furthermore, in another example, the information processing device 10A can also acquire "quantity information" indicating the amount of at least one damage that has occurred in a specific area included in the target area. For example, when the target area is a field and the specific area is a part of the field, the information processing device 10A can also acquire quantity information indicating the amount of at least one damage that has occurred in the part of the area. In this case, the information processing device 10A can acquire the above type information by searching for information associated with each pixel that constitutes the part of the area in the information acquired from the learning model in ST104. For example, when the specific area is an area constituted by the unit area 120D and the unit area 120G, the information processing device 10A can acquire the following information as quantity information. -Information "2-4" indicating that there are four pieces of information "2" indicating the type of damage that occurred in these unit areas -Information "3-6" indicating that there are six pieces of information "3" indicating the type of damage that occurred in these unit areas -Information "4-5" indicating that there are five pieces of information "4" indicating the type of damage that occurred in these unit areas -Information "5-3" indicating that there are three pieces of information "5" indicating the type of damage that occurred in these unit areas -Information "6-2" indicating that there are two pieces of information "6" indicating the type of damage that occurred in these unit areas

[0080] Returning to FIG. 4 again, so far, an example has been described in which information processing device 10A, as one embodiment, acquires unit type information (or type information) in ST104 and ST106 via ST102, and acquires unit amount information (or amount information) in ST108. However, in another embodiment, information processing device 10A can acquire unit type information (or type information) in ST106, and acquire unit amount information (or amount information) in ST108, without going through ST102 and ST104. Specifically, for example, information processing device 10A can acquire unit type information (or type information) from some device via network 2 in ST106, and acquire unit amount information (or amount information) from some device via network 2 in ST108. In this case, the certain device may be another information processing device capable of acquiring (generating) this information by executing the processes shown in ST102 to ST108, or may be another information processing device capable of acquiring (generating) this information by executing any other process different from the processes shown in ST102 to ST108.

[0081] Next, optionally, in ST110, information processing device 10A can determine at least one zone from within the target region using the unit type information and unit amount information (or type information and amount information). Here, a zone can be said to be a number of unit areas that are common or substantially common in the type of damage that has occurred and the amount of damage that has occurred.

[0082] A method for determining a zone executed by the information processing device 10A will be described with reference to Fig. 6. Fig. 6 is a schematic diagram showing an example of a method for determining a zone executed by the information processing device 10A in the determination system shown in Fig. 1. Fig. 6 is expressed corresponding to Fig. 5 referred to above.

[0083] First, attention is paid to the unit area 120A. In the unit area 120A, there are 10 pieces of information "1". That is, the unit type information related to the unit area 120A has information "1", and the unit amount information related to the unit area 120A has information "1-10". The information processing device 10A can identify that the unit type information related to each of the unit areas 120B and 120C both have only information "1", and the unit amount information related to each of the unit areas 120B and 120C both have only information "1-10" by searching for unit type information and unit amount information related to other unit areas. That is, the information processing device 10A can identify that the unit areas 120A, 120B, and 120C are common to each other in terms of both the type of harm caused and the amount of harm caused. As a result, the information processing device 10A can determine an area including the entire unit areas 120A, 120B, and 120C as the first zone 130.

[0084] Next, attention is paid to the unit area 120D. In the unit area 120D, there are four pieces of information "2" and six pieces of information "3". That is, the unit type information regarding the unit area 120D has information "2" and information "3", and the unit amount information regarding the unit area 120D has information "2-4" and information "3-6". By searching for unit type information and unit amount information regarding other unit areas, the information processing device 10A can identify that the unit type information regarding each of the unit areas 120E and 120F both has only information "2" and information "3", and the unit amount information regarding each of the unit areas 120E and 120F both has only information "2-4" and information "3-6". That is, the information processing device 10A can identify that the unit areas 120D, 120E, and 120F are common to each other in terms of both the type of harm caused and the amount of harm caused. As a result, the information processing device 10A can determine, as the second zone 131, an area including the entire unit areas 120D, 120E, and 120F.

[0085] Next, attention is paid to the unit area 120G. In the unit area 120G, there are five pieces of information "4", three pieces of information "5", and two pieces of information "6". That is, the unit type information regarding the unit area 120G has information "4", information "5", and information "6", and the unit amount information regarding the unit area 120G has information "4-5", information "5-3", and information "6-2". By searching for unit type information and unit amount information regarding other unit areas, the information processing device 10A can identify that the unit type information regarding the unit area 120H has only information "4", information "5", and information "6", and the unit amount information regarding the unit area 120H has only information "4-5", information "5-3", and information "6-2". That is, the information processing device 10A can identify that the unit areas 120G and 120H are common to each other in terms of both the type of harm caused and the amount of harm caused. As a result, the information processing device 10A can determine, as the third zone 132, an area including the entire unit areas 120G and 120H.

[0086] 6, the information processing device 10A can identify multiple areas that are "common" in the type of harm that occurred and the amount of harm that occurred, and determine an area that includes all of the multiple areas thus identified as one zone. However, in another embodiment, the information processing device 10A can also identify multiple areas that are "substantially common" in the type of harm that occurred and the amount of harm that occurred, and determine an area that includes all of the multiple areas thus identified as one zone.

[0087] Here, "substantially common" does not only mean that the type of harm caused in a certain unit area and the type of harm caused in another unit area are completely identical, but also that the type of harm caused in the certain unit area and the type of harm caused in the other unit area are similar (the two types can be considered to be substantially the same).

[0088] In addition, "substantially common" may include not only the case where the amount (density) of harm occurring in one unit area and the amount (density) of harm occurring in another unit area are completely identical, but also the case where the error between the amount (density) of harm occurring in the one unit area and the amount (density) of harm occurring in another unit area is below any specified value (the two amounts can be considered to be substantially the same).

[0089] Returning to FIG. 4, next, in ST112, information processing device 10A can generate and store (in auxiliary storage device 15, for example) zone information indicating each zone (one or more zones) determined in ST110.

[0090] Specifically, in the example shown in Fig. 6, the information processing device 10A can generate and store zone information for the first zone 130 indicating that the first zone 130 is an area including unit areas 120A, 120B, and 130C. The information processing device 10A can also generate and store zone information for the second zone 131 indicating that the second zone 131 is an area including unit areas 120D, 120E, and 130F. Similarly, the information processing device 10A can generate and store zone information for the third zone 132 indicating that the third zone 132 is an area including unit areas 120G and 120H.

[0091] As described below, by using such zone information, the mobile spraying device 20 can spray a pesticide into a zone that is a mixture of multiple pesticide compositions determined for that zone, in the amounts of each pesticide composition determined for that zone.

[0092] Note that the information processing device 10A can also execute the processes in ST110 and ST112 (FIG. 4) described above in ST200 (FIG. 3).

[0093] 3-2. Operations performed in ST200 Fig. 7 is a flow diagram specifically showing an example of a part (ST200) of the operations executed by the determination system 1 shown in Fig. 3. The information processing device 10A can execute the operations shown in Fig. 7 for each specific area included in the target area or each zone included in the target area. Of course, the information processing device 10A can also execute the operations shown in Fig. 7 for any one specific area included in the target area or any one zone included in the target area.

[0094] First, in ST202, the information processing device 10A can acquire type information indicating at least one damage that has occurred in a specific area (for example, one farm field, or a part of an area included in one farm field, etc.) and amount information indicating the amount of each of the at least one damage that has occurred in the specific area. Such information has already been acquired and written by the information processing device 10A in ST106 and ST108.

[0095] Here, when the zone determined in ST110 (see FIG. 4) is used as the specific area, the information processing device 10A can acquire zone information stored in ST112 (see FIG. 4) corresponding to that zone, and acquire unit type information and unit amount information corresponding to each unit area identified by this zone information.

[0096] Next, in ST204, the information processing device 10A can acquire various pieces of information to be input to the trained first learning model or the trained first decision tree model. Specifically, the information processing device 10A can acquire the following information (1) to (5).

[0097] (1) Soil information on the type of soil in a specific area (zone) (2) Information on past harms that have occurred in a specific area (zone) (3) Seed burial information on seeds buried in the soil of a specific area (zone) (4) Information on the prevalence of pests resistant to drugs in specific areas (zones) (5) Weather forecast information regarding weather data predicted for the rice cultivation period in a specific area (zone).

[0098] The information (1), i.e., soil information, may be, for example, information indicating (identifying) the type of soil (sand, clay, etc.) in a particular area (zone). This information (1) may be used in consideration of the possibility that the type of soil in a particular area (zone) may affect damage that may occur in the soil, the speed and timing of development of this damage, etc.

[0099] Information (2), i.e., harm occurrence information, may be information that indicates (identifies) harm that has occurred in the past in a specific area (zone). This information (2) may be used in consideration that harm that has already occurred in the past in a specific area (zone) is likely to occur in the same specific area (zone) in the present.

[0100] The information (3), i.e., seed burial information, may be information that indicates (identifies) seeds (weed seeds) buried in a specific area (zone). When a certain seed is buried in a specific area (zone), this information (3) may be used, taking into consideration the high possibility that a weed corresponding to that seed is still occurring in the same specific area (zone).

[0101] The information (4), i.e., the pest prevalence information, may be information indicating (identifying) which pests having resistance to agents (fungicides, insecticides and / or herbicides) are currently prevalent in a specific area (zone). When a pest having a certain resistance is currently prevalent in an area including a specific area (zone), this information (5) may be used, taking into account the high possibility that such a pest having resistance is still occurring in the same specific area (zone).

[0102] The information (5), i.e., weather forecast information, may be information indicating (identifying) weather data (temperature, humidity, weather, and / or precipitation, etc.) predicted for the period during which rice is cultivated in a specific area (zone). This weather data for the period during which rice is cultivated in a specific area (zone) may be used in consideration of the possibility that such weather data may affect damage occurring in the same specific area (zone), the speed and timing of the development of such damage, etc.

[0103] The above information may be information that the user inputs using the input device 14 (keyboard, mouse, touch panel, etc.) of the information processing device 10A by utilizing a menu screen displayed on the output device (display, etc.) 16 of the information processing device 10A.

[0104] Next, in ST206, the information processing device 10A can acquire the following information (6) to information (8) by inputting the information (type information and quantity information) acquired in ST202 and at least one of the information acquired in ST204 (i.e., at least one of information (1) to information (5)) into a trained first learning model or a trained first decision tree model.

[0105] (6) Information on the type and amount of damage that may occur in a specific area (zone) (7) Information on the forecast of the growth period of this pest (8) Information on pesticide damage occurring in a specific area (zone)

[0106] Information (6), i.e., harm occurrence prediction information, may literally be information that indicates (identifies) the type of harm that will occur in a specific area (zone) and the amount of this harm.

[0107] Information (7), i.e., pest growth prediction information, may be information that indicates (identifies) the timing (how early, what period) of the occurrence of the pest indicated in information (6).

[0108] The information (8), i.e., drug damage information, may be information that indicates (identifies) what kind of drug damage (side effects, etc.) will occur in a specific area (zone).

[0109] The first learning model (such as a machine learning / deep learning model having an input layer, multiple intermediate layers, and an output layer) and the first decision tree model each perform learning in advance using a large number (e.g., tens of thousands or more) of teacher data including type information, quantity information, and information (1) to (5) (input data) and information (6) to (8) (output data). Such a first learning model or first decision tree model can perform learning by optimizing a large number of coefficients (or a large number of parameters) used in the first learning model or first decision tree model so as to reduce the error between data output when input data in each teacher data is input and the output data in the teacher data.

[0110] In a first example, the information processing device 10A can have such a trained first learning model and trained first decision tree model. In a second example, the information processing device 10A can receive (acquire) such a trained first learning model and trained first decision tree model stored in a device other than the device in which the information processing device 10A is installed, via the network 2. In a third example, the information processing device 10A can receive (acquire) information (6) to information (8), which are output data of the first learning model or the first decision tree model, by transmitting necessary information (for example, at least one of information (1) to information (5)) via the network 2 to the trained first learning model or the trained first decision tree model stored in a device other than the device in which the information processing device 10A is installed.

[0111] Next, in ST208, the information processing device 10A inputs the information (6) to information (8) acquired in ST206 into a trained second learning model or a trained second decision tree model, thereby acquiring and storing (for example, in the auxiliary storage device 15) the information (9) and information (10) shown below.

[0112] (9) Information indicating (identifying) multiple pesticide compositions constituting the agent to be sprayed in a specific area (zone) (10) Information indicating (identifying) the amount of each of the multiple pesticide compositions shown in information (9)

[0113] In addition, as an option, the information processing device 10A can input the information (6) to information (8) acquired in ST206 into a trained second learning model or a trained second decision tree model, and thereby acquire and store (for example, in the auxiliary storage device 15) at least one of the information (11) and information (12) shown below, in addition to the information (9) and information (10).

[0114] (11) Information on optimal continuous processing regarding the optimal method for continuously spraying (processing) the agent mixed according to information (9) and information (10) (12) Information on the optimal time for spraying (treating) the agent mixed in accordance with information (9) and information (10).

[0115] Information (11), i.e., optimal continuous treatment information, may be information indicating (identifying) the optimal method for spraying (treating) the drug mixed in accordance with information (9) and information (10) (e.g., a method of spraying the drug continuously every two weeks, etc.).

[0116] Information (12), i.e., optimal treatment time information, may be information indicating (identifying) the optimal time (e.g., March, etc.) to spray (treat) the pesticide mixed in accordance with information (9) and information (10).

[0117] Such second learning model and second decision tree model each perform learning in advance using a large number (e.g., tens of thousands or more) of teacher data including information (6) to (8) (input data) and information (9) to (12) (output data). Such second learning model or second decision tree model can perform learning by optimizing a large number of coefficients (or a large number of parameters) used in the second learning model or second decision tree model so as to reduce the error between data output when input data in each teacher data is input and the output data in that teacher data.

[0118] In a first example, the information processing device 10A can have such a trained second learning model and trained second decision tree model. In a second example, the information processing device 10A can receive (acquire) such a trained second learning model and trained second decision tree model stored in a device other than the device in which the information processing device 10A is installed, via the network 2. In a third example, the information processing device 10A can receive (acquire) information (9) to information (12), which are output data of the second learning model or the second decision tree model, by transmitting necessary information (for example, information (6) to information (8)) via the network 2 to the trained second learning model or the trained second decision tree model stored in a device other than the device in which the information processing device 10A is installed.

[0119] In ST208, information processing device 10A can also display the acquired information (9) and information (10) (and further information (11) and / or information (12)) to the user via output device 16.

[0120] Next, in ST210 as an option, the information processing device 10A can acquire various pieces of information to be input to the trained third learning model or the trained third decision tree model. Specifically, the information processing device 10A can acquire information (13) and "at least one" piece of information (14) to (16) shown below.

[0121] (13) Information on the general effect of the drug on the harms indicated in the above information (6) (Harm Prediction Information) (14) Information on the types and effects of drugs previously used in a specific area (zone) (15) Information regarding the composition of soil in a particular area (zone) (16) Information regarding the moisture retention capacity of specific areas (zones)

[0122] Information (13) may be information indicating (identifying) a commonly known effect of the drug on the harm indicated by information (6).

[0123] The information (14) may be information indicating (identifying) the type of drug that has actually been used in the past in a specific area (zone) and the effect that has actually been obtained in the past for that drug. Even if a certain drug is known to be generally highly effective (or less effective) against a certain harm, there may be cases where the drug was actually used in the past in a specific area (zone) but was not sufficiently effective (or was able to be sufficiently effective). This information (14) may be used in consideration of such cases.

[0124] The information (15) may be information indicative of (identifying) the composition of the soil of a particular area (zone), which may be used in view of the high probability that the composition of the soil of a particular area (zone) may affect the efficacy of the pesticide composition applied to this area of ​​interest.

[0125] The information 16 may be information that identifies the water-holding capacity of a particular area (zone), which may be used in light of the high probability that the water-holding capacity of a particular area (zone) may affect the efficacy of a pesticide composition applied to this target area.

[0126] The above information may be information that the user inputs using the input device 14 (keyboard, mouse, touch panel, etc.) of the information processing device 10A by utilizing a menu screen displayed on the output device (display, etc.) 16 of the information processing device 10A.

[0127] Next, in optional ST212, the information processing device 10A can acquire and store (for example, in the auxiliary storage device 15) the above-mentioned following information (9) and information (10) by inputting the information acquired in ST210, i.e., information (13) and at least one of information (14) to information (16), into a trained third learning model or a trained third decision tree model. In this case, information (13) may be essential information.

[0128] (9) Information indicating multiple pesticide compositions constituting the agent to be sprayed in a specific area (zone) (10) Information indicating the amount of each of the multiple pesticide compositions shown in information (9)

[0129] Also, as an option, the information processing device 10A can input the information acquired in ST210, i.e., information (13) and at least one of information (14) to information (16), into a trained third learning model or a trained third decision tree model, thereby acquiring and storing (for example, in the auxiliary storage device 15) at least one of information (11) and information (12) in addition to the above-mentioned information (9) and information (10). In this case, information (13) may be essential information.

[0130] (9) Information indicating multiple pesticide compositions constituting the agent to be sprayed in a specific area (zone) (10) Information indicating the amount of each of the multiple pesticide compositions shown in information (9) (11) Information on optimal continuous processing regarding the optimal method for continuously spraying (processing) the agent mixed according to information (9) and information (10) (12) Information on the optimal time for spraying (treating) the agent mixed in accordance with information (9) and information (10).

[0131] The third learning model (such as a machine learning / deep learning model having an input layer, multiple intermediate layers, and an output layer) and the third decision tree model each perform learning in advance using information (13) to information (16) (input data) and information (9) to information (12) (output data) and a large number (e.g., tens of thousands or more) of teacher data. Such a third learning model or third decision tree model can perform learning by optimizing a large number of coefficients (or a large number of parameters) used in the third learning model or the third decision tree model so as to reduce the error between data output when input data in each teacher data is input and the output data in the teacher data.

[0132] In a first example, the information processing device 10A can have such a trained third learning model and trained third decision tree model. In a second example, the information processing device 10A can receive (acquire) such a trained third learning model and trained third decision tree model stored in a device other than the device in which the information processing device 10A is installed, via the network 2. In a third example, the information processing device 10A can receive (acquire) information (9) to information (12), which are output data of the third learning model or the third decision tree model, by transmitting necessary information (for example, information (13) to information (16)) via the network 2 to the trained third learning model or the trained third decision tree model stored in a device other than the device in which the information processing device 10A is installed.

[0133] In ST212, information processing device 10A can also display the acquired information (9) and information (10) (and further information (11) and / or information (12)) to the user via output device 16.

[0134] In the above-mentioned ST208, the information processing device 10A can identify the multiple pesticide compositions constituting the agent to be sprayed in the specific area and the amount of each pesticide composition, respectively, by the information (9) and the information (10). At this point, the information processing device 10A can achieve the purpose to some extent. However, by executing the above-mentioned optional ST210 and ST212, the information processing device 10A can input the information (13) and at least one of the information (14) to the information (16) into the trained third learning model or the trained third decision tree model, and thereby obtain the information (9) and the information (10) based on these information. That is, the information processing device 10A can obtain the multiple pesticide compositions constituting the agent to be sprayed in the specific area and the amount of each pesticide composition, based on the information (13) and at least one of the information (14) to the information (16). As a result, the user can recognize two types of information as options, including information (9) and information (10) (and further information (11) and / or information (12)) presented on information processing device 10A in ST208, and information (9) and information (10) (and further information (11) and / or information (12)) presented on information processing device 10A in ST212.

[0135] 3-3. Operations performed in ST300 In this ST300, the mobile spraying device 20 can spray a drug prepared by mixing the pesticide compositions determined in ST200 in determined amounts in a specific area (or zone). Before describing the operations executed in ST300, the hardware configuration of the mobile sprinkling device 20 will be briefly described with reference to FIG.

[0136] Fig. 8 is a block diagram showing an example of the hardware configuration of the mobile spraying device 20 shown in Fig. 1. As shown in Fig. 8, the mobile spraying device 20 includes the above-mentioned information processing device 10A, a driving source 21 and a driving mechanism 22 for moving the mobile spraying device 20, a GPS (Global Positioning System) unit 23 for acquiring position information on the current position of the mobile spraying device 20, a plurality of tanks 24 each containing a unique pesticide composition, a preparation device 25 for mixing any one or more pesticide compositions from the plurality of tanks 24 to prepare a drug, and one or more nozzles 26 for spraying the drug supplied from the preparation device 25.

[0137] The driving source 21 may be an engine and / or a motor capable of generating and providing to the driving mechanism 22 a driving force for moving the mobile sprinkling device 20 under the control of the information processing device 10A.

[0138] The drive mechanism 22 is any mechanism that converts the driving force supplied from the drive source 21 into the propulsion force of the mobile spraying device 20, and may include, but is not limited to, gears, shafts, tires, caterpillars, and / or propellers. The drive mechanism 22 is also any mechanism that controls the direction and / or speed of movement of the mobile spraying device 20 under the control of the information processing device 10A, and may include, but is not limited to, a transmission, a reducer, a steering mechanism, flaps, and / or a rudder.

[0139] The GPS unit 23, under the control of the information processing device 10A, can provide location information regarding the current location of the mobile spraying device 20 to the information processing device 10A by utilizing well-known GPS technology.

[0140] Each of the plurality of tanks 24 may contain a pesticide composition specific to that tank among a plurality of pesticide compositions.

[0141] The preparation device 25 can be connected to each of the multiple tanks 24 via pipes, valves, and sensors that the preparation device 25 has. The preparation device 25 can prepare a pesticide by obtaining and mixing multiple pesticide compositions from any multiple tanks among the multiple tanks 24 under the control of the information processing device 10A.

[0142] Specifically, the preparation device 25 may have a valve that is provided in a pipe connecting the preparation device 25 to each of the multiple tanks 24 and can be opened and closed under the control of the information processing device 10A. The preparation device 25 can open a valve designated by the information processing device 10A and measure the amount of the pesticide composition passing through the valve with a sensor. The preparation device 25 can return the valve to a closed state when the amount of the pesticide composition measured by the sensor reaches the amount designated by the information processing device 10A. In this way, the preparation device 25 can prepare a drug by obtaining and mixing the pesticide composition contained in any of the multiple tanks 24 designated (determined) by the information processing device 10A in the amount designated (determined) by the information processing device 10A.

[0143] Each of the one or more nozzles 26 is coupled to the preparation device 25. Each nozzle 26 is capable of dispersing (spraying) the medicine supplied from the preparation device 25 under the control of the information processing device 10A.

[0144] Next, the operation performed by the mobile spraying device 20 having the above configuration will be described with reference to Fig. 9. Fig. 9 is a flow chart specifically showing an example of a part (ST300) of the operations performed by the determination system 1 shown in Fig. 3.

[0145] First, in ST302, the information processing device 10A can load (acquire) from the auxiliary storage device 15 the multiple pesticide compositions and the amount of each pesticide composition determined in ST200 for the specific area (or each zone).

[0146] Next, in ST304, the information processing device 10A can acquire position information indicating the position of the specific area (or each zone). In one example, this position information can be calculated based on the position information (of the target area) assigned to the image captured in ST102 by the device that generated the image, and the relative positional relationship of the specific area (each zone) with respect to the target area derived from the image. In another example, this position information can be input by the user via the input device 14 of the information processing device 10A at any timing, and acquired by the information processing device 10A.

[0147] Next, in ST306, the information processing device 10A can first use the GPS unit 23 to acquire current position information indicating the current position of the mobile spraying device 20. Furthermore, the information processing device 10A can determine a movement route (information on positions corresponding to multiple points to be passed through in sequence) based on the current position acquired in this way and the position of the specific area (or each zone) acquired in ST304. Such a movement route can be determined, for example, using a technology used in a well-known navigation system. Here, in the case of spraying a drug to multiple zones, the information processing device 10A can determine a movement route so as to pass through all of the multiple unit areas included in each zone. For example, in the example shown in FIG. 6, the movement route can be determined so as to pass through all of the unit areas 120A, 120B, and 120C for the first zone 130, pass through all of the unit areas 120D, 120E, and 120F for the second zone 131, and pass through all of the unit areas 120G and 120H for the third zone 132.

[0148] In ST308, the information processing device 10A can control the drive source 21 and the drive mechanism 22 to start the movement of the mobile spraying device 20. Furthermore, the information processing device 10A can control the drive source 21 and the drive mechanism 22 to move the mobile spraying device 20 along the movement route determined in ST306. This can be realized by controlling the drive source 21 and / or the drive mechanism 22 so that the current position of the mobile spraying device 20 provided by the GPS unit 23 is aligned along the movement route determined in ST306.

[0149] In ST310, the information processing device 10A can control the preparation device 25, for example, to mix a plurality of pesticide compositions determined corresponding to a specific area (or each zone) in a state approaching the specific area (or each zone) in the determined amount of each pesticide composition. This allows the preparation device 25 to perform the above-mentioned operation to mix a plurality of pesticide compositions determined for the specific area (or each zone) in the determined amount of each pesticide composition, thereby preparing a drug corresponding to the specific area (or each zone).

[0150] Furthermore, the information processing device 10A can transmit a signal to the nozzle 26 to instruct the nozzle to be in an open state when the difference between the current position of the mobile spraying device 20 and the position of the specific area (or each zone) becomes equal to or less than a threshold value. This enables the nozzle 26 to spray (spray) the agent supplied from the preparation device 25.

[0151] Furthermore, the information processing device 10A can continue to transmit a signal to the nozzle 26 instructing it to open the nozzle while the difference between the current position of the mobile spraying device 20 and the position of the specific area (or each zone) is equal to or less than a threshold value. This allows the mobile spraying device 20 to spray the agent corresponding to the specific area (or each zone) while it is in a position that coincides with the position of the specific area (or each zone).

[0152] Thereafter, when the difference between the current position of the mobile spraying device 20 and the position of the specific area (or each zone) exceeds a threshold value, the information processing device 10A can transmit a signal to the nozzle 26 instructing the nozzle to be in a closed state, thereby enabling the nozzle 26 to stop spraying the agent.

[0153] In a case where the mobile spraying device 20 sprays the agent in a plurality of specific areas (or a plurality of zones), ST308 and ST310 may be executed sequentially for each specific area (each zone).

[0154] The operations performed by information processing device 10A on a specific area as described above can be applied to operations performed by information processing device 10A on a zone.

[0155] 4. Operation of the decision system (part 2) Up to this point, the embodiment (first embodiment) of determining the amount of each of the multiple agrochemical compositions and the multiple agrochemical compositions to be contained in the agent to be sprayed in a specific area (or zone) included in the target area has been described. However, this first embodiment is also applicable to the embodiment (second embodiment) of determining the amount of each of the multiple agrochemical compositions and the multiple agrochemical compositions to be contained in the agent to be treated (applied) on the seed rice to be directly sown in the specific area (or zone) included in the target area (or the seed rice to be sown in a seedling box to transplant the seedlings in the specific area). In this section 4, the target area refers to the area where the seed rice is directly sown and / or the area where the seedlings grown from the seedling box in which the seed rice is sown are transplanted, and may be one field, a part of one field, and / or multiple fields.

[0156] In the following, in order to avoid redundant description, the second aspect will be briefly described with reference to Fig. 10, focusing only on the parts different from the first aspect described above. Fig. 10 is a flow diagram showing another example of the operation executed by the determination system 1 shown in Fig. 1.

[0157] As shown in Fig. 10, the operation performed by the determination system 1 can include, roughly speaking, ST1000 for acquiring information on the target area (or a specific area included in the target area), and ST2000 for determining, based on the information acquired in ST1000, a plurality of pesticide compositions constituting the agent and the amount of each pesticide composition for the agent to be applied to the seed rice to be directly sown in the target area (or a specific area included in the target area) or the seed rice to be sown in a seedling box for transplanting seedlings in the target area (or the specific area). Furthermore, as an option, the operation performed by the determination system 1 can include ST3000 for applying the agent prepared by mixing the determined amount of each of the plurality of pesticide compositions determined in ST2000 to the seed rice to be directly sown in the target area (or a specific area included in the target area) or the seed rice to be sown in a seedling box for transplanting seedlings in the target area (or the specific area).

[0158] First, attention is focused on ST1000. Fig. 11 is a flow diagram specifically illustrating an example of a part (ST1000) of the operations executed by the determination system 1 shown in Fig. 10. The operation shown in Fig. 11 does not include ST102 and ST104 shown in Fig. 4.

[0159] ST1002 corresponds to ST106 shown in FIG. 4. However, in ST1002, for each of a plurality of unit areas included in the target area, the "unit type information" indicating at least one type of harm that occurred in this unit area may be, for example, past information (such as the previous year) actually collected in relation to the unit area. In one example, such unit type information may be input by a user via the input device 14 of the information processing device 10A and acquired by the information processing device 10A. In another example, such unit type information may be acquired by the information processing device 10A via the network 2 from any other device (such as a server device) that collects and stores such information.

[0160] ST1004 corresponds to ST108 shown in FIG. 4. However, in ST1004, for each of a plurality of unit areas included in the target area, the "unit amount information" indicating the amount of each of at least one harm that occurred in this unit area may be, for example, information actually obtained in the past (the previous year, etc.). In one example, such unit amount information may be input by a user via the input device 14 of the information processing device 10A and acquired by the information processing device 10A. In another example, such unit type information may be acquired by the information processing device 10A via the network 2 from any other device (such as a server device) that collects and stores such information.

[0161] ST1006 and ST1008 correspond to ST110 and ST112 shown in FIG.

[0162] Returning to Fig. 10, attention is now focused on ST 2000. Fig. 12 is a flow chart specifically showing an example of a part (ST2000) of the operations executed by the determination system 1 shown in Fig. 10.

[0163] ST2002 to ST2006 respectively correspond to ST204 to ST208 shown in Fig. 7. Below, only the points in ST2002 to ST2006 that are different from those described above with reference to Fig. 7 will be described.

[0164] In ST2002, the information processing device 10A can acquire the following information (1) to (5) to be input to the trained first learning model or the trained first decision tree model.

[0165] (1) Soil information on the type of soil in a specific area (zone) (2) Information on past harms that have occurred in a specific area (zone) (3) Information on the prevalence of pests resistant to drugs in specific areas (zones) (4) Weather forecast information on weather data predicted for the rice cultivation period in a specific area (zone).

[0166] These pieces of information (1) to (4) correspond to the information (1), (2), (4), and (5) described above in relation to ST204, respectively.

[0167] Next, in ST2004, the information processing device 10A can acquire the following information (5) to information (7) by inputting at least one of the information acquired in ST2002 (i.e., at least one of information (1) to information (4)) into a trained first learning model or a trained first decision tree model.

[0168] (5) Information on the type and amount of damage that may occur in a specific area (zone) (6) Information on the forecast of the growth period of this pest (7) Information on pesticide damage occurring in a specific area (zone)

[0169] The information (5) to (7) correspond to the information (6) to (8) described above in relation to ST206, respectively.

[0170] The first learning model (such as a machine learning / deep learning model having an input layer, multiple intermediate layers, and an output layer) and the first decision tree model each perform learning in advance using a large number (e.g., tens of thousands or more) of teacher data including information (1) to (4) (input data) and information (5) to (7) (output data). Such a first learning model or first decision tree model can perform learning by optimizing a large number of coefficients (or a large number of parameters) used in the first learning model or first decision tree model so as to reduce the error between data output when input data in each teacher data is input and the output data in the teacher data.

[0171] Next, in ST2006, the information processing device 10A inputs the information (5) to information (7) acquired in ST2004 into a trained second learning model or a trained second decision tree model, thereby acquiring and storing (for example, in the auxiliary storage device 15) the information (8) and information (9) shown below.

[0172] (8) Information indicating a plurality of pesticide compositions constituting a drug to be applied to seed rice to be directly sown in a specific area (or information indicating a plurality of pesticide compositions constituting a drug to be applied to seed rice to be sown in a seedling box for transplanting seedlings in a specific area) (9) Information indicating the amount of each of the multiple pesticide compositions shown in information (8)

[0173] Such second learning model and second decision tree model each have been trained in advance using a large number (e.g., tens of thousands or more) of teacher data including information (5) to (7) (input data) and information (8) to (9) (output data). Such second learning model or second decision tree model can perform training by optimizing a large number of coefficients (or a large number of parameters) used in the second learning model or second decision tree model so as to reduce the error between data output when input data in each teacher data is input and the output data in that teacher data.

[0174] Returning to FIG. 10 again, in ST3000, a pesticide can be prepared by mixing a plurality of pesticide compositions determined for a specific area (zone) in ST2000 in the amount of each determined pesticide composition. In one example, the pesticide prepared in this way is applied to rice seeds to be directly sown in the specific area (zone). The rice seeds thus treated are sown in the specific area (zone). In another example, the pesticide prepared in this way is applied to rice seeds, and the rice seeds thus treated are sown in a seedling box. Thereafter, the seedlings grown in the seedling box are transplanted to the specific area (zone).

[0175] The operations performed by information processing device 10A on a specific area as described above can be applied to operations performed by information processing device 10A on a zone.

[0176] As described above, according to various embodiments disclosed in the present application, for a drug to be sprayed in a specific area (zone) included in a target area where rice is cultivated, the multiple pesticide compositions constituting the drug and the amount of each pesticide composition can be determined at any timing. Also, for a drug to be applied to rice seeds directly sown in a specific area (zone) included in a target area, or to rice seeds sown in a seedling box for transplanting seedlings in the specific area (zone), the multiple pesticide compositions constituting the drug and the amount of each pesticide composition can be determined at any timing.

[0177] 5. Variations In the example described with reference to FIG. 4, the case where ST102 to ST112 can be executed by the same information processing device 10A has been described. However, ST102 to ST112 can be shared and executed by the information processing device 10A mounted on the mobile spraying device 20, the information processing device 10A mounted on the terminal device 30, the information processing device 10A mounted on the server device 10, and / or any other device (other server device, etc.) that can be connected to the network 2. In this case, each device that shares and executes ST102 to ST112 can transmit necessary information to the device that executes the next process via the network 2. This is similarly applicable to the example described with reference to FIG. 11.

[0178] In the example described with reference to FIG. 7, the case where ST202 to ST212 can be executed by the same information processing device 10A has been described. However, ST202 to ST212 can be shared and executed by the information processing device 10A mounted on the mobile spraying device 20, the information processing device 10A mounted on the terminal device 30, the information processing device 10A mounted on the server device 10, and / or any other device (other server device, etc.) that can be connected to the network 2. In this case, each device that shares and executes ST202 to ST212 can transmit necessary information to the device that executes the next process via the network 2. This is similarly applicable to the example described with reference to FIG. 12.

[0179] The above has been described with respect to the case where a drug is prepared by mixing a plurality of pesticide compositions. However, the technology disclosed in the present application is also applicable to the case where only one pesticide composition is selected from a plurality of pesticide compositions, and a drug is prepared using the selected one pesticide composition in a determined amount. In this case, for example, information (9) and information (10) used in the learning model described in ST208 may be changed as follows. (9) Information indicating one pesticide composition constituting the agent to be sprayed in a specific area (zone) (10) Information showing the amount of one of the pesticide compositions shown in information (9) Similarly, "information indicating a plurality of pesticide compositions" used in various learning models may be replaced with "information indicating one pesticide composition," and "information indicating the amount of each of a plurality of pesticide compositions" may be replaced with "information indicating the amount of one pesticide composition."

[0180] The inventors confirmed that there is a correlation between the data input to each learning model described in this application and the data output from this learning model, and as a result, adopted a configuration in which such data is used for each learning data.

[0181] As would be easily understood by a person skilled in the art having the benefit of this disclosure, the various examples described above may be appropriately combined with each other in various patterns, unless a contradiction occurs.

[0182] <2> Pesticide Composition The pesticide composition according to the present invention is a liquid pesticide composition containing the following components (a) and (b1), or a solid pesticide composition containing the following components (a) and (b2): (a) at least one active ingredient selected from the group consisting of active ingredients having a herbicidal effect, active ingredients having an insecticidal effect, and active ingredients having a fungicidal effect; (b1) 1 to 200 g / L of an anionic dispersant; (b2) 1 to 20% w / w of an anionic dispersing agent.

[0183] The pesticide composition of the present invention may contain one or more of the active ingredients described above, and may also contain one or more anionic dispersants.

[0184] As described above, by mixing the anionic dispersant at a specific content, it is possible to ensure sufficient dispersibility of the pesticide composition in the field. As a result, it is possible to reduce the damage caused by local application and the environmental load caused by the active ingredient and auxiliary ingredient.

[0185] When the pesticide composition is a liquid pesticide composition, the formulation is, for example, an aqueous suspension or an oil suspension. Specific examples of the formulation of the liquid pesticide composition include a suspension concentrate (SC).

[0186] When the pesticide composition is a solid pesticide composition, the formulation is, for example, a wettable powder and a granule. Specific formulations of the solid pesticide composition include, for example, a water dispersible granule (WG) and a floating granule. In one embodiment, the solid pesticide composition is a wettable powder having at least one of self-diffusion and floating properties. By having such characteristics, the pesticide can uniformly control pests and weeds in the field where it is sprayed.

[0187] (a) Active ingredient The agricultural chemical composition of the present invention contains at least one selected from the group consisting of (a1) an active ingredient having a herbicidal effect, (a2) an active ingredient having an insecticidal effect, and (a3) ​​an active ingredient having a fungicidal effect.

[0188] (a1) Active ingredient having herbicidal effect Examples of active ingredients having a herbicidal effect include, but are not limited to, tefuryltrione, triafamone, fentrazamide, clomeprop, oxadiazon, ipfencarbazone, cafenstrole, indanofan, fenoxasulfone, mefenacet, butachlor, pretilachlor, fenquinotrion, benzobicyclon, sulcotrione, mesotrione, pyrazolate, benzofenap, pyrimisulfan, ethoxysulfuron, bensulfuron methyl, propyrisulfuron, metazosulfuron, penoxsulam, oxadiargyl, pyraclonil, pentoxazone, florpyrauxifenbenzyl, fenoxaprop ethyl, cyhalofop butyl, metamifop, cyclopyrimorate, dimethamethrin, simethrin, bentazon, oxaziclomefone, bromobutide, and tetflupyrorimet. The active ingredient having a herbicidal effect is at least one selected from the group consisting of these compounds.

[0189] The pesticide composition of the present invention may contain a plurality of active ingredients (a1) having herbicidal effects. In a preferred embodiment, the pesticide composition of the present invention contains one or more active ingredients (a1) having herbicidal effects selected from the group consisting of tefuryltrione, triafamone, fentrazamide, clomeprop, oxadiazon and ethoxysulfuron.

[0190] (a2) An active ingredient having an insecticidal effect Examples of active ingredients having insecticidal effects include, but are not limited to, imidacloprid, thiacloprid, dinotefuran, flupyradifurone, flupirimine, nitenpyram, clothianidin, sulfoxaflor, ethiprole, fipronil, spinosad, tetraniliprole, chlorantraniliprole, cyantraniliprole, pymetrozine, triflumezopyrim, benzpyrimoxane and oxazosulfil. The active ingredient having insecticidal effects is at least one selected from the group consisting of these compounds. The pesticide composition according to the present invention may contain a plurality of active ingredients (a2) having insecticidal effects.

[0191] (a3) An active ingredient having a bactericidal effect Examples of the active ingredient having a fungicidal effect include, but are not limited to, isotianil, probenazole, tricyclazole, diclobentiazox, penflufen, thifluzamide, impirfluxam, kasugamycin, validamycin, fthalide, metominostrobin, azoxystrobin and pencycuron. The active ingredient having a fungicidal effect is at least one selected from the group consisting of these compounds. The pesticide composition according to the present invention may contain a plurality of active ingredients (a3) ​​having a fungicidal effect.

[0192] When the pesticide composition of the present invention contains a plurality of active ingredients selected from the group consisting of the above-mentioned components (a1), (a2) and (a3), the content of the active ingredients specified herein is the total content of each active ingredient.

[0193] When the pesticide composition of the present invention is a liquid pesticide composition, it contains, for example, 10 to 700 g / L, preferably 50 to 600 g / L, and more preferably 200 to 600 g / L of the active ingredient (a).

[0194] When the pesticide composition of the present invention is a solid pesticide composition, it contains, for example, 10 to 70% w / w, preferably 50 to 60% w / w, and more preferably 20 to 60% w / w of the active ingredient (a).

[0195] (b) Anionic dispersants Examples of anionic dispersants include, but are not limited to, alkylnaphthalenesulfonic acid derivatives and ligninsulfonic acid derivatives. Anionic dispersants may be synthetic or commercially available. Examples of commercially available alkylnaphthalenesulfonic acid derivatives of anionic dispersants include Demol SNB, New Calgen PS-P, New Calgen WG-101, New Calgen BX-C, AEROSOL OS, MORWET D425, MORWET IP, SUPRAGIL WP, SUPRAGIL MNS / 90, and TERSPERSE 2020. Examples of commercially available ligninsulfonic acid derivatives of anionic dispersants include New Calgen RX-B, New Calgen WG-4, BORRESPERSE CA, BORRESPERSE NA, MARASPERSE CBOS-4, POLYFON H, POLYFON T, POLYFON O, UFOXANE 3A, and VANISPERSE CB. The anionic dispersant is at least one selected from the group consisting of the compounds mentioned above.

[0196] When the pesticide composition according to the present invention contains a plurality of anionic dispersants, the content specified for the anionic dispersants is the total content of each anionic dispersant.

[0197] When the pesticide composition of the present invention is a liquid pesticide composition, it contains, for example, 1 to 200 g / L, preferably 5 to 150 g / L, and more preferably 50 to 150 g / L of an anionic dispersant.

[0198] When the pesticide composition of the present invention is a solid pesticide composition, it contains, for example, 1 to 20% w / w, preferably 5 to 20% w / w, more preferably 5 to 15% w / w of an anionic dispersant.

[0199] In a preferred embodiment, the agrochemical composition of the present invention is designed so that the application amount of the anionic dispersant is 9 to 300 g / ha when applied to a field. By setting such an application amount, when the spray solution after single use or mixed use is sprayed in a field, particularly in a paddy field, it is possible to impart sufficient diffusion performance to the agrochemical composition while avoiding phytotoxicity to rice. In a more preferred embodiment, the agrochemical composition of the present invention is designed so that the application amount of the anionic dispersant is 9 to 150 g / ha when applied to a field. In an even more preferred embodiment, the agrochemical composition of the present invention is designed so that the application amount of the anionic dispersant is 9 to 130 g / ha when applied to a field.

[0200] The pesticide composition according to the present invention can ensure sufficient dispersibility even when applied locally to paddy fields, and can reduce the environmental load caused by the active ingredient and / or auxiliary ingredient. In addition, since the auxiliary ingredient used is the same, even when multiple pesticide compositions according to the present invention are mixed and applied, the environmental load caused by using more auxiliary ingredients than necessary can be reduced.

[0201] The pesticide composition of the present invention may further comprise auxiliary components, such as surfactants, antifreeze agents, thickeners, preservatives, antifoaming agents, safeners and carriers. All of these auxiliary components may be synthetic or commercially available.

[0202] As the surfactant, for example, a nonionic surfactant can be used. The pesticide composition according to the present invention may contain one or more nonionic surfactants. Examples of the nonionic surfactant include polyoxyethylene-polyoxypropylene block polymers, polyalkylene oxide block polymers, polyoxyalkylene alkylphenyl ethers, polyoxyalkylene fatty acid esters, polyoxyalkylene tristyryl phenyl ethers, polyoxyalkylene alkylamines, polyoxyethylene alkanediols, acetylene glycols, polyoxyethylene acetylene glycols, sorbitan fatty acid esters, sucrose fatty acid esters, polyoxyalkylene sorbitan esters, and glycerin fatty acid esters. Specific examples of commercially available nonionic surfactants include Surfynol 104, Surfynol 420, Surfynol 440, New Calgen TG-310, ATLAS G 5000, DOWFAX 100N50, GENAPOL 10500, PLURONIC (registered trademark) F127, PLURONIC (registered trademark) L62, PLURONIC (registered trademark) P105, PLURONIC (registered trademark) PE 6200, PLURONIC (registered trademark) PE 10500, SYNPERONIC PE / F 127, SOPROPHOR BSU, SOPROPHOR S / 40P, STEP-FLOW 26, SYNPERONIC PE / L 62, TERMUL 5429, ULTRARIC PE 62, ULTRARIC PE 105, and SORPOL (Toho Chemical Co., Ltd.). Examples include T series products, various SPAN products, Mitsubishi Chemical Corporation's Ryoto Sugar Ester products, Daiichi Kogyo Seiyaku Co., Ltd.'s DK Ester products, and Kao Corporation's Leodor SP series products.

[0203] Examples of antifreeze agents include urea, glycerin, polyglycerin and polyglycerin derivatives, ethylene glycol, propanediol, and propylene glycol.

[0204] Specific examples of thickeners include GOHSENOL GL-05, Diutan Gum, Rheocrysta, FBP-34 WELAN GUM, KELZAN, KELZAN BT, KUNIPIA F, KUNIPIA G, RHODOPOL G, RHODOPOL 23, RHODOPOL 50 MC, SATIAXANE CX911, VAN GEL B, VEEGUM R, VOLCLAY HPM-20, WELAN GUM BG3810, and EXILVA.

[0205] Specific examples of preservatives include, for example, Biohope, ACTICIDE B 20, BRONOPOL, KATHON CG / ICP, PREVENTOL BIT 20 N, PREVENTOL D 2, PREVENTOL D 7, PROXEL GXL, and PROXEL GXL(S).

[0206] The defoaming agent includes silicone oil and calcium stearate.Specific examples of commercially available defoaming agents include, for example, ANTIFOAM E-20, ANTIFOAM 8830 FOOD GRADE, SAG10, SAG30, SAG1 1572, SILCOLAPSE 426R, SILCOLAPSE 432, SILCOLAPSE454, SILCOLAPSE482, SILFOAM SE2, SILFAR SE 4 and SILFOAM SRE.

[0207] Examples of carriers include talc, kaolin clay, silica, clay, chalk, quartz, attapulgite, montmorillonite, diatomaceous earth, calcium carbonate, resins, wax, water, alcohol, organic solvents, mineral oils and vegetable oils, and are appropriately selected depending on the form of the pesticide composition.

[0208] When the pesticide composition of the present invention is a solid pesticide composition, it may further contain the following components: for example, citric acid, malic acid, hydrochloric acid, sulfuric acid, sodium hydroxide, aqueous ammonia, ammonium sulfate, binders (e.g., starch, CMC, PVA, polyurethane, PVP, etc.), granulating / disintegration improving agents (e.g., anionic surfactants, disintegration improving agents (e.g., anionic surfactants, CMC), disintegration extending agents (e.g., polyacrylates), etc.

[0209] Another aspect of the present invention relates to a method for preparing an agrochemical composition, the method comprising mixing an active ingredient (a) and an anionic dispersant (b). Specifically, the method for preparing the agrochemical composition comprises: a first mixing step of mixing the active ingredient (a) and the anionic dispersant (b); A second mixing step of adding and mixing auxiliary ingredients to the mixture obtained in the first mixing step; Includes.

[0210] When the pesticide composition of the present invention is a liquid pesticide composition, it can be prepared, for example, by mixing and grinding the active ingredient, an anionic dispersant, a carrier (e.g., water) and other ingredients such as an antifoaming agent, and then adding a separate mixture of water, a thickener and a preservative.

[0211] When the pesticide composition of the present invention is a solid pesticide composition, it can be prepared, for example, by mixing an active ingredient, an anionic dispersant, a carrier (e.g., talc and / or kaolin clay) and other ingredients, dry-grinding them, adding a mixture of a liquid wetting agent and water to the ground product, mixing and kneading them, granulating, drying and sieving them. For example, when the solid pesticide composition is a water dispersible granule (WG), it can be prepared by conventional methods such as spray drying, fluidized bed granulation, pan granulation, mixing with a high-speed mixer, and extrusion without using a solid inert material.

[0212] In another embodiment, the method for producing an agrochemical composition according to the present invention comprises the steps of: a mixing step of mixing the active ingredient (a) with the anionic dispersant (b); a grinding step in which grinding is performed after the mixing step; a further mixing step of further adding and mixing an anionic dispersant (b) after the grinding step; In this way, by adding and mixing the anionic dispersant in portions before and after grinding, it is possible to avoid heat generation due to grinding of the grinding liquid and lower the product temperature. It is preferable to suppress the heat generation due to grinding to less than 10°C, for example. This production method can be used for both SC and WG. Preferably, this production method can be used for SC.

[0213] The pesticide composition according to the present invention may be a mixture prepared by mixing a plurality of pesticide compositions included in the present invention. Therefore, yet another aspect of the present invention relates to a method for producing a pesticide composition, which includes mixing a plurality of the above-mentioned pesticide compositions. When a plurality of pesticide compositions according to the present invention are mixed, the auxiliary components used are common, so that the environmental load caused by using more auxiliary components than necessary can be reduced.

[0214] <3> Methods for controlling diseases, pests and / or weeds Another aspect of the present invention relates to a method for controlling diseases, pests, and / or weeds using the above-mentioned agrochemical composition. More specifically, the present invention relates to a method for controlling diseases, pests, and / or weeds, comprising applying one or more of the above-mentioned agrochemical compositions to a field, in which the anionic dispersant is applied to the field at an application rate of, for example, 9 to 300 g / ha, preferably 9 to 150 g / ha, more preferably 9 to 130 g / ha. By applying the amount within this range, the agrochemical composition can be given sufficient diffusion performance when the spray solution after single use or mixed use is sprayed to a field, particularly a paddy field.

[0215] Examples of rice diseases include, but are not limited to, rice blast (Pyricularia oryzae).

[0216] Examples of rice pests include, but are not limited to: Hemiptera: Nephotettix cincticeps, Nilaparvata lugens, Laodelphax striatellus (Fallen), Sogatella furcifera (Horvath), etc. Coleoptera: Oulema oryzae (Kuwayama), Lissorhoptrus oryzophilus Kuschel, etc.

[0217] Weeds refer to plants that are undesirable for the growth of crop plants growing in a field, and "weed control" refers to controlling undesirable plants or regulating the growth of said plants. In the method for controlling weeds, one or more pesticide compositions according to the present invention are applied to the area where weeds (e.g., pest plants such as monocotyledonous or dicotyledonous weeds or undesirable crop plants), seeds (e.g., grains, seeds or vegetative propagules such as tubers or shoot parts with buds) or crop plants grow (e.g., the area under cultivation).

[0218] Examples of paddy field weeds include, but are not limited to: Dicotyledons of the following genera: Polygonum, Rorippa, Rotala, Lindernia, Bidens, Dopatrium, Eclipta, Elatine, Gratiola, Lindernia, Ludwigia, Oenanthe, Ranunculus, Deinostema, etc. Monocotyledonous plants of the following genera: Echinochloa, Panicum, Poa, Cyperus, Monochoria, Fimbristylis, Sagittaria, Eleocharis, Scirpus, Alisma, Aneilema, Blyxa, Eriocaulon, Potamogeton, etc.

[0219] More specifically, the pesticide composition of the present invention can be used, for example, against the following representative paddy field weeds: Dicotyledonous plants: Rotala indica Koehne, Lindernia procumbens Philcox, Ludwigia prostrata Roxburgh, Pondweed (Potamogeton distinctus A. Benn), Chickweed (Elatine triandra Schk), Japanese parsley (Oenanthe javanica), Monocotyledonous plants: Echinochloa oryzicola Vasing, Monochoria vaginalis Presl, Eleocharis acicularis L., Eleocharis Kuroguwai Ohwi, Cyperus difformis L., Cyperus serotinus Rottboel, Sagittaria pygmaea Miq, Alisma canaliculatum A. Br. et Bouche, Scirpus juncoides Roxburgh.

[0220] In the control method of the present invention, application may be carried out by any means, for example, manual spraying or automatic spraying by manned or unmanned aircraft and vehicles.

[0221] In the control method of the present invention, the field is preferably a paddy field, and the application is preferably to the water surface of the paddy field, more preferably to a localized application to the water surface of the paddy field.

[0222] In the control method according to the present invention, the anionic dispersant is preferably applied to the field at an application rate of 9 to 250 g / ha. By applying the amount within this range, the pesticide composition can be more efficiently diffused in the field.

[0223] In the control method according to the present invention, the active ingredient is applied to the field at an application rate of, for example, 5 to 1000 g / ha, preferably 15 to 500 g / ha. In one embodiment, the active ingredient is applied to the field at an application rate of 50 to 650 g / ha. The application rate of the active ingredient can be appropriately selected from the above range depending on, for example, the climate, the type of disease, pests and / or weeds in the field, and the application time.

[0224] In the control method according to the present invention, the pesticide composition to be applied is automatically selected or selected (for example, by a user) based on, for example, prediction of occurrence of disease, pests and / or weeds in a field and / or the occurrence status thereof. The pesticide composition to be automatically selected or selected may be one or more. The automatic selection may be performed by the information processing device described above.

[0225] In one embodiment, in the control method according to the present invention, the agrochemical composition is automatically measured and applied to the field. In another embodiment, a plurality of agrochemical compositions are automatically measured and / or automatically mixed and applied to the field. The automatic measurement and / or automatic mixing may be performed by the information processing device 10A described above and / or the mobile spraying device 20 described above. In another embodiment in which the information processing device 10A described above and / or the mobile spraying device 20 described above are not used, the agrochemical composition is measured, for example, by a user using an instrument or device and applied to the field, or a plurality of agrochemical compositions are measured and / or mixed, for example, by a user using an instrument or device and applied to the field.

[0226] Another aspect of the present invention is a kit for use in the aforementioned control method, the kit comprising the aforementioned pesticide composition and a container. Preferably, the kit comprises: - a plurality of pesticide compositions as described above; Spatially separated multiple containers and each of the plurality of pesticide compositions is stored in each of the plurality of containers. An example of the kit according to the present invention is a plurality of tanks 24 shown in FIG. 8. With such a kit, an appropriate pesticide composition can be appropriately selected from a plurality of candidate pesticide compositions according to the field conditions and applied. Such a kit may be used in combination with the above-mentioned information processing device 10A, or may be incorporated into the above-mentioned mobile spraying device 20.

[0227] The present invention further relates to the use of the aforementioned agrochemical composition for controlling diseases, pests and / or weeds. In one embodiment of the use of the agrochemical composition of the present invention, the diseases, pests and / or weeds occur in paddy fields.

[0228] As described above, according to the present invention, it is possible to provide a pesticide composition that exhibits excellent diffusibility, particularly excellent diffusibility in water, and therefore, even when applied locally, it is sufficiently effective and phytotoxicity to crop plants can be avoided.

[0229] In addition, two or more kinds of pesticide compositions can be mixed in appropriate amounts according to the disease, pest and / or weed to be controlled based on the prediction of the occurrence of the disease, pest and / or weed and its occurrence status, to obtain a stable dilution, which can be applied to a field (e.g., a paddy field). By applying the agent selected for control according to the field status, it is possible to avoid spraying excessive chemical substances in the environment. In addition, by combining a plurality of pesticide compositions, it is possible to provide a pesticide composition having stable physical and / or chemical properties of the dilution, as well as a pesticide composition as a mixture having different physical and chemical properties and capable of simultaneously controlling different diseases, pests and / or weeds. Such a pesticide composition has excellent diffusibility, and when the active ingredients are mixed and applied, they can be diffused uniformly and widely in a field (e.g., a paddy field) and can exert insecticidal, fungicidal and / or herbicidal effects, so that it is possible to control diseases, pests and / or weeds while avoiding spraying excessive active ingredients that are environmentally burdensome. In addition, by applying a plurality of pesticide compositions within the scope of the present invention, the auxiliary components are common between the pesticide compositions, so that it is possible to avoid using more auxiliary components than necessary, which also reduces the environmental load.

[0230] Furthermore, by combining it with an information processing device and / or a spraying device, it becomes possible to select the type and application amount of pesticide that is more suitable for the disease, pest and / or weed to be controlled based on the prediction of the occurrence of the disease, pest and / or weed and its occurrence status, thereby achieving even more efficient control.

[0231] This application is based on Japanese Patent Application No. 2021-189672 entitled "Agricultural Chemical Composition, Information Processing Apparatus and Computer Program" filed on November 22, 2021, and enjoys the benefit of priority from this Japanese patent application. The entire contents of this Japanese patent application are incorporated herein by reference. EXAMPLES

[0232] Based on the ingredients and compositions shown in the table below, water, active ingredients, dispersants, antifoaming agents, and other ingredients were mixed and pulverized, and then a mixture of water, thickeners, and preservatives was added and mixed to prepare the agricultural chemical compositions of the examples and comparative examples. In the examples, the "application amount" corresponds to the "application amount". The abbreviations in the table are as follows.

[0233] The suspension concentrate (SC) was prepared by mixing the active ingredient, anionic dispersant, water, and other ingredients such as antifoaming agent, grinding the mixture, and then adding a separate mixture of water, thickener, and preservative. The water dispersible granule (WG) was prepared by mixing the active ingredient, anionic dispersant, carrier, and other ingredients, dry grinding the mixture, adding a separate mixture of liquid wetting agent and water to the grinding mixture, mixing and kneading, granulating, drying, and sieving.

[0234] [Table 1-1] [Table 1-2] [Table 1-3] [Table 1-4]

[0235] Test Example 1A The pesticide compositions of Examples 7 and 8 and Comparative Examples 1 and 2 were applied locally to paddy fields at the formulation application rates and dose rates shown in the table below. Localized application was performed on one side of a 2m x 13m plot.

[0236] [Table 2-1]

[0237] When the anionic dispersant was applied at a rate of 4 g / ha (Comparative Example 1) and 0.8 g / ha (Comparative Example 2), the damage to the application site was greater when applied locally (one side treatment on the short side of a 2 m x 13 m plot) than when applied at a rate of 55 g / ha (Example 7) and 9.167 g / ha (Example 8). These results show that anionic dispersants need to be applied at least at 9 g / ha in a pesticide composition whose formulation is a water dispersible granule.

[0238] Test Example 1B Next, using Example 8 and Comparative Example 2, which contain the same active ingredient triafamone but have different dosage forms and dispersant contents, differences in effects due to dispersibility were tested.

[0239] [Table 2-2]

[0240] As shown by the above results, when triafamone was contained as the active ingredient, the pesticide composition containing 0.8 g / ha of dispersant (Comparative Example 2) was insufficiently effective at a distance from the application site, whereas the pesticide composition containing 9.167 g / ha of anionic dispersant (Example 8) was able to ensure sufficient effectiveness even at a distance from the application site.

[0241] Test Example 1C In this test example, Example 2 and Comparative Example 2 were used to test the difference in phytotoxicity due to dispersibility. Example 2 and Comparative Example 2 were applied locally to paddy fields at the formulation application rates and dosages shown in the table below. Localized application was performed by treating one side of a 1m x 15m plot. Both Example 2 and Comparative Example 2 are flowable formulations containing the same active ingredient triafamone, but Example 2 has a higher dispersant concentration than Comparative Example 2.

[0242] [Table 2-3]

[0243] When the anionic dispersant was applied to the field at a rate of 0.8 g / ha (Comparative Example 2), the damage to the application site in localized application (one side treatment on the short side of a 1 m x 15 m plot) was greater than when the anionic dispersant was applied to the field at a rate of 10 g / ha (Example 2). This result shows that anionic dispersant needs to be applied at least at 10 g / ha when applied to the field.

[0244] Test Example 1D In this test example, the difference in phytotoxicity due to dispersibility when multiple formulations were mixed was tested. A mixture of Example 2 and Example 4, and a mixture of Comparative Example 2 and Comparative Example 3 were locally applied to paddy fields at the formulation application rate and dosage shown in the table below. Localized application was performed on one side of a short side of a 1m x 15m plot. Both Example 2 and Comparative Example 2 are flowable formulations containing the active ingredient triafamone, but Example 2 has a higher dispersant concentration than Comparative Example 2. Both Example 4 and Comparative Example 3 are flowable formulations containing the active ingredient fentrazamide, but Example 4 has a higher dispersant concentration than Comparative Example 3.

[0245] [Table 2-4]

[0246] When the total amount of anionic dispersant applied to the field was 8.8 g / ha (Comparative Example 2 + Comparative Example 3), the damage to the application site in localized application (one side treatment on the short side of a 1 m x 15 m plot) was greater than that in the case of 70 g / ha (Example 2 + Example 4). This result shows that the total amount of anionic dispersant applied to the field is insufficient at 8.8 g / ha.

[0247] Test Example 2 A number of pesticide compositions were sprayed in fields using a drone for spraying pesticides, and the effects on barnyard grass, Monochoria vaginalis, and Bulbula juncoides were tested six weeks after treatment. Similarly, the results were compared with manual spraying of pesticides by humans. The pesticide compositions used, the amount of formulation used, the amount of drug applied, and the amount of dispersant applied are shown in the table below. In addition, in Figure 13, the drone spraying flight route is indicated by an arrow (→), and the effectiveness survey points are indicated by numbers 1 to 12.

[0248] [Table 3]

[0249] The above results show that drone spraying is as effective as hand spraying. In addition, similar formulations can be applied to different active ingredients, and there were no problems when multiple pesticide compositions containing at least one of these active ingredients were mixed. The above shows that sufficient effects can be obtained by various application methods and local application.

[0250] Test Example 3 When mixing the active ingredient, anionic dispersant, water, and other ingredients such as an antifoaming agent, the relationship between the amount of anionic dispersant added and the temperature of the grinding liquid during the grinding process was investigated. Specifically, in the pesticide composition of Example 3, the anionic dispersant was added in portions before and after grinding, and the relationship between the amount added and heat generation was investigated. The results are shown in the following table and Figure 14.

[0251] [Table 4-1]

[0252] [Table 4-2]

[0253] The above results show that adding the dispersant in a predetermined amount in portions can avoid heat generation during grinding and lower the product temperature. Specifically, it was shown that when the amount of anionic dispersant added at one time was 80g / L or more, the temperature rose by 10°C or more after 6 minutes of grinding. [Explanation of symbols]

[0254] 1 Decision System 2 Network 10. Server equipment 20 Mobile spreading equipment 21 Power Source 22 Drive mechanism 23 GPS unit 24 Multiple Tanks 25 Preparation equipment 26 Nozzles 30 Terminal Equipment

Claims

1. At least one processor; The at least one processor: Acquire type information indicating at least one type of damage that has occurred in a specific area included in a target area in which the rice seeds are directly sown or the seedlings grown from the seedling box in which the rice seeds are sown are transplanted, and quantity information indicating the amount of each of the at least one type of damage that has occurred in the specific area; Based on the type information and the amount information, a plurality of pesticide compositions constituting an agent to be applied to the seed rice to be directly sown in the specific area or the seed rice to be sown in a seedling box for transplanting seedlings in the specific area, and the amount of each of the plurality of pesticide compositions are determined. An information processing device characterized by being configured as follows.

2. The at least one processor: For each of a plurality of unit areas included in the target area, unit type information indicating the type of at least one harm that has occurred in the unit area and unit amount information indicating the amount of each of the at least one harm that has occurred in the unit area are obtained; Using the unit type information and the unit amount information, a zone is determined from the target area, the zone including a plurality of unit areas that are common or substantially common in the type of harm caused and the amount of harm caused; Using the one zone as the specific area, a plurality of pesticide compositions constituting a drug to be applied to the seed rice to be directly sown in the one zone or the seed rice to be sown in the seedling box, and the amount of each of the plurality of pesticide compositions are determined. The information processing apparatus according to claim 1 , configured to:

3. The at least one processor: Using the unit type information and the unit amount information, determine a plurality of zones from within the target area, each of which includes a plurality of unit areas, and each of which includes a plurality of unit areas that are common or substantially common in the type of harm caused and the amount of harm caused; By using each of the plurality of zones as the specific region, a plurality of pesticide compositions constituting an agent to be applied to the seed rice to be directly sown in each of the plurality of zones or the seed rice to be sown in the seedling box, and the amount of each of the plurality of pesticide compositions are determined. The information processing apparatus according to claim 2 .

4. The at least one processor: The trained first learning model or the trained first decision tree model is At least one of soil information on the type of soil in the specific area, damage occurrence information on damage that has occurred in the specific area in the past, damage spread information on the spread of damage with resistance to pesticides in the specific area, and weather forecast information on weather data predicted for the period during which rice will be cultivated in the specific area, By inputting the above, damage occurrence prediction information regarding the type of damage occurring in the specific area and the amount of the damage, damage growth prediction information regarding the time when the damage will grow, and pesticide damage information regarding pesticide damage occurring in the specific area are obtained, The trained second learning model or the trained second decision tree model is By inputting the damage occurrence prediction information, the damage growth prediction information, and the chemical damage information, a plurality of pesticide compositions constituting a drug to be applied to the seed rice to be directly sown in the specific area or the seed rice to be sown in the seedling box, and the amount of each of the plurality of pesticide compositions are determined.

4. The information processing apparatus according to claim 1, configured as follows:

5. 1. A method performed by at least one processor executing computer readable instructions, comprising: A step of acquiring type information indicating at least one type of damage occurring in a specific area included in a target area in which the rice seeds are directly sown or the seeds grown from the seedling box in which the rice seeds are sown are transplanted, and amount information indicating the amount of each of the at least one damage occurring in the specific area; A step of determining, based on the type information and the amount information, a plurality of pesticide compositions constituting an agent to be applied to the seed rice to be directly sown in the specific area or the seed rice to be sown in a seedling box for transplanting seedlings in the specific area, and the amount of each of the plurality of pesticide compositions; The method according to claim 1, further comprising:

6. When executed by at least one processor, Acquire type information indicating at least one type of damage that has occurred in a specific area included in a target area in which the rice seeds are directly sown or the seedlings grown from the seedling box in which the rice seeds are sown are transplanted, and quantity information indicating the amount of each of the at least one type of damage that has occurred in the specific area; Based on the type information and the amount information, a plurality of pesticide compositions constituting an agent to be applied to the seed rice to be directly sown in the specific area or the seed rice to be sown in a seedling box for transplanting seedlings in the specific area, and the amount of each of the plurality of pesticide compositions are determined. A computer program product causing the at least one processor to function in such a manner.

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