Pesticide composition

Abstract

The present invention relates to a pesticide composition, an information processing device, and a computer program, and provides a pest control method that involves applying a pesticide composition containing an appropriate agent selected by processing information using at least one processor according to the disease, pest, and / or weed occurrence conditions in a paddy field. [Solution] The pesticide composition is a solid pesticide composition comprising 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, and 1 to 200 g / L of an anionic dispersant, or an additional 1 to 20% w / w of an anionic dispersant relative to the two components.

Description

Technical Field

[0001] The present invention relates to a pesticide composition, an information processing apparatus, and a computer program.

Background Art

[0002] Currently, a variety of pesticide formulations developed for paddy rice are on the market. Such pesticide formulations include, for example, herbicides, insecticides, and fungicides.

[0003] As herbicides, there are initial agents that are sprayed before weed emergence, before paddy rice seed sowing or before paddy rice transplantation, immediately after sowing or transplantation; early to mid-term single-shot agents that are sprayed after transplantation; and late-stage agents and mid- to late-stage agents that are sprayed from the mid-growth stage to the late growth stage of paddy rice for controlling weeds that could not be controlled by these initial agents and early to mid-term single-shot agents and difficult-to-control weeds. A number of agents have been developed and sold according to the active ingredients and the appropriate spraying time. Also, as insecticides and fungicides, seed treatment agents, box application agents, and paddy field application agents have been developed and sold according to the characteristics of the active ingredients.

[0004] From the perspective of efficient control, mixtures containing multiple active ingredients have been developed, and by using such mixtures, a variety of diseases, pests, and / or weeds can be efficiently controlled. On the other hand, products that necessarily match the occurrence status of diseases, pests, and / or weeds specific to the field are not always on the market, and there is a current situation where sometimes more active ingredients than necessary are sprayed, raising concerns about the environmental load. In view of such a situation, applying an appropriate amount of a pesticide formulation containing single or multiple active ingredients according to the field situation leads to a reduction in the environmental load.

[0005] Furthermore, if the occurrence of diseases, pests, and / or weeds in the field cannot be accurately grasped, it may be impossible to select appropriate pesticides, and there is a possibility that diseases, pests, and / or weeds cannot be completely controlled. As a result, additional pesticides may have to be applied to control the diseases, pests, and / or weeds that could not be controlled, which may increase the labor, costs, and environmental burden on producers. Moreover, in fields where diseases, pests, and / or weeds cannot be properly controlled, the growth of crop plants may be insufficient, leading to a decrease in yield. In other words, applying the right pesticides at the right time and in the necessary amount is essential for efficient agriculture.

[0006] It should be noted that, conventionally, there is a known technology in which an unmanned aerial vehicle operates a unit that sprays liquid agents at locations determined by a processing unit based on the results of analysis of images of farmland captured by a camera (Patent Document 7). However, this technology does not select an appropriate agent according to the disease, pest, and / or weed situation in the paddy field.

[0007] From the perspective of efficient application of pesticides, selecting multiple pesticide formulations containing a single active ingredient, mixing them in appropriate amounts, and applying them in a single application is an effective method. However, pesticide formulations come in various forms, such as granules, emulsions, flowable formulations, and wettable granules, and there are several problems when mixing and using them. For example, (1) even pesticide formulations that can be diluted and sprayed (emulsifies, flowable formulations, and wettable granules, etc.) are formulations optimized for each active ingredient, and their compositions differ. Therefore, applying a mixture prepared by mixing them releases more chemical substances, which are auxiliary components of the formulation, into the environment than necessary. Also, (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 would not normally occur, 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 formulations that standardize the auxiliary components used for active ingredients, which have diverse physical and chemical properties.

[0008] Furthermore, from the viewpoint of efficient application of pesticides to fields, for example, in the case of paddy fields, it is effective to apply pesticides locally and use the field water to diffuse the active ingredients throughout the paddy field. Several technologies for the diffusion of active ingredients have already been proposed. For example, as a herbicide for rice paddies, a highly diffusible flowable formulation that can be applied from the levees without dilution (Patent Document 1), and a jumbo formulation in which floating granules packaged in a water-soluble film are thrown from the levees (Patent Document 2) have been developed and are on the market. In addition, formulations applied to these local applications sometimes contain dispersants so that the active ingredients can be sufficiently diffused in water after application (Patent Documents 4, 5, and 6). Furthermore, as an application method, the application of wettable granules to 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 undesirable from the viewpoint of environmental impact mentioned above. Furthermore, while insecticides such as surface sprays, which spread an oil film when dropped onto the water surface to diffuse the active ingredient, have been developed, these insecticides are also undesirable from an environmental perspective because they use organic solvents.

[0009] Furthermore, when selecting multiple pesticide formulations containing a single active ingredient and mixing them in appropriate amounts for application, ensuring the diffusibility of the pesticide formulations is important in order to disperse the active ingredient throughout the paddy field using the paddy water. [Prior art documents] [Non-patent literature]

[0010] [Non-Patent Document 1] Guide to Pesticide Formulations / Edited by the Research Group on Pesticide Formulations and Application Methods, Pesticide Science Society of Japan / 1997 [Patent Documents]

[0011] [Patent Document 1] Special Publication No. 7-47522 [Patent Document 2] Patent No. 2957751 [Patent Document 3] Japanese Patent Publication No. 2010-083869 [Patent Document 4] Japanese Patent Publication No. 2011-126786 [Patent Document 5] Japanese Patent Publication No. 2002-338403 [Patent Document 6] Japanese Patent Publication No. 2012-077097 [Patent Document 7] International Publication No. 2020 / 225278 [Overview of the project] [Problems that the invention aims to solve]

[0012] The present invention aims to provide a pesticide composition, an information processing device, and a computer program. [Means for solving the problem]

[0013] A pesticide composition according to one embodiment 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 an active ingredient having an insecticidal effect, an active ingredient having a fungicidal effect, and an active ingredient having a herbicidal effect, (b1) Anionic dispersant in a concentration of 1-200 g / L, (b2) Characterized by being a 1-20% w / w anionic dispersant.

[0014] An information processing device according to one embodiment is characterized by "equipping at least one processor, the at least one processor acquiring type information indicating at least one type of harm that occurred in a specific area included in a target area where rice is cultivated, and quantity information indicating the amount of each of the at least one harm that occurred in the specific area, and determining a plurality of pesticide compositions constituting a pesticide to be sprayed in the specific area, and the amount of each of the plurality of pesticide compositions, based on the type information and the quantity information."

[0015] A method according to one embodiment is characterized by "a method executed by at least one processor that executes instructions readable by a computer, comprising the steps of: acquiring type information indicating at least one type of damage that occurred in a specific area included in a target area where rice is cultivated, and quantity information indicating the amount of each of the at least one type of damage that occurred in the specific area; and determining a plurality of pesticide compositions constituting a chemical to be sprayed on the specific area, and the amount of each of the plurality of pesticide compositions, based on the type information and the quantity information."

[0016] A computer program according to one embodiment is characterized by "being executed by at least one processor to acquire type information indicating at least one type of damage that occurred in a specific area included in a target area where rice is cultivated, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area, and causing the at least one processor to function in such a way that it determines 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, based on the type information and the quantity information."

[0017] An information processing device according to another embodiment is characterized by "equipping at least one processor, the at least one processor acquiring type information indicating at least one type of harm that occurred in a specific area included in a target area where rice seeds are directly sown or seedlings grown from seedling trays into which rice seeds are transplanted, and quantity information indicating the amount of each of the at least one harm that occurred in the specific area, and determining a plurality of pesticide compositions that constitute a drug to be applied to rice seeds directly sown in the specific area or rice seeds sown in seedling trays for transplanting seedlings into the specific area, and the amount of each of the plurality of pesticide compositions based on the type information and the quantity information."

[0018] A method according to another aspect is a method executed by at least one processor that executes computer-readable instructions, the method comprising: obtaining type information indicating at least one type of pest that has occurred in a specific area included in a target area where seeds are directly sown or seedlings grown from a seedling box in which seeds are sown are transplanted, and quantity information indicating the quantity of each of the at least one pest that has occurred in the specific area; and determining, based on the type information and the quantity information, a plurality of pesticide compositions that constitute a pesticide to be applied to the seeds directly sown in the specific area or the seeds sown in a seedling box for transplanting seedlings into the specific area, and the quantity of each of the plurality of pesticide compositions.

[0019] A computer program according to another aspect causes at least one processor to function so as to obtain type information indicating at least one type of pest that has occurred in a specific area included in a target area where seeds are directly sown or seedlings grown from a seedling box in which seeds are sown are transplanted, and quantity information indicating the quantity of each of the at least one pest 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 pesticide to be applied to the seeds directly sown in the specific area or the seeds sown in a seedling box for transplanting seedlings into the specific area, and the quantity of each of the plurality of pesticide compositions.

Advantages of the Invention

[0020] According to the present invention, a pesticide composition, an information processing apparatus, and a computer program can be provided.

Brief Description of the Drawings

[0021] [Figure 1] FIG. 1 is a block diagram showing an example of the overall configuration of a determination system according to an embodiment. [Figure 2]Figure 2 is a block diagram showing an example of the hardware configuration of the information processing device 10A mounted on the server device 10, mobile dispensing device 20, and terminal device 30 in the decision system shown in Figure 1. [Figure 3] Figure 3 is a flowchart showing an example of an operation performed by the decision system 1 shown in Figure 1. [Figure 4] Figure 4 is a flowchart that specifically illustrates an example of one of the operations (ST100) performed by the decision system 1 shown in Figure 3. [Figure 5] Figure 5 is a schematic diagram conceptually illustrating an example of information generated by the information processing device 10A using a learning model in the decision system shown in Figure 1. [Figure 6] Figure 6 is a schematic diagram showing an example of a zone determination method executed by the information processing device 10A in the decision system shown in Figure 1. [Figure 7] Figure 7 is a flowchart that specifically illustrates an example of one of the operations (ST200) performed by the decision system 1 shown in Figure 3. [Figure 8] Figure 8 is a block diagram showing an example of the hardware configuration of the mobile spraying device 20 shown in Figure 1. [Figure 9] Figure 9 is a flowchart that specifically illustrates an example of one of the operations (ST300) performed by the decision system 1 shown in Figure 3. [Figure 10] Figure 10 is a flowchart illustrating another example of the actions performed by the decision system 1 shown in Figure 1. [Figure 11] Figure 11 is a flowchart that specifically illustrates an example of one of the operations (ST1000) performed by the decision system 1 shown in Figure 10. [Figure 12] Figure 12 is a flowchart that specifically illustrates an example of one of the operations (ST2000) performed by the decision system 1 shown in Figure 10. [Figure 13] Figure 13 shows the application areas of the pesticide composition by drone spraying. [Figure 14]Figure 14 shows the relationship between the amount of anionic dispersant added and the heat generated. [Modes for carrying out the invention]

[0022] Section below <1> Next, we will mainly explain the systems for preparing pesticides by mixing pesticide compositions, etc. <2> and <3> Next, we will describe the pesticide compositions that can be prepared using this system, and the methods for controlling diseases, pests, and / or weeds that can be controlled using this system.

[0023] <1> A decision system for determining multiple pesticide compositions to be mixed. Various embodiments of the present invention will be described below with reference to the attached drawings. Note that common components in the drawings are denoted by the same reference numerals. Also, please note that components shown in one drawing may be omitted in another drawing for the sake of clarity. Furthermore, please note that the attached drawings are not necessarily drawn to an exact scale.

[0024] The various systems, methods, and apparatus described herein should not be construed as limiting in any way. In practice, this disclosure is directed toward any novel features and aspects of each of the various embodiments disclosed, combinations of these various embodiments, and combinations of some of these various embodiments. The various systems, methods, and apparatus described herein are not limited to any particular aspects, particular features, or combinations of such particular aspects and particular features, and the things and methods described herein do not require that one or more particular effects exist or problems are solved. Furthermore, various features or aspects of the various embodiments described herein, or some of such features or aspects, may be used in combination with each other.

[0025] While the operation of some of the various methods disclosed herein is described in a particular order for convenience, this method of description should be understood to include the possibility of rearranging the order of the operations unless a particular order is required by the following specific sentences. For example, multiple operations described in order may, in some cases, be rearranged or performed simultaneously. Furthermore, for the sake of simplification, the accompanying drawings do not show various ways in which the various matters and methods described herein may be used in conjunction with other matters and methods.

[0026] Any operating theories, scientific principles, or other theoretical descriptions presented herein in connection with the apparatus or method of this disclosure are provided for the purpose of better understanding and are not intended to limit the technical scope. The apparatus and method in the appended claims are not limited to apparatus and method that operate in the manner described by such operating theories.

[0027] Any of the various methods disclosed herein can be implemented using a plurality of computer-executable instructions stored on one or more computer-readable media, and can be executed on a computer. The one or more media may be non-transient computer-readable storage media such as, for example, at least one optical media disk, a plurality of volatile memory components, or a plurality of non-volatile memory components. Here, the plurality of volatile memory components include, for example, DRAM or SRAM. The plurality of non-volatile memory components include, for example, hard drives and solid-state drives (SSDs). Furthermore, the computer includes any computer available on the market, including, for example, smartphones and other mobile devices having computing hardware.

[0028] Any of the multiple computer-executable instructions for implementing the technologies disclosed herein, along with any data generated and used during implementations of the various embodiments disclosed herein, may be stored in one or more computer-readable media (e.g., non-temporary computer-readable storage media). Such multiple computer-executable instructions may, for example, be 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 (as a process run 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 network computers.

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

[0030] Furthermore, any of the various embodiments of such software (including, for example, a plurality of computer-executable instructions for causing a computer to perform any of the various methods disclosed herein) may be uploaded, downloaded, or accessed remotely by preferred means of communication. Such preferred means of communication include, for example, the Internet, the World Wide Web, intranets, software applications, cables (including fiber optic cables), magnetic communications, electromagnetic communications (including RF communications, microwave communications, and infrared communications), electronic communications, or other such means of communication.

[0031] 1. Overview of the decision system Figure 1 is a block diagram showing an example of the overall configuration of a decision system according to one embodiment. As shown in Figure 1, the decision system 1 may include, for example, at least one server device 10, at least one mobile dispensing device 20, and at least one terminal device 30. These devices 10, 20, and 30 are interconnected via a network 2.

[0032] Note that while Figure 1 shows only one server device 10 as an example, it is also possible to use multiple server devices 10. Similarly, while Figure 1 shows only one mobile sprayer 20 as an example, it is also possible to use multiple mobile sprayers 20. Likewise, while Figure 1 shows only one terminal device 30 as an example, it is also possible to use multiple terminal devices 30.

[0033] Network 2 may include, but is not limited to, cellular networks, wireless networks, fixed-line telephone networks, the Internet, intranets, local area networks (LANs), wide area networks (WANs), and / or Ethernet® networks. The wireless network may include, but is not limited to, Bluetooth®, WiFi (such as IEEE 802.11a / b / n), WiMAX, cellular, satellite, laser, infrared, and 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 this information processing device to spray chemicals. Such a mobile spraying device 20 may be a road-running spraying device (for example, a spraying device that runs on the ground or water surface) or a flying spraying device (for example, 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, feature phone, mobile phone, portable information terminal, personal computer, or tablet, etc.

[0037] In short, the decision system shown in Figure 1 allows the server device 10 to receive and acquire type information indicating at least one type of pest that occurred in a specific area (for example, a specific area within a field) included in a field (target area), and quantity information indicating the amount of each of the at least one pests that occurred in that specific area, from various devices. Such various devices may include a mobile spraying device 20, a terminal device 30, and / or other devices (not shown) that can be connected to the network 2.

[0038] Furthermore, based on the type and quantity information received in this manner, the server device 10 can determine the number of pesticide compositions that constitute the chemicals to be sprayed in the specific area, and the quantity of each of these pesticide compositions. Such a determination may be performed by an information processing device (not shown) mounted on the server device 10. Alternatively, such a determination can also be performed 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 connected to the network 2, in addition to or instead of the information processing device mounted on the server device 10. To achieve this, the device making the determination can receive type and quantity information from other devices (10, 20, 30, etc.) via the network 2. Alternatively, the device making the determination can also receive type and quantity information via a user interface provided on the device.

[0039] Furthermore, as an option, the mobile sprayer 20 can prepare a pesticide by mixing each of the multiple pesticide compositions determined as described above in the amounts determined as described above. Alternatively, the mobile sprayer 20 can also obtain the prepared pesticide itself from a similarly capable preparation device (not shown). This allows the mobile sprayer 20 to move to the specific area (a certain area in the field) within the target area (a field, etc.) and spray the pesticide 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 the information processing device 10A. Figure 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 a decision system according to one embodiment.

[0041] As shown in Figure 2, the information processing device 10A mainly includes a central processing unit 11, a main memory 12, an input / output interface device 13, an input device 14, an auxiliary storage 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 12 and store the results of those calculations in the main memory 12. Furthermore, the central processing unit 11 can control input devices 14, auxiliary storage devices 15, output devices 16, etc., via input / output interface devices 13. The information processing device 10A may include one or more such central processing units 11.

[0043] The main memory 12, referred to as "memory," can store instructions and data received from the input device 14, auxiliary storage device 15, and network 400 via the input / output interface device 13, as well as the calculation results of the central processing unit 11. The main memory 12 may include RAM (Random Access Memory), ROM (Read Only Memory), and / or flash memory, etc., without being limited to these.

[0044] The auxiliary storage device 15 is a storage device with a larger capacity than the main memory device 12. The auxiliary storage device can store instructions and data (computer programs) that constitute the operating system and specific applications. These specific applications are executed by the information processing device 10A mounted on the server device 10 (or mobile dispensing device 20, terminal device 30), thereby enabling the information processing device 10 as a whole to function as the server device 10 (or mobile dispensing device 20, terminal device 30). Furthermore, the auxiliary storage device 15, controlled by the central processing device 11, can transmit these instructions and data (computer programs) to the main memory device 12 via the input / output interface device 13. The auxiliary storage device 15 may include magnetic disk devices and / or optical disk devices, etc., without being limited to these.

[0045] The input device 14 is a device that acquires data from an external source and may include, but is not limited to, at least one sensor, touch panel, button, keyboard and / or mouse. 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.

[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 this hardware configuration, the central processing unit 11 sequentially loads instructions and data (computer programs) constituting a specific application stored in the auxiliary storage device 15 into the main memory device 12, and can perform calculations on the loaded instructions and data. As a result, the central processing unit 11 can control the input device 14 and / or output device 16 via the input / output interface device 13, or it can send and receive various information with other devices (e.g., server device 10, mobile dispensing device 20 and / or terminal device 30, etc.) via the input / output interface device 13 and the network 2.

[0048] Furthermore, the information processing device 10A may also include one or more microprocessors and / or a graphics processing unit (GPU), etc., in place of or together with the central processing device 11.

[0049] 3. Operation of the decision system (Part 1) Next, a specific example of an operation performed by the decision system 1 having the above configuration will be explained with reference to Figure 3. Figure 3 is a flowchart showing an example of an operation performed by the decision system 1 shown in Figure 1.

[0050] As shown in Figure 3, the actions performed by the decision system 1 broadly include a step (hereinafter referred to as "ST") 100 for acquiring information about the target area (or a specific area included in the target area), and an ST 200 for determining a plurality of pesticide compositions that constitute the agent, and the amount of each pesticide composition, based on the information acquired in ST 100. Furthermore, as an option, the actions performed by the decision system 1 may include an ST 300 for spraying the agent, which is prepared by mixing each of the plurality of pesticide compositions determined in ST 200 in a determined amount, onto the target area (or a specific area included in the target area). The following sections will sequentially explain specific examples of ST100, ST200, and ST300.

[0051] 3-1. Operations performed in ST100 Figure 4 is a flowchart that specifically illustrates an example of one of the operations (ST100) performed by the decision system 1 shown in Figure 3. Here, the target area refers to the area where rice is cultivated (for example, the area where seedlings are transplanted, and / or the area where rice seeds are directly sown), and may be one field, a part of one field, and / or multiple fields. Furthermore, "harm" may include, but is not limited to, diseases that occur in the target area (or rice grown in the target area), insect pests that occur in the target area (or rice grown in the target area), and / or weeds that occur in the target area.

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

[0053] In ST104, the information processing device 10A can identify which pixels in the image have been affected by which type of damage or none of the damage, by inputting the image acquired in ST102 into a trained model.

[0054] Such a pre-trained model may be a machine learning (particularly deep learning) capable model that includes an input layer, an output layer, and multiple hidden layers placed between the input and output layers. Each such model is pre-trained using an image of rice plants affected by a certain type of damage (input data), information indicating which pixels (one or more pixels) in the image are affected by the damage (output data), and a large number (e.g., tens of thousands or more) of training data. Such a model can perform learning by optimizing a large number of coefficients used in the model to minimize the error between the output data when the input data for each training data set is input and the output data for that training data set.

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

[0056] The information processing device 10A can input an image into such a trained model and obtain information from the trained model indicating which pixel in the image is affected and what kind of damage has occurred.

[0057] Figure 5 is a schematic diagram conceptually illustrating an example of information generated by the information processing device 10A using a learning model in the decision system shown in Figure 1. For the sake of simplicity, Figure 5 shows an example of information generated for each of a very small unit regions within a target region by inputting an image of the target region into the learning model.

[0058] Figure 5 shows only six unit regions 120A to 120I as an example. Each unit region can be represented by any number of pixels, but in this example, it can be represented by 25 pixels (5 pixels x 5 pixels). In this example, each unit region is a square, but in other examples, it can be any shape such as a polygon, circle, ellipse, trapezoid, or rhombus.

[0059] For each unit region, each pixel can be associated with information specific to the pest that occurred at the location corresponding to that pixel. For example, focusing on unit region 120A, the location corresponding to pixel 120A1 is associated with the information "1" specific to the first pest that occurred at that location. Here, as an example, a total of 10 locations corresponding to pixels can be associated with the information "1" specific to the first pest that occurred at that location. The first pest is one of the following: a disease that occurred in the target region (or the rice grown in the target region), a pest that occurred in the target region (or the rice grown in the target region), or a weed that grew in the target region.

[0060] Furthermore, in one example, the position corresponding to pixel 120A2 may be associated with "null," which is information specific to the event that no harm occurred at this position. In another example, the position may be associated with "0," or other information specific to the event that no harm occurred at this position.

[0061] Furthermore, if we focus on a unit region 120G, for example, the position corresponding to pixel 120G1 will be associated with information "4" that is specific to the fourth type of pest that occurred at that position. Here, as an example, information "4" that is specific to the fourth type of pest that occurred at that position can be associated with each of the five positions corresponding to a total of five pixels. The fourth type of pest is one of the following: a disease that occurred in the target region (or the rice grown in the target region), a pest that occurred in the target region (or the rice grown in the target region), or a weed that grew in the target region.

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

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

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

[0065] Other unit regions will not be explained in detail, but the concept is the same as that described for unit regions 120A and 120G.

[0066] Returning to Figure 4, in ST106, the information processing device 10A can use the information output from the learning model in ST104 (for example, the information exemplified in Figure 5) to acquire "unit type information" indicating at least one type of harm that occurred in each of the multiple unit regions included in the target region, and store it (for example in the auxiliary storage device 15).

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

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

[0069] Furthermore, for the unit area 120G, the information processing device 10A can acquire information "4", information "5", and information "6" (information "4" corresponding to the fourth type of harm, information "5" corresponding to the fifth type of harm, and information "6" corresponding to the sixth type of harm) as unit type information. The information processing device 10A can also acquire information "4", information "5", and information "6" as unit type information for the unit area 120H.

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

[0071] In one example, the information processing device 10A can acquire unit type information for all unit areas included in the target area, but in another example, it can acquire unit type information for each of the partial 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, if the target area is a single field and the specific area is a part of this field, the information processing device 10A can also acquire type information indicating at least one type of damage that occurred in this 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 constituting the part of the area in the information acquired from the learning model by ST104. For example, if the specific area is an area composed of unit area 120D and unit area 120G, the information processing device 10A can acquire information "2", information "3", information "4", information "5", and information "6" indicating the types of damage that occurred in these unit areas as type information.

[0073] Furthermore, information identifying the target area and information identifying a specific area within this target area can be input by a user via the input device 14 of the information processing device 10A and acquired by the information processing device 10A.

[0074] Returning to Figure 4, in ST108, the information processing device 10A can use the information output from the learning model in ST104 (for example, the information exemplified in Figure 5) to determine the amount of at least one harm that occurred in each of the multiple unit regions included in the target region. As a result, the information processing device 10A can acquire and store (for example, in the auxiliary storage device 15) unit quantity information indicating the amount of at least one harm that occurred in each of the multiple unit regions included in the target region.

[0075] Focusing on the example shown in Figure 4, the information processing device 10A can acquire, as unit quantity information, "1-10" (information indicating that there are 10 pieces of information "1" indicating the type of harm that occurred in the unit area 120A / information indicating that the density of information "1" is "10") for the unit area 120A. The information processing device 10A can also acquire, as unit quantity information, "1-10" (information indicating that there are 10 pieces of information "1" indicating the type of harm that occurred in the unit area 120) for each of the unit areas 120B and 120C.

[0076] Furthermore, for the unit region 120D, the information processing device 10A can acquire, as unit quantity information, the information "2-4" indicating that there are four pieces of information "2" indicating the type of harm that occurred in this unit region 120D (information indicating that there are four pieces of information "2" corresponding to the second type of harm / information indicating that the density of information "2" is "4") and the information "3-6" indicating that there are six pieces of information "3" indicating the type of harm that occurred in this unit region 120D (information indicating that there are six pieces of information "3" corresponding to the third type of harm / information indicating that the density of information "3" is "6"). For each of the unit regions 120E and 120F, the information processing device 10A can also acquire, as unit quantity information, the information "2-4" indicating that there are four pieces of information "2" indicating the type of harm that occurred in this unit region and the information "3-6" indicating that there are six pieces of information "3" indicating the type of harm that occurred in this unit region 120D.

[0077] Furthermore, for the unit area 120G, the information processing device 10A can acquire as unit quantity information the following: information "4-5" indicating that there are 5 pieces of information "4" indicating the type of harm that occurred in this unit area 120G, information "5-3" indicating that there are 3 pieces of information "5" indicating the type of harm that occurred in this unit area 120G, and information "6-2" indicating that there are 2 pieces of information "6" indicating the type of harm that occurred in this unit area 120G. Similarly, for the unit area 120H, the information processing device 10A can acquire as unit quantity information the following: information "4-5" indicating that there are 5 pieces of information "4" indicating the type of harm that occurred in this unit area 120H, information "5-3" indicating that there are 3 pieces of information "5" indicating the type of harm that occurred in this unit area 120H, and information "6-2" indicating that there are 2 pieces of information "6" indicating the type of harm that occurred in this unit area 120H.

[0078] In one example, the information processing device 10A can acquire unit quantity information for all unit areas included in the target area, but in another example, it can acquire unit quantity information for each of the partial 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 "quantitative information" indicating the amount of at least one type of damage that occurred in a specific area included in the target area. For example, if the target area is a single field and the specific area is a part of this field, the information processing device 10A can also acquire quantitative information indicating the amount of at least one type of damage that occurred in this part of the field. In this case, the information processing device 10A can acquire the above-mentioned type information by searching for information associated with each pixel constituting the part of the field in the information acquired from the learning model by ST104. For example, if the specific area is an area composed of unit area 120D and unit area 120G, the information processing device 10A can acquire the following information as quantitative information. • Information indicating that there are four instances of "2," which represents the type of harm that occurred in these unit regions, is "2-4." • Information indicating that there are six instances of "3," which represents the type of harm that occurred in these unit regions, is "3-6." • Information indicating that there are five instances of information "4" indicating the type of harm that occurred in these unit areas, labeled "4-5". • Information indicating that there are three instances of information "5" indicating the type of harm that occurred in these unit regions ("5-3"). • Information "6-2" indicating that there are two instances of information "6" indicating the type of harm that occurred in these unit regions.

[0080] Returning to Figure 4, we have so far described an example in which, as one embodiment, the information processing device 10A acquires unit type information (or type information) in ST104 and ST106 via ST102, and acquires unit quantity information (or quantity information) in ST108. However, in another embodiment, the information processing device 10A can acquire unit type information (or type information) in ST106 and unit quantity information (or quantity information) in ST108 without going through ST102 and ST104. Specifically, for example, the information processing device 10A can acquire unit type information (or type information) from some device via network 2 in ST106, and acquire unit quantity information (or quantity information) from some device via network 2 in ST108. In this case, the device in question could be any other information processing device capable of obtaining (generating) this information by performing the processes shown in ST102 to ST108, or it could be any other information processing device capable of obtaining (generating) this information by performing any other process different from the processes shown in ST102 to ST108.

[0081] Next, as an option, in ST110, the information processing device 10A can determine at least one zone from within the target area using unit type information and unit quantity information (or type information and quantity information). Here, a zone can be defined as a group of unit areas that share common or substantially common characteristics in terms of the type and amount of harm that occurs.

[0082] The zone determination method executed by the information processing device 10A will be explained with reference to Figure 6. Figure 6 is a schematic diagram showing an example of the zone determination method executed by the information processing device 10A in the determination system shown in Figure 1. This Figure 6 corresponds to Figure 5, which was referred to earlier.

[0083] First, let's focus on unit area 120A. In unit area 120A, there are 10 pieces of information "1". That is, there is one piece of single-type information related to unit area 120A, and there are pieces of unit quantity information related to unit area 120A, numbered "1-10". By searching for unit type information and unit quantity information related to other unit areas, the information processing device 10A can identify that the unit type information for each of unit areas 120B and 120C each has only one piece of information "1", and the unit quantity information for each of unit areas 120B and 120C each has only pieces of information "1-10". In other words, the information processing device 10A can identify that 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 the area encompassing all of unit areas 120A, 120B, and 120C as the first zone 130.

[0084] Next, let's focus on unit area 120D. In unit area 120D, there are four instances of information "2" and six instances of information "3". That is, the single-type information for unit area 120D is information "2" and information "3", and the unit quantity information for unit area 120D is information "2-4" and information "3-6". By searching for unit type information and unit quantity information for other unit areas, the information processing device 10A can identify that the unit type information for unit area 120E and unit area 120F each consists only of information "2" and information "3", and the unit quantity information for unit area 120E and unit area 120F each consists only of information "2-4" and information "3-6". In other words, the information processing device 10A can identify that 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 the area encompassing the entirety of the unit areas 120D, 120E, and 120F as the second zone 131.

[0085] Next, let's focus on unit area 120G. In unit area 120G, there are five instances of information "4", three instances of information "5", and two instances of information "6". That is, unit area 120G has single-type information consisting of information "4", information "5", and information "6", and unit quantity information consisting of information "4-5", information "5-3", and information "6-2". By searching for unit type information and unit quantity information for other unit areas, the information processing device 10A can identify that unit area 120H has only unit type information consisting of information "4", information "5", and information "6", and unit quantity information consisting of information "4-5", information "5-3", and information "6-2". That is, the information processing device 10A can identify that unit areas 120G and 120H are common to each other in terms of both the type of harm that occurred and the amount of harm that occurred. As a result, the information processing device 10A can determine the area including the entirety of the unit areas 120G and 120H as the third zone 132.

[0086] In the example shown in Figure 6, the information processing device 10A can identify multiple areas that are "common" in terms of the type and amount of damage that occurred, and determine that the area including all of these identified areas is a single zone. However, in another embodiment, the information processing device 10A can also identify multiple areas that are "substantially common" in terms of the type and amount of damage that occurred, and determine that the area including all of these identified areas is a single zone.

[0087] Here, "substantially common" means not only when the type of harm occurring in one unit domain is exactly the same as the type of harm occurring in another unit domain, but also when the type of harm occurring in the aforementioned unit domain is similar to the type of harm occurring in the aforementioned unit domain (it is possible to consider both types as substantially identical).

[0088] Furthermore, "substantially identical" means not only when the amount (density) of harm generated in one unit region is exactly the same as the amount (density) of harm generated in another unit region, but also when the error between the amount (density) of harm generated in one unit region and the amount (density) of harm generated in another unit region is less than or equal to an arbitrary predetermined value (i.e., both amounts can be considered substantially identical).

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

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

[0091] As will be described later, by using such zone information, the mobile sprayer 20 can spray a mixture of multiple pesticide compositions determined for each zone, in the amount of each pesticide composition determined for that zone, onto that zone.

[0092] Furthermore, the information processing device 10A can also execute the processes described in ST110 and ST112 (Figure 4) in ST200 (Figure 3).

[0093] 3-2. Operations performed in ST200 Figure 7 is a flowchart specifically illustrating an example of one of the operations (ST200) performed by the decision system 1 shown in Figure 3. The information processing device 10A can perform the operations shown in Figure 7 for each specific area or each zone included in the target area. Of course, the information processing device 10A can also perform the operations shown in Figure 7 for any one specific 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 type of damage that occurred in a specific area (for example, a field or a part of a field), and quantity information indicating the amount of each of these at least one types of damage that occurred in that specific area. Such information has already been acquired and recorded by the information processing device 10A in ST106 and ST108.

[0095] In this case, if a zone determined by ST110 (see Figure 4) is used as the specific area, the information processing device 10A can acquire the zone information stored in ST112 (see Figure 4) corresponding to that zone, and acquire unit type information and unit quantity information corresponding to each unit area identified by this zone information.

[0096] Next, in ST204, the information processing device 10A can acquire various types 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 information (5).

[0097] (1) Soil information regarding soil types in specific areas (zones) (2) Information on damage that has occurred in a specific area (zone) in the past (3) Seed burial information regarding seeds buried in the soil of a specific area (zone) (4) Information on the spread of drug-resistant pests in specific areas (zones) (5) Weather forecast information relating to predicted weather data for the period of rice cultivation in a specific area (zone)

[0098] Information (1), namely soil information, may be information that indicates (identifies) the type of soil (sand, clay, etc.) in a specific area (zone). This information (1) may be used considering the possibility that the type of soil in a specific area (zone) may influence the damage that occurs in that soil, the speed and timing of the development of this damage, etc.

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

[0100] Information (3), namely seed burial information, may be information that indicates (identifies) seeds (weed seeds) that are buried in a specific area (zone). If a certain seed is buried in a specific area (zone), this information (3) may be used considering that there is a high probability that the weed corresponding to that seed is currently growing in the same specific area (zone).

[0101] Information (4), namely pest spread information, may be information that indicates (identifies) which pests resistant to pesticides (fungicides, insecticides, and / or herbicides) are currently prevalent in a specific area (zone). If a certain resistant pest is currently prevalent in an area including a specific area (zone), this information (5) may be used, taking into consideration the high probability that such resistant pests are currently occurring in the same specific area (zone).

[0102] Information (5), namely weather forecast information, may be information that indicates (identifies) predicted weather data (temperature, humidity, weather, and / or precipitation, etc.) for the period during which rice is cultivated in a specific area (zone). This information (5) may be used considering the possibility that this weather data for the period during which rice is cultivated in a specific area (zone) may affect pests occurring in the same specific area (zone), and the speed and timing of the development of such pests.

[0103] The information described above may also be information that the user inputs using the input device 14 (keyboard, mouse, touch panel, etc.) of the information processing device 10A, by using the 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 obtain the following information (6) to information (8) by inputting the information acquired in ST202 (type information and quantity information) and at least one piece of information acquired in ST204 (i.e., at least one piece of information (1) to information (5)) into a trained first learning model or a trained first decision tree model.

[0105] (6) Harm prediction information regarding the type and amount of harm that occurs in a specific area (zone) (7) Information on the timing of the growth of this pest. (8) Drug-induced harm information concerning drug-induced harm occurring in specific areas (zones)

[0106] Information (6), namely, damage prediction information, can literally be information that indicates (identifies) the type of damage that will occur in a specific area (zone) and the amount of this damage.

[0107] Information (7), namely pest growth prediction information, can be information that indicates (identifies) when (how quickly and at what time) the pests shown in information (6) will occur.

[0108] Information (8), that is, drug-induced harm information, may be information that indicates (identifies) what kind of drug-induced harm (side effects, etc.) occurs in a specific area (zone).

[0109] The first learning model (such as a machine learning / deep learning model with an input layer, multiple hidden layers, and an output layer) and the first decision tree model are each pre-trained using type information, quantity information, and a large number (e.g., tens of thousands or more) of training data, including information (1) to information (5) (input data) and information (6) to information (8) (output data). Such a first learning model or first decision tree model can perform learning by optimizing the numerous coefficients (or numerous parameters) used in the first learning model or first decision tree model to minimize the error between the data output when the input data in each training data set is input and the output data in that training data set.

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

[0111] Next, in ST208, 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, thereby acquiring and storing the following information (9) and information (10) (for example, in the auxiliary storage device 15).

[0112] (9) Information indicating (identifying) multiple pesticide compositions that constitute 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] As an option, the information processing device 10A can also input 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) at least one of the following information (11) and information (12), in addition to information (9) and information (10).

[0114] (11) Optimal continuous treatment information for the optimal method of continuously spraying (treating) the chemicals mixed according to information (9) and information (10) (12) Optimal treatment timing information regarding the most suitable time to spray (treat) the chemicals mixed according to information (9) and information (10).

[0115] Information (11), namely optimal continuous treatment information, may be information that indicates (identifies) the optimal method (for example, a method of spraying the chemicals continuously every two weeks) for spraying (treating) the chemicals mixed according to information (9) and information (10).

[0116] Information (12), namely, optimal treatment timing information, may be information that indicates (identifies) at what time (for example, March) it is optimal to spray (treat) the chemicals mixed according to information (9) and information (10).

[0117] Such second learning models and second decision tree models are each pre-trained using information (6) to information (8) (input data), information (9) to information (12) (output data), and a large number of training data (e.g., tens of thousands or more). Such second learning models or second decision tree models can perform learning by optimizing the numerous coefficients (or numerous parameters) used in the second learning model or second decision tree model to minimize the error between the data output when the input data in each training data is input and the output data in that training data.

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

[0119] In addition, in ST208, the 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 the output device 16.

[0120] Next, in the optional ST210, the information processing device 10A can acquire various types 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 the information (13) shown below, and "at least one" of the information (14) to information (16).

[0121] (13) Information on the general effects of drugs on harm indicated by 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 on the soil composition of a specific area (zone) (16) Information regarding the moisture retention capacity of a specific area (zone)

[0122] Information (13) may be information that indicates (identifies) an effect of the drug that is generally known as an effect of the drug on the harm indicated by information (6).

[0123] Information (14) may be information indicating (identifying) the types of drugs actually used in the past in a particular area (zone) and the effects actually obtained with those drugs in the past. Even if a drug is generally known to be highly effective (or less effective) against a certain harm, there may be cases where, despite being used in the past in a particular area (zone), the drug did not exert sufficient effects (or was able to exert sufficient effects). This information (14) may be used to take such cases into consideration.

[0124] Information (15) may be information indicating (identifying) the soil composition of a specific area (zone). This information (15) may be used considering the high probability that the soil composition of a specific area (zone) will affect the effectiveness of the pesticide composition applied to this area.

[0125] Information (16) may be information indicating (identifying) the moisture retention capacity of a specific area (zone). This information (16) may be used considering the high probability that the moisture retention capacity of a specific area (zone) will affect the effectiveness of the pesticide composition sprayed on this area.

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

[0127] Next, in the optional ST212, the information processing device 10A can acquire and store the following information (9) and information (10) (for example, in the auxiliary storage device 15) by inputting the information acquired in ST210, namely information (13) and at least one of the 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 that constitute the agent to be sprayed in a specific area (zone) (10) Information showing the amount of each of the multiple pesticide compositions shown in information (9)

[0129] Additionally, as an option, the information processing device 10A can input the information acquired by ST210, namely 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 information (9) and information (10) described above. In this case, information (13) may be essential information.

[0130] (9) Information indicating multiple pesticide compositions that constitute the agent to be sprayed in a specific area (zone) (10) Information showing the amount of each of the multiple pesticide compositions shown in information (9) (11) Optimal continuous treatment information for the optimal method of continuously spraying (treating) the chemicals mixed according to information (9) and information (10) (12) Optimal treatment timing information regarding the most suitable time to spray (treat) the chemicals mixed according to information (9) and information (10).

[0131] The third learning model (machine learning / deep learning model having an input layer, multiple hidden layers, and an output layer, etc.) and the third decision tree model are each pre-trained using information (13) to information (16) (input data), information (9) to information (12) (output data), and a large number (e.g., tens of thousands or more) of training data. Such a third learning model or third decision tree model can perform learning by optimizing the numerous coefficients (or numerous parameters) used in the third learning model or third decision tree model to minimize the error between the data output when the input data in each training data is input and the output data in that training data.

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

[0133] In addition, in ST212, the 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 the output device 16.

[0134] In ST208 described above, the information processing device 10A can identify multiple pesticide compositions constituting the chemicals to be sprayed in a specific area, and the amount of each pesticide composition, using information (9) and information (10), respectively. At this point, the information processing device 10A can achieve its initial objective. However, by executing the optional ST210 and ST212 described above, the information processing device 10A can input information (13) and at least one of the information (14) to information (16) into a trained third learning model or a trained third decision tree model, thereby obtaining information (9) and information (10) based on this information. That is, the information processing device 10A can obtain multiple pesticide compositions constituting the chemicals to be sprayed in a specific area, and the amount of each pesticide composition, based on information (13) and at least one of the information (14) to information (16). As a result, the user can recognize two types of information as options: information (9) and information (10) (and also information (11) and / or information (12)) shown on the information processing device 10A in ST208, and information (9) and information (10) (and also information (11) and / or information (12)) shown on the information processing device 10A in ST212.

[0135] 3-3. Operations performed in ST300 In ST300, the mobile sprayer 20 can spray a chemical prepared by mixing the determined amounts of each pesticide composition determined in ST200 onto a specific area (or zone). Before describing the operations performed in this ST300, we will first briefly explain the hardware configuration of the mobile spraying device 20 with reference to Figure 8.

[0136] Figure 8 is a block diagram showing an example of the hardware configuration of the mobile spraying device 20 shown in Figure 1. As shown in Figure 8, the mobile spraying device 20 may include the information processing device 10A described above, a drive source 21 and a drive mechanism 22 for moving the mobile spraying device 20, a GPS (Global Positioning System) unit 23 for acquiring positional information regarding the current position of the mobile spraying device 20, a plurality of tanks 24, each containing its own unique pesticide composition, a preparation device 25 for mixing any plurality of pesticide compositions from these plurality of tanks 24 to prepare a chemical agent, and one or more nozzles 26 for spraying the chemical agent supplied from the preparation device 25.

[0137] The drive source 21 may be an engine and / or motor that, under the control of the information processing device 10A, generates driving force to move the mobile spraying device 20 and provides it to the drive mechanism 22.

[0138] The drive mechanism 22 is an optional 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, tracks, and / or propellers. The drive mechanism 22 is also an optional 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 reduction gear, a steering mechanism, flaps, and / or a rudder.

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

[0140] Each of the multiple tanks 24 can contain a pesticide composition specific to that tank from among multiple pesticide compositions.

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

[0142] Specifically, the preparation device 25 may have a valve, which can be opened and closed under the control of the information processing device 10A, provided in a pipe connecting the preparation device 25 to each of the multiple tanks 24. The preparation device 25 can open the valve designated by the information processing device 10A and measure the amount of pesticide composition passing through the valve using a sensor. When the amount of pesticide composition measured by this sensor reaches the amount designated by the information processing device 10A, the preparation device 25 can return the valve to the closed state. As a result, the preparation device 25 can prepare the pesticide by obtaining and mixing the amount of 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 connected to the preparation device 25. Each nozzle 26 is controlled by the information processing device 10A and can spray (inject) the chemical supplied from the preparation device 25.

[0144] Next, the operations performed by the mobile spraying device 20 having the above configuration will be explained with reference to Figure 9. Figure 9 is a flowchart that specifically shows an example of one of the operations (ST300) performed by the decision system 1 shown in Figure 3.

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

[0146] Next, in ST304, the information processing device 10A can acquire positional information indicating the location of a specific region (or each zone). In one example, this positional information may be calculated based on the positional information (of the target region) assigned to the image by the device that generated the image of the target region in ST102, and the relative positional relationship of the specific region (each zone) with respect to the target region derived from this image. In another example, this positional information may be input by the user at any time via the input device 14 of the information processing device 10A 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 location information indicating the current position of the mobile spraying device 20. Furthermore, the information processing device 10A can determine a travel route (information relating to the positions of multiple points to be sequentially passed through) based on the current location thus acquired and the positions of specific areas (or each zone) acquired in ST304. Such a travel route can be determined, for example, using technology used in well-known navigation systems. In the case of spraying pesticides over multiple zones, the information processing device 10A can determine a travel route that passes through all of the multiple unit areas included in each zone. For example, in the example shown in Figure 6, the travel route can be determined so as to pass through all of unit areas 120A, 120B, and 120C for the first zone 130, all of unit areas 120D, 120E, and 120F for the second zone 131, and all of 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 achieved 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 along the movement route determined in ST306.

[0149] In ST310, the information processing device 10A can control the preparation device 25 to mix multiple pesticide compositions determined for a specific area (or zone) in the amount of each determined pesticide composition, for example, when approaching that specific area (or zone). As a result, the preparation device 25 can perform the above-described operation and prepare the pesticide corresponding to that specific area (or zone) by mixing multiple pesticide compositions determined for that specific area (or zone) in the amount of each determined pesticide composition.

[0150] Furthermore, the information processing device 10A can transmit a signal to the nozzle 26 instructing it to open when the difference between the current position of the mobile spraying device 20 and the position of a specific area (or each zone) falls below a threshold. This allows the nozzle 26 to spray (inject) the chemical supplied from the preparation device 25.

[0151] Furthermore, the information processing device 10A can continuously transmit a signal to the nozzle 26 instructing it to open the nozzle as long as the difference between the current position of the mobile spraying device 20 and the position of its specific area (or each zone) is below a threshold. This allows the mobile spraying device 20 to spray the chemical corresponding to its specific area (or each zone) while it is in a position that matches the location of that area.

[0152] Subsequently, the information processing device 10A can transmit a signal to the nozzle 26 instructing it to close the nozzle when the difference between the current position of the mobile spraying device 20 and the position of a specific area (or each zone) exceeds a threshold. This allows the nozzle 26 to stop spraying the chemical.

[0153] In cases where the mobile spraying device 20 sprays chemicals over multiple specific areas (or multiple zones), ST308 and ST310 may be executed sequentially for each specific area (each zone).

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

[0155] 4. Operation of the decision system (Part 2) Up to this point, we have described a method (first embodiment) for determining multiple pesticide compositions to be included in a pesticide and the amount of each pesticide composition for a pesticide to be sprayed on a specific area (or zone) included in the target area. However, this first embodiment is also applicable to a method (second embodiment) for determining multiple pesticide compositions to be included in a pesticide and the amount of each pesticide composition for a pesticide to be treated (applied) on rice seeds that are directly sown in a specific area (or zone) included in the target area (or rice seeds that are sown in seedling trays for transplanting seedlings to the specific area). In this Section 4, the target area means an area where rice seeds are directly sown, and / or an area where seedlings grown from seedling trays into which the seeds were sown are transplanted, and may be one field, a part of one field, and / or multiple fields.

[0156] To avoid redundant explanations, the following brief description will focus only on the parts of the second embodiment that differ from the first embodiment described above, with reference to Figure 10. Figure 10 is a flowchart showing another example of the operations performed by the decision system 1 shown in Figure 1.

[0157] As shown in Figure 10, the actions performed by the decision system 1 broadly include ST1000, which acquires information about the target area (or a specific area included in the target area), and ST2000, which, based on the information acquired in ST1000, determines a plurality of pesticide compositions and the amount of each pesticide composition for treating the rice seeds to be directly sown in the target area (or a specific area included in the target area), or the rice seeds to be sown in seedling trays for transplanting seedlings to this target area (or specific area). Furthermore, as an option, the actions performed by the decision system 1 may include ST3000, which treats the rice seeds to be directly sown in the target area (or a specific area included in the target area), or the rice seeds to be sown in seedling trays for transplanting seedlings to this target area (or specific area), with a chemical prepared by mixing each of the plurality of pesticide compositions determined in ST2000 in determined amounts.

[0158] First, let's focus on ST1000. Figure 11 is a flowchart that specifically shows an example of one of the operations (ST1000) performed by the decision system 1 shown in Figure 10. The operation shown in Figure 11 lacks ST102 and ST104 shown in Figure 4.

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

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

[0161] ST1006 and ST1008 correspond to ST110 and ST112 shown in Figure 4, respectively.

[0162] Returning to Figure 10, we now focus on ST2000. Figure 12 is a flowchart that specifically illustrates an example of one of the operations (ST2000) performed by the decision system 1 shown in Figure 10.

[0163] ST2002 to ST2006 correspond to ST204 to ST208 shown in Figure 7, respectively. Below, we will explain only the differences between ST2002 to ST2006 and those described above, referring to Figure 7.

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

[0165] (1) Soil information regarding soil types in specific areas (zones) (2) Information on damage that has occurred in a specific area (zone) in the past (3) Information on the spread of drug-resistant pests in specific areas (zones) (4) Weather forecast information relating to predicted weather data for the period of rice cultivation in a specific area (zone)

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

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

[0168] (5) Prediction information regarding the type and amount of damage that occurs in a specific area (zone) (6) Information on the timing of the growth of this pest. (7) Drug-induced harm information concerning drug-induced harm occurring in specific areas (zones)

[0169] These pieces of information (5) to (7) correspond to the pieces of information (6) to (8) mentioned above in relation to ST206, respectively.

[0170] The first learning model (a machine learning / deep learning model having an input layer, multiple hidden layers, and an output layer, etc.) and the first decision tree model are each pre-trained using information (1) to information (4) (input data), information (5) to information (7) (output data), and a large number of training data (e.g., tens of thousands or more). Such a first learning model or first decision tree model can perform learning by optimizing the numerous coefficients (or numerous parameters) used in the first learning model or first decision tree model so as to minimize the error between the data output when the input data in each training data is input and the output data in that training data.

[0171] Next, in ST2006, the information processing device 10A can input 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 the following information (8) and information (9) (for example, in the auxiliary storage device 15).

[0172] (8) Information indicating multiple pesticide compositions that constitute a treatment applied to rice seeds directly sown in a specific area (or information indicating multiple pesticide compositions that constitute a treatment applied to rice seeds sown in seedling trays for transplanting seedlings in a specific area) (9) Information showing the amount of each of the multiple pesticide compositions shown in information (8)

[0173] Such second learning models and second decision tree models are each pre-trained using information (5) to information (7) (input data), information (8) to information (9) (output data), and a large number of training data (e.g., tens of thousands or more). Such second learning models or second decision tree models can perform learning by optimizing the numerous coefficients (or numerous parameters) used in the second learning model or second decision tree model to minimize the error between the data output when the input data in each training data is input and the output data in that training data.

[0174] Returning to Figure 10, in ST3000, multiple pesticide compositions determined in ST2000 for a specific area (zone) can be mixed in the determined amounts of each pesticide composition to prepare a pesticide. In one example, the pesticide prepared in this way is applied to rice seeds that are to be directly sown in the specific area (zone). The treated rice seeds are then sown in the specific area (zone). In another example, the pesticide prepared in this way is applied to rice seeds, and the treated rice seeds are sown in seedling trays. After this, seedlings grown in these seedling trays are transplanted to the specific area (zone).

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

[0176] As described above, according to the various embodiments disclosed in this application, the multiple pesticide compositions constituting the agent, and the amount of each pesticide composition, can be determined at any time for an agent to be sprayed on a specific area (zone) included in the area where rice is cultivated. Furthermore, the multiple pesticide compositions constituting the agent, and the amount of each pesticide composition, can be determined at any time for an agent to be applied to rice seeds that are directly sown in a specific area (zone) included in the area, or to rice seeds that are sown in seedling trays for transplanting to this specific area (zone).

[0177] 5. Variations In the example described with reference to Figure 4, the case was described in which ST102 to ST112 can be executed by the same information processing device 10A. However, ST102 to ST112 can be divided and executed by the information processing device 10A mounted on the mobile dispensing 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 (such as another server device) that can be connected to the network 2. In this case, each device that divides the execution of ST102 to ST112 can transmit the necessary information to the device that performs the next process via the network 2. This is similarly applicable to the example described in reference to Figure 11.

[0178] In the example described with reference to Figure 7, the case was described in which ST202 to ST212 can be executed by the same information processing device 10A. However, ST202 to ST212 can be divided and executed by the information processing device 10A mounted on the mobile dispensing 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 (such as another server device) that can be connected to the network 2. In this case, each device that divides the execution of ST202 to ST212 can transmit the necessary information to the device that performs the next process via the network 2. This is similarly applicable to the example described in reference to Figure 12.

[0179] The above has described the case in which a pesticide is prepared by mixing multiple pesticide compositions. However, the technology disclosed in this application is also applicable in the case in which only one pesticide composition is selected from among multiple pesticide compositions, and the pesticide is prepared using only the selected pesticide composition in a determined amount. In this case, for example, the information (9) and information (10) used in the learning model described in ST208 may be modified as follows. (9) Information indicating one pesticide composition that constitutes a pesticide to be sprayed in a specific area (zone). (10) Information indicating the amount of one pesticide composition shown in information (9) Similarly, "information indicating multiple pesticide compositions" used in various learning models can be replaced with "information indicating a single pesticide composition," and "information indicating the amount of each of multiple pesticide compositions" can be replaced with "information indicating the amount of a single 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 that uses such data for each learning dataset.

[0181] As will be readily apparent to those skilled in the art who have an interest in this disclosure, the various examples described above can be appropriately combined with one another in various patterns, insofar as they do not create a contradiction.

[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 herbicidal effects, active ingredients having insecticidal effects, and active ingredients having fungicidal effects; (b1) Anionic dispersant in a concentration of 1-200 g / L; (b2) An anionic dispersant with a concentration of 1-20% w / w.

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

[0184] As described above, by mixing an anionic dispersant in a specific concentration, sufficient dispersibility of the pesticide composition in the field can be ensured. As a result, it is possible to reduce phytotoxicity caused by local application, as well as the environmental burden from the active ingredient and auxiliary components.

[0185] When the pesticide composition is a liquid pesticide composition, the dosage form is, for example, an aqueous suspension or an oily suspension. Specific dosage forms of liquid pesticide compositions include, for example, flowable formulations (SC: suspension concentrate).

[0186] When the pesticide composition is a solid pesticide composition, the dosage form is, for example, a wettable powder and granules. Specific dosage forms of solid pesticide compositions include, for example, water dispersible granules (WG) and floating granules. In one embodiment, the solid pesticide composition is a wettable powder having at least one of self-diffusing and floating properties. Having such properties allows for uniform control of pests, diseases, and weeds within the field where the pesticide is applied.

[0187] (a) Active ingredients The pesticide composition according to the present invention comprises 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 ingredients that have herbicidal effects Examples of active ingredients with herbicidal effects include, but are not limited to, tefuryltrione, triafamone, fentrazamide, clomeprop, oxadiazone, ibufencarbazone, cafenstrole, indanophan, phenoxasulfone, mefenacet, butachlor, pretilachlor, fenquinotrione, benzobicyclon, sulcotrione, mesotrione, pyrazolate, benzofenap, pyrimisulfan, ethoxysulfuron, bensulfuron-methyl, propyrisulfuron, metazosulfuron, penoxulam, oxaziargyl, pyraclonil, pentoxazone, florpyrauxifenbenzyl, phenoxaprop-ethyl, cyhalofop-butyl, metamihop, cyclopyrimorate, dimethametrin, simetrin, bentazone, oxadiclomefone, bromobutide, and tetoflupyrrolimet. The active ingredient having herbicidal effect is at least one selected from the group consisting of these compounds.

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

[0190] (a2) Active ingredients having insecticidal effect Examples of active ingredients having insecticidal effects include, but are not limited to, imidacloprid, thiacloprid, dinotefuran, flupyradiflon, flupyrimine, nitenpyram, clothianidin, sulfoxaflor, ethiprole, fipronil, spinosad, tetraniliprole, chlorantraniliprole, cyantraniliprole, pyrimetrozine, triflumezopyrim, benzpyrimoxane, and oxazosulfyl. 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 multiple active ingredients (a2) having insecticidal effects.

[0191] (a3) Active ingredients that have a bactericidal effect Examples of active ingredients having a fungicidal effect include, but are not limited to, isothianil, probenazole, tricyclazole, diclobentiazox, penflufen, tifluzamide, impulfluxam, kasugamycin, validamycin, fusalide, metminostrobin, azoxystrobin, and pencyclon. 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 multiple active ingredients (a3) ​​having a fungicidal effect.

[0192] In the case of a pesticide composition according to the present invention that contains multiple active ingredients selected from the group consisting of the above-mentioned components (a1), (a2), and (a3), the content specified for the active ingredients is the sum of the content of each active ingredient.

[0193] When the pesticide composition according to 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 active ingredient (a).

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

[0195] (b) Anionic dispersant Examples of anionic dispersants include, but are not limited to, alkylnaphthalene sulfonic acid derivatives and lignin sulfonic acid derivatives. The anionic dispersant may be a synthetic product or a commercially available product. Examples of commercially available alkylnaphthalene sulfonic acid derivatives used as anionic dispersants include Demol SNB, Newcalgen PS-P, Newcalgen WG-101, Newcalgen BX-C, AEROSOL OS, MORWET D425, MORWET IP, SUPRAGIL WP, SUPRAGIL MNS / 90, and TERSPERSE 2020. Examples of commercially available lignin sulfonic acid derivatives used as anionic dispersants include Newcalgen RX-B, Newcalgen 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 aforementioned compounds.

[0196] In the present invention, when the pesticide composition contains multiple anionic dispersants, the specified content for the anionic dispersants is the sum of the content of each anionic dispersant.

[0197] When the pesticide composition according to 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 according to the present invention is a solid pesticide composition, it contains, for example, 1 to 20% w / w, preferably 5 to 20% w / w, and more preferably 5 to 15% w / w of an anionic dispersant.

[0199] In one preferred embodiment, the pesticide composition according to the present invention is designed so that the application rate of the anionic dispersant is 9 to 300 g / ha when applied to a field. By using such an application rate, when the spray solution is applied to a field, especially a paddy field, both when used alone and when mixed, it is possible to impart sufficient diffusion performance to the pesticide composition while avoiding phytotoxicity to rice plants. In a more preferred embodiment, the pesticide composition according to the present invention is designed so that the application rate of the anionic dispersant is 9 to 150 g / ha when applied to a field. In an even more preferred embodiment, the pesticide composition according to the present invention is designed so that the application rate 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 burden caused by the active ingredient and / or auxiliary ingredients. Furthermore, because the auxiliary ingredients used are common, even when multiple pesticide compositions according to the present invention are mixed and applied, the environmental burden caused by using more auxiliary ingredients than necessary can be reduced.

[0201] The pesticide composition according to the present invention may further contain auxiliary components. Examples of auxiliary components include surfactants, antifreezes, thickeners, preservatives, defoamers, phytotoxicity reducers, and carriers. These auxiliary components may be synthetic or commercially available products.

[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 nonionic surfactants include polyoxyethylene-polyoxypropylene block polymers, polyalkylene oxide block polymers, polyoxyalkylene alkylphenyl ethers, polyoxyalkylene fatty acid esters, polyoxyalkylene toristyrylphenyl 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 Surfinol 104, Surfinol 420, Surfinol 440, Newcalgen TG-310, ATLAS G 5000, DOWFAX 100N50, GENAPOL 10500, PLURONIC® F127, PLURONIC® L62, PLURONIC® P105, PLURONIC® PE 6200, PLURONIC® 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 from Toho Chemical Co., Ltd. Examples include T-series products, various SPAN products, Ryoto Sugar Ester products from Mitsubishi Chemical Corporation, DK Ester products from Daiichi Kogyo Seiyaku Co., Ltd., and Leodor SP series products from Kao Corporation.

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

[0204] Specific examples of thickening agents include, for example, Gosenor GL-05, Daiyutan 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 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] Examples of antifoaming agents include silicone oil and calcium stearate. Specific examples of commercially available antifoaming agents include Antifoam E-20, ANTIFOAM 8830 FOOD GRADE, SAG10, SAG30, SAG1 1572, SILCOLAPSE 426R, SILCOLAPSE 432, SILCOLAPSE 454, SILCOLAPSE 482, 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, resin, wax, water, alcohol, organic solvents, mineral oil, and vegetable oil, which are appropriately selected depending on the dosage form of the pesticide composition.

[0208] If the pesticide composition according to 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.), granulation and disintegration improving agents (e.g., anionic surfactants, disintegration improving agents (e.g., anionic surfactants, CMC), disintegration spreading agents (e.g., polyacrylates), etc.).

[0209] Another aspect of the present invention relates to a method for producing a pesticide composition. This method includes mixing an active ingredient (a) and an anionic dispersant (b). Specifically, the method for producing the pesticide composition is: • A first mixing step of mixing the active ingredient (a) and the anionic dispersant (b), • A second mixing step involves adding auxiliary components to the mixture obtained in the first mixing step and mixing them. Includes.

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

[0211] When the pesticide composition according to 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 components, dry grinding, and then adding a separate mixture of a liquid wetting agent and water to the pulverized material, mixing and kneading, granulation, drying, and sieving. For example, when the solid pesticide composition is a granular wettable powder (WG), it can be prepared by conventional methods such as spray drying, fluidized bed granulation, pan granulation, mixing using a high-speed mixer, and extrusion without using solid inert substances.

[0212] In another embodiment, a method for producing the pesticide composition according to the present invention is: • A mixing step of mixing the active ingredient (a) and the anionic dispersant (b), • After the mixing step, a grinding step is performed, • A further mixing step is performed after the grinding step, in which an anionic dispersant (b) is added and mixed. This includes the following. By adding and mixing the anionic dispersant in two separate stages, before and after grinding, the heat generated by grinding the liquid can be avoided, and the product temperature can be lowered. It is preferable to keep the heat generated by grinding below, for example, 10°C. This manufacturing method can be used for both SC and WG. Preferably, this manufacturing method can be used for SC.

[0213] The pesticide composition according to the present invention may be a mixture prepared by mixing multiple 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 multiple pesticide compositions as described above. When mixing multiple pesticide compositions according to the present invention, the auxiliary components used are common, thus reducing the environmental burden caused by using more auxiliary components than necessary.

[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 aforementioned pesticide compositions. More specifically, the invention relates to a method for controlling diseases, pests, and / or weeds, comprising applying one or more of the aforementioned pesticide compositions to a field, wherein 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, and more preferably 9 to 130 g / ha. By using an application rate within this range, sufficient diffusion performance can be imparted to the pesticide composition when the spray solution, both when used alone and when mixed, is sprayed on a field, particularly a paddy field.

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

[0216] Examples of rice pests include, but are not limited to, the following: Hemiptera: Leafhoppers (Nephotettix cincticeps), brown planthoppers (Nilaparvata lugens), small brown planthoppers (Laodelphax striatellus (Fallen)), white-backed planthoppers (Sogatella furcifera (Horvath)), etc. Coleoptera: Rice water beetle (Oulema oryzae (Kuwayama)), rice water weevil (Lissorhoptrus oryzophilus Kuschel), etc.

[0217] Weeds are defined as plants that are undesirable for the growth of crop plants growing in a field, and "weed control" refers to controlling undesirable plants or regulating their growth. In a method of controlling weeds, one or more pesticide compositions according to the present invention are applied to weeds (e.g., harmful plants such as monocotyledonous or dicotyledonous weeds or undesirable crop plants), seeds (e.g., cereals, seeds, or vegetatively propagated parts such as tubers or shoots with buds), or areas where crop plants grow (e.g., areas under cultivation).

[0218] Examples of rice paddy weeds include, but are not limited to, the following: Dicotyledonous plants 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] The pesticide compositions according to the present invention can be used, for example, with respect to the following typical paddy field weeds: Dicotyledonous plants: Rotala indica Koehne, Lindernia procumbens Philcox, Ludwigia prostrata Roxburgh, Potamogeton distinctus A. Benn, Elatine triandra Schk, Oenanthe javanica, Monocotyledonous plants: Barnyard grass (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 pest control method according to the present invention, application may be carried out by any means. For example, it may be carried out by manual spraying, or by automatic spraying using manned or unmanned aircraft and vehicles.

[0221] The present invention relates to a pest control method, and the field is preferably a paddy field. Furthermore, the application is preferably applied to the water surface of the paddy field, and more preferably to the water surface of the paddy field.

[0222] In the pest 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 using an application rate within this range, the pesticide composition can be diffused more efficiently in the field.

[0223] Furthermore, in the pest 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 types of diseases, pests and / or weeds in the field, and the timing of application.

[0224] In the pest control method according to the present invention, the pesticide composition to be applied is automatically selected, for example, based on the prediction of the occurrence of diseases, pests, and / or weeds in the field, and / or their occurrence status, or selected (for example, by a user). There may be one or more pesticide compositions that are automatically selected or chosen. Automatic selection may be performed by the information processing device described above.

[0225] In one embodiment, in the pest control method according to the present invention, the pesticide composition is automatically measured and applied to the field. In another embodiment, multiple pesticide compositions are automatically measured and / or automatically mixed and applied to the field. Automatic measurement and / or automatic mixing may be performed by the aforementioned information processing device 10A and / or the aforementioned mobile spraying device 20. In another embodiment in which the aforementioned information processing device 10A and / or the aforementioned mobile spraying device 20 are not used, the pesticide composition is measured and applied to the field by a user, for example, using an instrument or device, or multiple pesticide compositions are measured and / or mixed by a user, for example, 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 pest control method, the kit comprising the aforementioned pesticide composition and a container. Preferably, the kit comprises • The aforementioned multiple pesticide compositions, • Multiple spatially separated containers The kit includes, with each of the multiple pesticide compositions stored in each of the multiple containers. An example of a kit according to the present invention is the multiple tanks 24 shown in Figure 8. With such a kit, an appropriate pesticide composition can be appropriately selected from multiple candidate pesticide compositions according to the field conditions and applied. Such a kit may be used in combination with the information processing device 10A described above, or it may be incorporated into the mobile spraying device 20 described above.

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

[0228] As described above, the present invention provides a pesticide composition that exhibits excellent diffusivity, particularly excellent diffusivity in water. Therefore, sufficient effects can be achieved even with localized application, and phytotoxicity to crops can be avoided.

[0229] Furthermore, based on the forecasting and occurrence status of diseases, pests, and / or weeds, two or more pesticide compositions can be mixed in appropriate amounts according to the diseases, pests, and / or weeds to be controlled, to obtain a stable diluted solution which can then be applied to the field (e.g., paddy field). By applying the selected pesticide according to the field conditions, it is possible to avoid spraying excessive amounts of chemical substances into the environment. In addition, by combining multiple pesticide compositions, it is possible to provide pesticide compositions with stable physical and / or chemical properties in the diluted solution, as well as pesticide compositions as mixtures that have different physical and chemical properties and can simultaneously control different diseases, pests, and / or weeds. Such pesticide compositions, due to their excellent diffusivity, allow the active ingredients to spread uniformly and widely throughout the field (e.g., paddy field) when mixed and applied, exhibiting insecticidal, fungicidal, and / or herbicidal effects. This allows for the control of diseases, pests, and / or weeds while avoiding the spraying of excessive active ingredients that would be an environmental burden. Furthermore, by applying multiple pesticide compositions within the scope of the present invention, the auxiliary components are common among the pesticide compositions, thus avoiding the use of more auxiliary components than necessary, and thus reducing the environmental burden.

[0230] Furthermore, by combining it with an information processing device and / or a spraying device, it becomes possible to select the type and application rate of pesticides more suitable for the diseases, pests, and / or weeds to be controlled, based on the forecast of the occurrence of diseases, pests, and / or weeds and their occurrence status, thereby achieving even more efficient control.

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

[0232] Based on the ingredients and composition shown in the table below, the pesticide compositions for the examples and comparative examples were prepared by mixing water, the active ingredient, a dispersant, an antifoaming agent, and other ingredients, grinding the mixture, and then separately adding a mixture of water, a thickener, and a preservative. In the examples, "amount applied" corresponds to "amount used." The abbreviations in the table are as follows.

[0233] Flowable formulations (SC: suspension concentrate) were prepared by mixing the active ingredient, anionic dispersant, water, and other components such as an antifoaming agent, grinding the mixture, and then adding a separate mixture of water, a thickener, and a preservative. Water dispersible granules (WG: water dispersible granule) were prepared by mixing the active ingredient, anionic dispersant, a carrier, and other components, dry grinding the mixture, and then adding a separate mixture of a liquid wetting agent and water to the ground material, 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 the pesticide compositions of Comparative Examples 1 and 2, were applied locally to paddy fields at the formulation application rates and dosages shown in the table below. Local application was performed by treating one side of the short edge of a 2m x 13m plot.

[0236] [Table 2-1]

[0237] When the application rate of the anionic dispersant was 4 g / ha (Comparative Example 1) and 0.8 g / ha (Comparative Example 2), compared with 55 g / ha (Example 7) and 9.167 g / ha (Example 8), phytotoxicity at the application site was greater in the case of local application (one-side treatment on the short side in a 2 m × 13 m plot). From these results, it was shown that in a pesticide composition in which the dosage form is a granule wettable powder, at least 9 g / ha or more of an anionic dispersant is required.

[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, the difference in the effect due to dispersibility was tested.

[0239] [Table 2-2]

[0240] As shown by the above results, when containing triafamone as an active ingredient, the pesticide composition containing 0.8 g / ha of a dispersant (Comparative Example 2) had insufficient effect at a location away from the application site, whereas the pesticide composition containing 9.167 g / ha of an anionic dispersant (Example 8) was able to ensure sufficient effect even at a location away 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 locally applied to a paddy field at the formulation application rates and application rates shown in the following table. The local application was performed by one-side treatment on the short side in a 1 m × 15 m 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 at a rate of 0.8 g / ha in the field (Comparative Example 2), phytotoxicity at the application site was greater in localized application (treatment on one side of a 1m x 15m plot) compared to when the application rate was 10 g / ha (Example 2). This result indicates that at least 10 g / ha of anionic dispersant is necessary when applying it to the field.

[0244] Test Example 1D This test investigated the differences in phytotoxicity due to dispersibility when multiple formulations were mixed. A mixture of Example 2 and Example 4, and a mixture of Comparative Example 2 and Comparative Example 3, were applied locally to paddy fields at the formulation application rates and dosages shown in the table below. Local application was performed on one side of a 1m x 15m plot, on the shorter side. 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), phytotoxicity at the application site was greater in localized application (treatment on one side of the short side of a 1m x 15m plot) compared to when the amount was 70 g / ha (Example 2 + Example 4). This result indicates that a total application amount of 8.8 g / ha of anionic dispersant when applied to the field is insufficient.

[0247] Test Example 2 Multiple pesticide compositions were applied to fields using a pesticide spraying drone, and their effectiveness against barnyard grass, Monochoria vaginalis, and bulrush was tested 6 weeks after treatment. This was similarly compared to manual pesticide application by humans. The pesticide compositions used, formulation amounts, application amounts, and dispersant application amounts are shown in the table below. Figure 13 shows the drone spraying flight route with arrows (→) and the effectiveness survey locations with numbers 1-12.

[0248] [Table 3]

[0249] The results above demonstrate that drone spraying can achieve the same effectiveness as manual spraying. Furthermore, similar formulations can be applied to different active ingredients, and no problems occurred when mixing multiple pesticide compositions containing at least one of these active ingredients. In summary, it was demonstrated that sufficient effectiveness can be obtained through various application methods and localized application.

[0250] Test Example 3 When mixing the active ingredient, anionic dispersant, water, and other components such as an antifoaming agent, the relationship between the amount of anionic dispersant added and the temperature of the pulverized liquid during the pulverization process was investigated. Specifically, in the pesticide composition of Example 3, the anionic dispersant was added in two separate amounts before and after pulverization, and the relationship between the amount added and the heat generated was investigated. The results are shown in the table and Figure 14 below.

[0251] [Table 4-1]

[0252] [Table 4-2]

[0253] From the above results, it was shown that by dividing the input of the dispersant at a predetermined input amount, heat generation during the pulverization of the pulverized liquid can be avoided and the product temperature can be lowered. Specifically, it was shown that when the input amount of the anionic dispersant per time is 80 g / L or more, the temperature rises by 10 °C or more after 6 minutes of pulverization.

Explanation of symbols

[0254] 1 Decision system 2 Network 10 Server device 20 Mobile spraying device 21 Driving source 22 Driving mechanism 23 GPS unit 24 Multiple tanks 25 Preparation device 26 Nozzle 30 Terminal device

Claims

1. 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 an active ingredient having an insecticidal effect, an active ingredient having a fungicidal effect, and an active ingredient having a herbicidal effect, (b1) Anionic dispersant in a concentration of 1 to 200 g / L, (b2) An anionic dispersant in a concentration of 1-20% w / w.

2. The pesticide composition according to claim 1, wherein the application rate of the anionic dispersant to the field is designed to be 9 to 300 g / ha.

3. The pesticide composition according to claim 1 or 2, wherein the solid pesticide composition is a wettable powder or granules.

4. The pesticide composition according to any one of claims 1 to 3, wherein the solid pesticide composition is a wettable powder having at least one of self-diffusing and floating properties.

5. The pesticide composition according to claim 1 or 2, wherein the liquid pesticide composition is an aqueous suspension or an oily suspension.

6. The pesticide composition according to any one of claims 1 to 5, wherein the active ingredient having the herbicidal effect is at least one selected from the group consisting of tefuryltrione, triafamone, fentrazamide, clomeprop, oxadiazone, ibufencarbazone, cafenstrole, indanophan, phenoxasulfone, mefenacet, butachlor, pretilachlor, fenquinotrione, benzobicyclon, sulcotrione, mesotrione, pyrazolate, benzofenap, pyrimisulfan, ethoxysulfuron, bensulfuron-methyl, propyrisulfuron, metazosulfuron, penoxulam, oxaziargyl, pyraclonil, pentoxazone, florpyrauxifenbenzyl, phenoxaprop-ethyl, cyhalofop-butyl, metamihop, cyclopyrimorate, dimethametrin, simetrin, bentazon, oxadiclomefone, bromobutide, and tetoflupyrrolimet.

7. The pesticide composition according to any one of claims 1 to 6, wherein the active ingredient having the insecticidal effect is at least one selected from the group consisting of imidacloprid, thiacloprid, dinotefuran, flupyradiflon, flupyrim, nitenpyram, clothianidin, sulfoxaflor, ethiprole, fipronil, spinosad, tetraniliprole, chlorantraniliprole, cyantraniliprole, pyrimetrozine, triflumezopyrim, benzpyrimoxane, and oxazosulfyl.

8. The pesticide composition according to any one of claims 1 to 7, wherein the active ingredient having the bactericidal effect is at least one selected from the group consisting of isothianil, probenazole, tricyclazole, diclobentiazox, penflufen, tifluzamide, impilfluxam, kasugamycin, validamycin, fusalide, metminostrobin, azoxystrobin, and pencyclon.

9. The agrochemical composition according to any one of claims 1 to 8, wherein the anionic dispersant is at least one selected from the group consisting of alkylnaphthalene sulfonic acid derivatives and lignin sulfonic acid derivatives.

10. A method for controlling diseases, pests and / or weeds, comprising applying one or more pesticide compositions according to any one of claims 1 to 9 to a field, A method of pest control in which the anionic dispersant is applied to the field at an application rate of 9 to 300 g / ha.

11. The pest control method according to claim 10, wherein the anionic dispersant is applied to the field at an application rate of 9 to 150 g / ha.

12. The pest control method according to claim 10 or 11, wherein the active ingredient is applied to the field at an application rate of 50 to 650 g / ha.

13. The aforementioned field is a paddy field, The pest control method according to any one of claims 10 to 12, wherein the application is a local application to the water surface of a paddy field.

14. (i-a) Apply the pesticide composition to the field by automatically measuring it, (i-b) Applying multiple pesticide compositions to the field after automatic measurement and / or automatic mixing, (ii-a) The pesticide composition is measured and applied to the field, or (ii-b) Multiple pesticide compositions are measured and / or mixed and applied to the field. A method for controlling pests according to any one of claims 10 to 13.

15. The pest control method according to any one of claims 10 to 14, wherein the pesticide composition is automatically selected or chosen based on the prediction of the occurrence of diseases, pests and / or weeds in the field and / or the current status of such occurrence.

16. A kit comprising a plurality of pesticide compositions according to any one of claims 1 to 9 and a plurality of spatially separated containers for use in the pest control method according to any one of claims 10 to 15, A kit in which each of the aforementioned multiple pesticide compositions is stored in each of the aforementioned multiple containers.

17. Use of the pesticide composition according to any one of claims 1 to 9 for controlling diseases, pests and / or weeds.

18. Use of the pesticide composition according to claim 17, wherein the aforementioned diseases, pests and / or weeds occur in the paddy field.

19. A method for producing the pesticide composition according to any one of claims 1 to 9, A mixing step of mixing the active ingredient and the anionic dispersant, After the mixing step, a grinding step is performed, After the grinding step, a further mixing step is performed in which the anionic dispersant is added and mixed. A manufacturing method that includes this.

20. A method for producing a pesticide composition, comprising mixing a plurality of pesticide compositions according to any one of claims 1 to 9.

21. It comprises at least one processor, The at least one processor, For a specific area included in the area where rice is cultivated, type information indicating at least one type of damage that occurred in the specific area, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area are obtained. Based on the aforementioned type information and quantity information, a plurality of pesticide compositions constituting the agent to be sprayed 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 in such a way.

22. The information processing apparatus according to claim 21, wherein the at least one of the harmful elements includes diseases, pests, and / or weeds.

23. The information processing apparatus according to claim 21 or claim 22, wherein the target area includes at least one field.

24. The aforementioned at least one processor, For each of the multiple unit regions included in the target region, unit type information indicating at least one type of harm that occurred in the unit region, and unit quantity information indicating the amount of each of the at least one harms that occurred in the unit region are obtained. Using the unit type information and the unit quantity information, one zone is determined from the target area that includes multiple unit areas that are common or substantially common in terms of the type of harm that occurred and the amount of harm that occurred. By using the one zone as the specific area, the amount of each of the multiple pesticide compositions that constitute the agent to be sprayed in the one zone is determined. An information processing apparatus according to any one of claims 21 to 23, configured as described above.

25. The aforementioned at least one processor, Using the unit type information and the unit quantity information, a plurality of zones are determined from the target area, each containing a plurality of unit regions, and each zone contains a plurality of unit regions that are common or substantially common in terms of the type of harm and the amount of harm that occurred in each zone. By using each of the multiple zones as the specified area, the multiple pesticide compositions constituting the agent to be sprayed on each of the multiple zones, and the amount of each of the multiple pesticide compositions are determined. The information processing apparatus according to claim 24, configured as described above.

26. The aforementioned at least one processor, In the first trained learning model or the first trained decision tree model, The aforementioned type information and the aforementioned quantity information, At least one of the following: soil information relating to the type of soil in the specified area; pest occurrence information relating to pests that have occurred in the specified area in the past; seed burial information relating to seeds buried in the soil of the specified area; pest spread information relating to the spread of pests resistant to pesticides in the specified area; and weather forecast information relating to weather data predicted for the period during which rice is cultivated in the specified area; By inputting the following, the system obtains information regarding the type and amount of damage that occurs in the specified area, information regarding the timing of the damage's growth, and information regarding phytotoxicity that occurs in the specified area. By inputting the pest occurrence prediction information, the pest growth prediction information, and the phytotoxicity information into a trained second learning model or a trained second decision tree model, the system determines a plurality of pesticide compositions that constitute the pesticide to be sprayed in the specific area, and the amount of each of these plurality of pesticide compositions. An information processing apparatus according to any one of claims 21 to 25, configured as described above.

27. The aforementioned at least one processor, A pre-trained third learning model or a pre-trained third decision tree model, Information regarding the general effect of the drug on the harm indicated by the harm prediction information, Information relating to the types and effects of drugs previously used in the specified area, information relating to the soil composition of the specified area, and information relating to the water retention capacity of the specified area, at least one of these, By inputting this information, the system determines the number of pesticide compositions that constitute the agent to be sprayed in the specified area, and the amount of each of these pesticide compositions. The information processing apparatus according to claim 26, configured as described above.

28. A dispensing device comprising an information processing device according to any one of claims 1 to 7, A drive source and drive mechanism for moving the spraying device, A plurality of tanks, each configured to contain a pesticide composition specific to that tank from among the plurality of pesticide compositions, A preparation device is configured to be connected to a plurality of tanks, measure and obtain the determined amount of pesticide composition contained in each selected tank from a plurality of selected tanks among the plurality of tanks, and mix each pesticide composition to prepare a drug, A nozzle connected to the preparation device and configured to spray the chemical prepared by the preparation device, A spraying device equipped with the following.

29. The aforementioned at least one processor, First position information indicating the location of the specified region is acquired, Second position information indicating the current location of the sprayer is acquired, When the difference between the position of the specific region indicated by the first position information and the current position indicated by the second position information is less than or equal to a threshold, a signal is transmitted from the nozzle indicating that the drug prepared by the preparation device should be released. The spraying device according to claim 28, configured as described above.

30. The drug prepared by the preparation apparatus comprises an anionic dispersant having a concentration of 1 to 200 g / L or 1 to 20% w / w. The spraying apparatus according to claim 28 or claim 29, wherein the anionic dispersant is sprayed at an application rate of 9 to 300 g / ha.

31. A method performed by at least one processor that executes instructions readable by a computer, A step of obtaining type information indicating at least one type of damage that occurred in a specific area included in the area where rice is cultivated, and quantity information indicating the amount of each of the at least one type of damage that occurred in the specific area. A step of determining, based on the type information and the quantity information, a plurality of pesticide compositions constituting the agent to be sprayed in the specific area, and the amount of each of the plurality of pesticide compositions, A method characterized by including the following.

32. By being executed by at least one processor, For a specific area included in the area where rice is cultivated, type information indicating at least one type of damage that occurred in the specific area, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area are obtained. Based on the aforementioned type information and quantity information, a plurality of pesticide compositions constituting the agent to be sprayed in the specific area, and the amount of each of the plurality of pesticide compositions are determined. A computer program characterized by causing the at least one processor to function as described above.

33. It comprises at least one processor, The at least one processor, For a specific area included in the target area where rice seeds are directly sown or seedlings grown from seedling trays into which rice seeds have been sown are transplanted, type information indicating at least one type of damage that occurred in the specific area, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area are obtained. Based on the aforementioned type information and quantity information, a plurality of pesticide compositions comprising a chemical agent to be applied to rice seeds to be directly sown in the specified area or rice seeds to be sown in seedling trays for transplanting seedlings to the specified area, and the amount of each of the plurality of pesticide compositions are determined. An information processing device characterized by being configured in such a way.

34. The aforementioned at least one processor, For each of the multiple unit regions included in the target region, unit type information indicating at least one type of harm that occurred in the unit region, and unit quantity information indicating the amount of each of the at least one harms that occurred in the unit region are obtained. Using the unit type information and the unit quantity information, one zone is determined from the target area that includes multiple unit areas that are common or substantially common in terms of the type of harm that occurred and the amount of harm that occurred. By using the one zone as the specific area, a plurality of pesticide compositions that constitute the agent to be applied to the rice seeds directly sown in the one zone or the rice seeds sown in the seedling tray, and the amount of each of the plurality of pesticide compositions are determined. The information processing apparatus according to claim 33, configured as described above.

35. The aforementioned at least one processor, Using the unit type information and the unit quantity information, a plurality of zones are determined from the target area, each containing a plurality of unit regions, and each zone contains a plurality of unit regions that are common or substantially common in terms of the type of harm and the amount of harm that occurred in each zone. By using each of the multiple zones as the specified area, a plurality of pesticide compositions comprising the agent to be applied to the rice seeds directly sown in each of the multiple zones or the rice seeds sown in the seedling trays, and the amount of each of the plurality of pesticide compositions is determined. The information processing apparatus according to claim 34, configured as described above.

36. The aforementioned at least one processor, In the first trained learning model or the first trained decision tree model, At least one of the following: soil information relating to the type of soil in the specified area; pest occurrence information relating to pests that have occurred in the specified area in the past; pest spread information relating to the spread of pests resistant to pesticides in the specified area; and weather forecast information relating to weather data predicted for the period during which rice is cultivated in the specified area. By inputting the following, the system obtains information regarding the type and amount of damage that occurs in the specified area, information regarding the timing of the damage's growth, and information regarding phytotoxicity that occurs in the specified area. A second trained learning model or a second trained decision tree model, By inputting the aforementioned pest occurrence prediction information, the pest growth prediction information, and the phytotoxicity information, the system determines a plurality of pesticide compositions that constitute the agents to be applied to the rice seeds directly sown in the specified area or the rice seeds sown in the seedling trays, and the amount of each of the plurality of pesticide compositions. An information processing apparatus according to any one of claims 33 to 35, configured as described above.

37. A method performed by at least one processor that executes instructions readable by a computer, A step of obtaining type information indicating at least one type of damage that occurred in a specific area included in a target area where rice seeds are directly sown or seeds grown from seedling trays into which rice seeds have been sown are transplanted, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area. A step of determining, based on the type information and the quantity information, a plurality of pesticide compositions that constitute a chemical agent to be applied to rice seeds to be directly sown in the specific area or rice seeds to be sown in seedling trays for transplanting seedlings in the specific area, and the amount of each of the plurality of pesticide compositions, A method characterized by including the following.

38. By being executed by at least one processor, For a specific area included in the target area where rice seeds are directly sown or seedlings grown from seedling trays into which rice seeds have been sown are transplanted, type information indicating at least one type of damage that occurred in the specific area, and quantity information indicating the amount of each of the at least one damages that occurred in the specific area are obtained. Based on the aforementioned type information and quantity information, a plurality of pesticide compositions comprising a chemical agent to be applied to rice seeds to be directly sown in the specified area or rice seeds to be sown in seedling trays for transplanting seedlings to the specified area, and the amount of each of the plurality of pesticide compositions are determined. A computer program characterized by causing the at least one processor to function as described above.

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