Information processing system, information processing device, and information processing method

Abstract

To determine a treatment according to the stage of disease in a plant.SOLUTION: An information treatment system is a system for monitoring a plant, and includes a memory unit and a control unit. The memory unit stores treatment information regarding treatments to be taken according to multiple stages of disease progression of the plant, and a learning model that has been trained on the characteristics of each sensing data obtained by remote sensing the plant at multiple stages of disease progression. The control unit acquires image data based on first sensing information obtained by capturing an image of the plant with a spectral camera or RGB camera, which is first sensing means possessed by a remote sensing device, and determines the stage of disease progression of the plant in the image data based on the acquired image data and the learning model. The control unit also determines the treatment to be taken according to the determined stage of disease progression based on the determined stage of disease progression and the stored treatment information, and generates treatment proposal information that proposes the treatment.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to an information processing system and an information processing method for monitoring the progress of plant diseases.

Background Art

[0002] Conventionally, there has been a technique for diagnosing the disease (infection with a pathogen) of a tree based on an image of the tree. For example, in Patent Document 1 below, an image of a tree (Paragonimus tree) to be diagnosed is obtained, and information indicating the image of the tree, the range in which the leaf group is shown in the image, and whether or not the range has characteristics of disease (infection with root white rot disease) are used as teacher data. By inputting an image of the tree to be diagnosed into a diagnostic model that has performed machine learning to diagnose a tree including diseased leaf groups as a diseased tree, a tree including diseased leaf groups is diagnosed as a diseased tree, and the result of the diagnosis is output.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, with the technique of Patent Document 1 above, it is only possible to diagnose the presence or absence of a tree's disease, and it is not possible to diagnose the degree to which the infection with the pathogen has progressed. Therefore, it is not possible to take measures according to the degree of infection.

[0005] The problem of the present invention relates to determining measures according to the stage of progression of a plant disease.

Means for Solving the Problems

[0006] An information processing system according to one embodiment of the present invention is an information processing system for monitoring plants, and comprises a storage unit and a control unit. This information processing system may be implemented by a single device or by multiple devices. The memory unit stores information on treatments to be taken according to multiple stages of the progression of the plant disease, and a learning model that has learned the characteristics of each sensing data obtained by remotely sensing the plant at multiple stages of the progression of the disease. The control unit acquires image data based on first sensing information obtained by imaging the plant with a spectral camera or RGB camera, which is a first sensing means of the remote sensing device, and determines the stage of disease progression of the plant in the image data based on the acquired image data and the learning model. Furthermore, the control unit determines the action to be taken according to the determined progress stage and the stored action information, and generates action suggestion information proposing the action.

[0007] An information processing method according to one embodiment of the present invention is an information processing method for monitoring plants, The system stores information on treatments to be taken according to multiple stages of the progression of the disease in the plant, and a learning model that has learned the characteristics of each sensing data obtained by remotely sensing the plant at multiple stages of the progression of the disease. Image data based on first sensing information obtained by imaging the plant with a spectral camera or RGB camera, which is a first sensing means of the remote sensing device, is acquired, and the progression stage of the plant disease in the image data is determined based on the acquired image data and the learning model. This includes determining the action to be taken according to the determined progression stage and the stored action information, and generating action proposal information that proposes the action. [Effects of the Invention]

[0008] According to the present invention, it is possible to determine treatment according to the stage of progression of a plant disease. [Brief explanation of the drawing]

[0009] [Figure 1] This diagram shows the configuration of a tree infection monitoring system according to one embodiment of the present invention. [Figure 2] This diagram shows the hardware configuration of a monitoring server according to one embodiment of the present invention. [Figure 3] This diagram shows the configuration of the database of a monitoring server according to one embodiment of the present invention. [Figure 4] This flowchart shows the flow of the learning model generation process by a monitoring server according to one embodiment of the present invention. [Figure 5] This is a flowchart showing the flow of the tree treatment method determination process by a monitoring server according to one embodiment of the present invention. [Figure 6] This table shows examples of tree treatment methods according to the stage of infection progression determined in one embodiment of the present invention. [Figure 7] This figure shows an example of a progress map generated in one embodiment of the present invention. [Figure 8] This figure shows an example in which suggested action information is displayed when a region of the generated progress map is selected by the user in one embodiment of the present invention. [Figure 9] This figure shows an example of a treatment map generated in one embodiment of the present invention. [Figure 10] This table shows examples of tree treatment methods according to the stage of infection progression determined in other embodiments of the present invention. [Figure 11] This figure shows an example of a progress map generated in another embodiment of the present invention. [Figure 12] This figure shows an example of a treatment map generated in another embodiment of the present invention. [Modes for carrying out the invention]

[0010] Hereinafter, embodiments of the present invention will be described while referring to the drawings.

[0011] [Configuration of the System] As shown in FIG. 1, this tree infection monitoring system includes a monitoring server 100 on the Internet 50, a user terminal 200, and a proposer terminal 300.

[0012] This system monitors the infection status of the pathogens (Ganoderma) of the trees (palm trees) in the farm, determines the stage of progression (infection stage) of the infection for the infected palm trees, and proposes a method for treating (treating or preventing) Ganoderma according to the infection stage. Here, palm trees include oil palms, coconut palms, etc.

[0013] The monitoring server 100 is a server (information processing device) operated by the operator of the above monitoring service. The monitoring server 100 is connected to the user terminal 200 and the proposer terminal 300 via the Internet 5 and 0.

[0014] The user terminal 200 is a terminal used by the owner or administrator (user) of the above farm, and is, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, etc.

[0015] The owner or administrator (user) of the farm requests the monitoring service provider for the monitoring service of his own farm, and the user terminal 200 receives the monitoring results from the monitoring server 100, that is, information on the infection stage of Ganoderma of each palm tree in his own farm and information on the corresponding treatment (treatment or prevention).

[0016] In response to a monitoring request from the user terminal 200, the monitoring server 100 will, for example, periodically (e.g., twice a year), use a remote sensing device to image the target area (all or part) of the user's farm from above or the ground.

[0017] As shown in the figure, the remote sensing device uses at least one of three components: a ground-based mobile device, a low-altitude flying object, or a low-Earth orbit microsatellite, and is equipped with sensing means. Preferably, a low-altitude flying object is used.

[0018] A remote sensing device is a small device that, in addition to sensing means such as optical RGB cameras, spectral cameras, radar, spectral sensors, and temperature sensors described later, also has basic mobile device means such as communication functions, GPS (Global Positioning System), and acceleration sensors, and is a terminal that can photograph objects such as plants at a specific wavelength.

[0019] Low-altitude flying objects include unmanned aerial vehicles (primarily drones), such as multirotors and fixed-wing aircraft, but also manned aircraft such as helicopters and gliders, and are equipped with the aforementioned sensing means / devices and basic means / devices for movement.

[0020] Low Earth orbit microsatellites are artificial satellites weighing less than 100 kg that orbit at high speed in low Earth orbit at an altitude of approximately 400-600 km above the ground, and are equipped with the aforementioned sensing means / devices and basic means / devices for mobile operation.

[0021] The ground-based mobile device is a device that can move on or near the ground, such as a mobile robot or an unmanned vehicle, and is equipped with the aforementioned sensing means / device and basic mobile device means / device.

[0022] The remote sensing device includes an optical RGB camera or a spectral camera as sensing means / device, and acquires first sensing data. The first sensing data may include reflected wavelengths, and an image or image information can be synthesized based on this.

[0023] The remote sensing device preferably further includes a second sensing means or device, at least one of various sensors such as radar systems (LiDAR, SAR, etc.) and temperature sensors (infrared temperature sensors), and it is preferable to obtain second sensing data using the second sensing means or device. It is preferable that the aforementioned first sensing data acquisition means / device and second sensing data acquisition means / device be used in combination, from the viewpoint of improving the detection accuracy of the initial infection stage of a disease. Here, each sensing means may be an integrated device or separate. In this embodiment, measurements are performed using one of the spectral camera and RGB camera (preferably a spectral camera), and at least one other type of sensor (for example, a temperature sensor or a radar system sensor).

[0024] By using second sensing data such as temperature sensors or radar, highly accurate information on temperature or plant morphology can be acquired and received, and by using this in conjunction with image information acquired and received by the first sensing, the accuracy of determining the infection stage can be improved. For example, by detecting changes in the temperature of palm trees due to Ganoderma infection, or slight changes in the morphology of palm trees due to infection, such as changes in leaf angle or curvature, it is possible to determine whether or not an infection is present and the stage of infection.

[0025] The monitoring server 100 acquires sensing data (spectral images and / or RGB images, and other sensing data) from the remote sensing device, and determines the stage of Ganoderma infection of each palm tree in the data based on the acquired image data and other sensing data, and a learning model that has learned the characteristics of each sensing data obtained by remote sensing palm trees at multiple infection stages of Ganoderma.

[0026] The monitoring server 100 then determines the appropriate action for each infection stage based on the action information received from the proposer terminal 300, generates action suggestion information (infection stage map) that notifies the user of the infection status and proposes the action to the user, and sends it to the user terminal 200.

[0027] The proposer terminal 300 is a terminal used by businesses that propose methods for dealing with the Ganoderma infection described above, and is, for example, a smartphone, mobile phone, tablet PC, notebook PC, or desktop PC. The proposer terminal 300 devises and calculates information on the measures to be taken according to the stage of Ganoderma infection, typically information on treatment actions such as spraying, injecting into trees, irrigation, fertilizer application, and microbial application, information on additives such as pesticides, adjuvants, fertilizers, fungicides, and microorganisms used in these treatments, more specifically, information on the type of additive, the amount to be added (per tree / treatment), the concentration of the additive, and the frequency of treatment, and transmits this information to the monitoring server 100.

[0028] [Monitoring Server Hardware Configuration] As shown in Figure 2, the monitoring server 100 includes a CPU (Central Processing Unit) 11, ROM (Read Only Memory) 12, RAM (Random Access Memory) 13, an input / output interface 15, and a bus 14 connecting these components to each other.

[0029] The CPU 11 accesses RAM 13 and other memory as needed, performing various calculations and comprehensively controlling all blocks of the monitoring server 100. ROM 12 is a non-volatile memory in which the OS, programs, and firmware such as various parameters to be executed by the CPU 11 are permanently stored. RAM 13 is used as a working area for the CPU 11 and temporarily holds the OS, various running applications, and various data being processed.

[0030] The input / output interface 15 is connected to a display unit 16, an operation reception unit 17, a storage unit 18, a communication unit 19, and the like.

[0031] The display unit 16 is a display device that uses, for example, an LCD (Liquid Crystal Display), an OLED (Organic ElectroLuminescence Display), or a CRT (Cathode Ray Tube).

[0032] The operation reception unit 17 is, for example, a pointing device such as a mouse, a keyboard, a touch panel, or other input device. If the operation reception unit 17 is a touch panel, the touch panel may be integrated with the display unit 16.

[0033] The storage unit 18 is a non-volatile memory such as an HDD (Hard Disk Drive), flash memory (SSD; Solid State Drive), or other solid-state memory. The OS, various applications, and various data are stored in this storage unit 18.

[0034] As will be described later, in this embodiment in particular, the storage unit 18 has a farm information database, a monitoring information database, and a treatment information database, in addition to programs such as applications that execute each step of the tree treatment method determination process described later. This program can be implemented as a program product.

[0035] The communication unit 19 is, for example, a NIC (Network Interface Card) for Ethernet or various modules for wireless communication such as wireless LAN, and is responsible for communication processing between the user terminal 200 and the proposer terminal 300.

[0036] Although not shown in the diagram, the basic hardware configuration of the user terminal 200 and the proposer terminal 300 is substantially the same as that of the monitoring server 100, and each has the basic configuration necessary to function as a computer, including a CPU, memory unit, communication unit, etc.

[0037] [Database configuration of the monitoring server] As shown in Figure 3, the monitoring server 100 has a farm information database 31, a monitoring information database 32, and a treatment information database 33 in its storage unit 18. Note that these databases may be stored in a storage device or server externally connected to the monitoring server 100, rather than in the storage unit 18.

[0038] The farm information database 31 stores information about the user's farm, including basic information such as the farm's name and location, information about the palm trees currently growing or in the past such as the type (seedling variety), age, generation, and height, and information about the palm tree growing environment such as the soil type and terrain type (slope) in which the palm trees grow, for each farm by receiving this information from, for example, the user terminal 200.

[0039] If the farm information database 31 is missing some information regarding palm trees on a farm, the monitoring server 100 can calculate and fill in the missing information based on sensing information as needed. For example, height can be calculated from image information, and age can be calculated from height and type. The slope of the land can also be calculated using radar sensors. Missing information may occur when the user has not entered the data. This information, along with the aforementioned image data and other sensing data, can be included in the training data of a learning model that has learned the characteristics of each sensing data obtained by remotely sensing palm trees at multiple stages of Ganoderma infection. This learning model can then be used to determine the stage of Ganoderma infection for each palm tree.

[0040] The monitoring information database 32 stores information regarding monitoring (sensing and analysis) for each farm, specifically the monitoring schedule (observation date), the method of execution (information regarding remote sensing equipment, etc.), the target area (location information), and the monitoring results (image data and other sensing data captured, information regarding the infection stage of Ganoderma on each palm tree determined from the sensing data, and information regarding the infection stage of Ganoderma on palm trees in the vicinity of each palm tree).

[0041] The treatment information database 33 stores information on treatments (cures and preventions) decided and proposed based on the above monitoring results, namely, the type and efficacy of the agents (pesticides, adjuvants, fertilizers) used in the treatment, information on the method of use of the agents (amount and frequency of spraying on the surface of the tree trunk, injection into the inside of the tree trunk, soil irrigation, etc.), history information of past treatments, information on the degree of improvement since the treatment at the first and previous monitoring, and data on the infection stage map transmitted to the user terminal 200 according to the above infection stage.

[0042] Of these, information regarding treatment may be stored in the treatment information database 33 when the monitoring server 100 transmits information regarding the infection stage of the palm tree determined from the monitoring results to the proposer terminal 200, and the proposer terminal 200 receives treatment information corresponding to the infection stage.

[0043] These databases are referenced and used as needed in the tree treatment method determination process by the tree infection monitoring system described later.

[0044] In addition to these databases, the monitoring server 100 stores a learning model for determining the infection stage of palm trees from sensing data (described later).

[0045] [Operation of the tree infection monitoring system] Next, the operation of the tree infection monitoring system configured as described above will be explained. This operation is performed through the cooperation of the hardware, such as the CPU 11 and communication unit 19 of the monitoring server 100, and the software stored in the storage unit 18. For convenience, in the following explanation, the CPU 11 of the monitoring server 100 will be considered the main operator.

[0046] First, we will explain the learning model generation process performed by the monitoring server 100. Figure 4 is a flowchart showing the flow of this learning model generation process.

[0047] As shown in the figure, when the CPU 11 of the monitoring server 100 acquires sensing data remotely sensed from multiple different palm oil plantations from a microsatellite, aerial vehicle, and ground terminal, it performs preprocessing on this data for the generation of a learning model (step 41).

[0048] Specifically, this involves correcting distortion in acquired image data (spectral images or RGB images) and correcting wavelength shifts between images so that multiple spectral images overlap at the same wavelength.

[0049] Next, CPU 11 sets the explanatory and target variables for machine learning based on the data after the above preprocessing (step 42). Specifically, it sets the preprocessed sensing data and data such as the location, age, type, generation, and soil type of palm trees received from the user terminal 200 as explanatory variables for machine learning, and sets the infection stage of the disease affecting the palm trees (for example, infection stages 0 to 4) as the target variable for machine learning (classification problem).

[0050] Next, CPU 11 generates training data and validation data from the pre-processed dataset (step 43). Specifically, it generates various training data with different weights and biases corresponding to the disease infection stage, as well as validation data used to evaluate the performance of the hyperparameters that define the behavior of the machine learning algorithm.

[0051] For example, as the infection of a diseased tree progresses, the area that can be determined from the spread of leaves viewed from above, the maximum diameter of a circle that can be drawn by connecting the tips of the leaves, or the leaf area itself tend to decrease, the temperature tends to change, the leaf color tends to change, and the spectrum also differs for each infection stage. Therefore, the CPU 11 extracts data with different values ​​for these items from the sensing data mentioned above and generates training data with parameters corresponding to each infection stage.

[0052] Furthermore, validation data is used to determine hyperparameters related to the structure and configuration of the model (for example, numerical values ​​such as the number of layers in a neural network and the learning rate) and to select a model that is expected to perform well.

[0053] Furthermore, the datasets other than the training and validation data after preprocessing are retained as test data to test the generality of the trained model.

[0054] Next, CPU 11 constructs a learning model based on the explanatory and target variables, and trains the learning model based on the training and validation data (step 44).

[0055] The CPU 11 then evaluates the generality of the trained model using the test data (step 45). By comparing the evaluation results of different trained models, it selects and generates the optimal machine learning model that describes the relationship between the explanatory variables and the target variable.

[0056] Here, machine learning algorithms such as logistic regression, random forest, boosting, and SVM (Support Vector Machine), which are suitable for image processing, may be used. Although the above examples show supervised learning, unsupervised learning is also acceptable. Furthermore, algorithms such as CNN (Convolutional Neural Network), VAE (Variational Autoencoder), GAN (Generative Adversarial Network) and deep learning may also be used.

[0057] Next, we will explain the process for determining the tree treatment method using the learning model generated above. Figure 5 is a flowchart showing the flow of the tree treatment method determination process by the monitoring server 100.

[0058] As shown in the figure, first, the CPU 11 of the monitoring server 100 determines whether or not it has received a request from the user terminal 200 to check the infection status of the palm tree Ganoderma (step 51). The infection status check request includes location information of the area in the user's farm where the palm tree is growing and whose infection status the user wishes to check. The infection status check request may also include at least one piece of information from the growth information and environmental information of the palm tree whose infection status the user wishes to check. Examples of growth information include seedling variety, age, generation, and tree height, while examples of environmental information include the soil type and topography type in which the plant grows.

[0059] Next, the CPU 11 transmits instruction information to the remote sensing device based on the received location information (step 52). The instruction information includes the location information, as well as information such as the sensing date and time, and the sensing method (type of sensor to be used). If multiple remote sensing devices are used, the instruction information is transmitted to each of them.

[0060] Next, the CPU 11 determines whether or not it has received sensing data from the remote sensing device (step 53).

[0061] If the CPU determines that sensing data has been received (Yes in step 53), it uses the learning model described above to determine the stage of infection of each palm tree with Ganoderma, based on the sensing data and information about the palm tree (or farm) to be checked for infection (step 54).

[0062] Next, the CPU 11 refers to the treatment information database 33 and determines the treatment (treatment method, prevention method) to be taken for each palm tree according to the determined infection stage (step 55).

[0063] Figure 6 is a table showing examples of tree treatment methods according to the progression stage in this embodiment. As shown in the figure, in this embodiment, the infection stages of Ganoderma are set and determined by the learning model described above as follows: infection stage 0 (healthy), infection stage 1 (early infection), infection stage 2 (mid-stage infection), infection stage 3 (severe infection), and infection stage 4 (very severe infection). Note that the infection stages of plant diseases vary depending on the plant, and standardized standards established by government agencies or academic research institutions in the country where the plant grows may be used.

[0064] A typical example of treatment in this embodiment is the addition of pesticides and adjuvants (pesticide efficacy enhancers) to increase the effectiveness of the pesticides. In this embodiment, the number of treatments is increased as the infection stage becomes more advanced, and the use of adjuvants together with pesticides as additives is set, with their concentrations being set to be higher.

[0065] Palm trees at infection stage 0 are healthy trees surrounding infected trees at infection stage 1 or higher. As shown in the figure, as a preventative measure, these palm trees at infection stage 0 are sprayed on the surface of their trunks with an aqueous solution of pesticide and adjuvant (pesticide alone may be used as they are healthy trees). The aqueous solution refers to a dispersion with water as the solvent and may also be referred to as the treatment solution. Hereafter, adding pesticides or adjuvants can be rephrased as adding these treatment solutions.

[0066] For example, hexaconazole is used as a pesticide, with an additive concentration of 0.1%. As an adjuvant, Kao Adjuvant A-134 is used, with an additive concentration of 0.1%. The total amount of both added is 2L, and the treatment is performed at least once a year.

[0067] Palm trees in infection stage 1 show white buttons at their base, and symptoms on the leaves are difficult to discern with the naked eye. As shown in the figure, for palm trees in infection stage 1, treatment stage 1 is proposed, which involves spraying an aqueous solution of pesticide and adjuvant onto the surface of the trunk, injecting the same solution into the trunk, and irrigating the soil. Furthermore, the addition of microorganisms is also proposed.

[0068] Whether the treatment involves spraying the surface of the tree trunk, injecting it into the trunk, or irrigating the soil, hexaconazole is used as the pesticide and Kao Adjuvant A-134 as the adjuvant. The concentration of the pesticide added is 0.1-0.3% in all treatments, and the concentration of the adjuvant added is 1.0% for spraying the surface of the tree trunk and injecting it into the trunk, and 0.1-1.0% for irrigating the soil. The total amount of both added is 2L for spraying the surface of the tree trunk and injecting it into the trunk, and 20L applied to the base of the tree for irrigating the soil. The number of treatments is at least twice a year for all treatments.

[0069] For microbial supplementation, fertilizers containing microorganisms (microorganism types: Trichoderma, Hendersonia, Actinomycetes, etc.) are used. When using a fertilizer such as Bio-fertilizer (Gano EF), the recommended concentration and frequency of application are 50g / tree three times / year for young trees, 500g / hole once / hole when replanting trees, 2kg / tree once / year for trees under 4 years old, and 4kg / tree once / year for trees 4 years or older.

[0070] Palm trees in infection stage 2 exhibit white buttons and fungi at the base of the tree, show less than 30% decay, and have yellowing leaves; these are all factors considered in the overall assessment. As shown in the figure, for palm trees in infection stage 2, treatment stage 2 is proposed, which includes spraying an aqueous solution of pesticide and adjuvant onto the surface of the trunk, injecting the same solution into the trunk, and irrigating the soil. Furthermore, the addition of microorganisms is also proposed.

[0071] In treatment stage 2, whether the treatment involves spraying the trunk surface, injecting into the trunk, or irrigating the soil, the pesticides and adjuvants used are the same as those for palm in infection stage 1 above, but the amount added is proposed to be increased, and the number of treatments is increased to a minimum of 4 times per year for all treatments. In addition, the addition of microorganisms will be carried out using the same drugs, concentrations, and frequency as for palm trees in treatment stage 1 above.

[0072] Infection stage 3 palm trees show white buttons and fungi at the base of the tree, and can be confirmed by observing less than 50% decay and leaf breakage. As shown in the figure, for the infection stage 2 palm trees, treatment stage 3 is proposed, which involves spraying an aqueous solution of pesticide and adjuvant onto the surface of the trunk, injecting the same solution into the trunk, and irrigating the soil. Furthermore, the addition of microorganisms is also proposed.

[0073] In treatment stage 3, whether the treatment involves spraying the trunk surface, injecting into the trunk, or irrigating the soil, the pesticides and adjuvants used are the same as those used for palm trees in infection stages 1 and 2. However, an increase in the amount of additive is proposed, and the number of treatments is increased to a minimum of 6 times per year for all treatments. In addition, the addition of microorganisms will be carried out with the same drugs, concentrations, and frequency as those used for palm trees in treatment stages 1 and 2.

[0074] Palm trees in infection stage 4 are those that have collapsed or died. As shown in the figure, treatment stage 4 involves felling, replanting of the palm trees, soil replacement, and addition of microorganisms.

[0075] The microorganisms used are the same as those used in treatment stages 1 to 3 described above, and the amount added and the number of treatments are 500g / hole once per hole.

[0076] In this embodiment, examples of pesticide active ingredients that can be used in pesticides include those listed in the 2011 edition of the Pesticide Handbook (Japan Plant Protection Association), but are not limited to these. Furthermore, the pesticide efficacy enhancer composition of the present invention can be used safely on various crops without causing phytotoxicity. In this embodiment, as described above, the fungicide hexaconazole ((RS)-2-(2,4-dichlorophenyl)-1-(1H-1,2,4-triazole-1-yl)hexane-2-ol) is used, but is not limited to this, and the following may also be used.

[0077] Other fungicides include sulfur-based zineb (zinc ethylene bisdithiocarbamate), maneb (manganese ethylene bisdithiocarbamate), manzeb (zinc ion-coordinated manganese ethylene bisdithiocarbamate), polycarbamate (bisdimethyldithiocarbamoyl zinc ethylene bisdithiocarbamate), and benomyl (methyl-1-(butylcarbamoyl)-2-benzimidazole carbamate) as a benzimidazole-based agent. , thiophanate-methyl (1,2-bis(3-methoxycarbonyl·2-thioureido)benzene), dicarboximide-based vinclozoline (3-(3,5-dichlorophenyl)-5-methyl-5-vinyl-1,3-oxazolidine-2,4-dione), iprodione (3-(3,5-dichlorophenyl)-N-isopropyl-2,4-dioxoimidazolidine-1-carboxamide), procymidone (N-(3,5-di Chlorophenyl)-1,2-dimethylcyclopropane-1,2-dicarboximide), also triazine (2,4-dichloro-6-(2-chloroanilino)-1,3,5-triazine), trifumizole ((E)-4-chloro-α,α,α-trifluoro-N-(1-imidazole-1-yl-2-propoxyethylidan)-o-toluidine), iminoctadine acetate (1,1-iminodi(octamethylene)diguanidi (Umtriacetate), organocopper (Oxine-copper), cupric hydroxide (Kocide Bordeaux, etc.), antibiotic fungicides (streptomycin, tetracycline, polyoxy, blastocydin S, kasugamycin, validamycin), triadimephon (1-(4-chlorophenoxy)-3,3-dimethyl-1-(1,2,4-triazole-1-yl)-2-butanone), isoprothiolane (diisopropyl) Examples include (1,3-dithiolan-2-ylidenemalonate), TPN (tetrachloroisophthalonitrile), etc., and preferred examples include organocopper (Oxine-copper), cupric hydroxide, trifumizole ((E)-4-chloro-α,α,α-trifluoro-N-(1-imidazole-1-yl-2-propoxyethylidan)-o-toluidine), iprodione (3-(3,5-dichlorophenyl)-N-isopropyl-2,Examples include 4-dioxoimidazolidine-1-carboxamide) and hexaconazole ((RS)-2-(2,4-dichlorophenyl)-1-(1H-1,2,4-triazole-1-yl)hexane-2-ol), and two or more of these may be combined.

[0078] In the case of insecticides, pyrethroid insecticides include permethrin ((3-phenoxybenzyl=(1RS,3RS)-(1RS,3RS)-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylate), cyvermethrin ((RS)-α-cyano-3-phenoxybenzyl=(1RS,3RS)-(1RS,3RS)-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylate), and fenvalereate (α-cyano-3-phenoxybenzyl-2-(4-chlorophenyl)-3-methylbutanoate). Organophosphorus insecticides include DDVP (dimethyl 2,2-dichlorovinyl phosphate) and Sumithion (MEP). (O,O-dimethyl-O-(3-methyl-4-nitrophenyl)thiophosphate), Malathion (S-[1,2-bis(ethoxycarbonyl)ethyl]dimethylphosphorothiol thionate), Dimethoate (dimethyl S-(N-methylcarbamoylmethyl)dithiophosphate), Elsan (S-[α-(ethoxycarbonyl)benzyl]dimethylphosphorothiol thionate), Byjit (O,O-dimethyl-O-(3-methyl-4-methylthiophenylthiophosphate)), Carbamate insecticides include Bassa (O-sec-butylphenyl methylcarbamate), MTMC (m-tolylmethylcarbamate), Meopal (3,4-dimethylphenyl-N-methylcarbamate), Methomyl (S-methyl-N Examples include (methylcarbamoyl)oxythioacetimide, and preferred examples include permethrin, DDVP (dimethyl 2,2-dichlorovinyl phosphate), and methomyl (S-methyl-N[(methylcarbamoyl)oxy]thioacetimide).

[0079] Furthermore, natural insecticides include pyrethrins derived from pyrethrum, piperonyl butoxides, rotenones derived from the leguminous shrub Delis, and nicotinic agents (3-(1-methyl-2-pyrrolidinyl)pyridine sulfate). Insect growth regulators (IGRs) include diflubenzuron (1-(4-chlorophenyl)-3-(2,6-difluorobenzoyl)urea), teflubenzuron (1-(3,5-dichloro-2,4-difluorophenyl)-3-(2,6-difluorobenzoyl)urea), and chlorfluazuron (1-[3,5-dichloro-4-(3-chloro-5-trifluoromethyl-2-pyridyloxy)phenyl]-3-(2,6-difluorobenzoyl)urea).

[0080] In addition, as acaricides, CPCBS (parachlorophenyl-parachlorobenzene sulfonate), phenisobromolate (4,4'-dibromobenzylate isopropyl), tetradiphon (2,4,5,4'-tetrachlorodiphenyl sulfone), phenothiocarb (S-4-phenoxybutyl=dimethylthiocarbamate), fenpyroximate (tert-butyl=(E)-α-(1,3-dimethyl-5-phenoxypyrazole-4-ylmethyleneaminooxy)-p-toluate), fluacinam (3-chloro-N-(3-chloro-5-trifluoromethyl-2-pyridyl)-α,α,α-trifluoro-2,6-dinitro-p-toluidine), fenbutasin oxide (hexakis(β,β-dimethylphenylethyl)distannoxane), hexythiazox (trans-5-(4-chlorophenyl)-N Examples include cyclohexyl-4-methyl-2-oxothiazolidine-3-carboxamide and amitrastin (3-methyl-1,5-bis(2,4-xylyl)-1,3,5-triazapenta-1,4-diene). Preferred examples include phenisobromolate (4,4'-dibromobenzylate isopropyl), amitrastin (3-methyl-1,5-bis(2,4-xylyl)-1,3,5-triazapenta-1,4-diene), and fenpyroximate (tert-butyl=(E)-α-(1,3-dimethyl-5-phenoxypyrazole-4-ylmethyleneaminooxy)-p-toluate).

[0081] Examples of herbicides include acid amide herbicides such as Stam (3,4-dichloropropionanilide, DCPA), urea herbicides such as DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea), bipyridilium herbicides such as paraquat (1,1'-dimethyl-4,4'-bipyridium dichloride) and diquat (6,7-dihydrodipyrido[1,2-a:2',1'c]pyrazinedium dibromide), diazine herbicides such as bromacil (5-bromo-3-sec-butyl-6-methyluracil), triazine herbicides such as simazine (2-chloro-4,6-bis(ethylamino)-1,3,5-triazine), and nitrile herbicides such as DBN (2,6-dichlorobenzonitrile). Examples of dinitroaniline herbicides include trifluralin (α,α,α-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine). Examples of carbamate herbicides include benthiocarb (Saturn) (Sp-chlorobenzyl-N,N-diethylthiocarbamate). Examples of diphenyl ether herbicides include NIP (2,4-dichlorophenyl-p-nitrophenyl ether). Examples of phenolic herbicides include PCP (sodium pentachlorophenoxide). Examples of benzoic acid herbicides include MDBA (dimethylamine-3,6-dichloro-o-anisate). Examples of phenoxy herbicides include 2,4-D sodium salt (sodium 2,4-dichlorophenoxyacetate). Examples of amino acid herbicides include glyphosate (N-(phosphonomethyl)glycine or its salt) and glyfosinate (ammonium-DL-homoalanine-4-yl(methyl)phosphine).Aliphatic herbicides include, for example, sodium TCA salt (sodium trichloroacetate), and preferred examples include DBN (2,6-dichlorobenzonitrile), DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea), paraquat (1,1'-dimethyl-4,4'-bipyridium dichloride), and diquat (6,7-dihydrodipyrido[1,2-a:2',1'c]pyrazinedium dibromide).

[0082] Furthermore, examples of adjuvants include, but are not limited to, spreading agents listed in the 2011 edition of the Pesticide Handbook (published by the Japan Plant Protection Association on February 25, 2011). In this embodiment, as mentioned above, the product name "Kao Adjuvant A-134" is used as the adjuvant, but is not limited to this, and the following may also be used.

[0083] Other commercially available spreading agents include, for example, Makupika (93.0% by mass of polyoxyethylene methylpolysiloxane, manufactured by Ishihara Bioscience Co., Ltd.), Squash (70% by mass of sorbitan fatty acid ester, 5.5% by mass of polyoxyethylene resin acid ester, manufactured by Kao Corporation), Avion-E (24% by mass of paraffin, manufactured by Avion Co., Ltd.), Petan V (42.0% by mass of paraffin, manufactured by Agrokanesho Co., Ltd.), Submerge (50% by mass of alkylbenzene sulfonate sodium, manufactured by Syngenta), and Bray. Examples include Ksru (80% by mass of polyoxyalkylene oxypropylheptamethyltrisiloxane, 20% by mass of polyoxyalkylene propenyl ether, manufactured by Sankei Chemical Co., Ltd.), AgriDex (71.5% by mass of paraffin oil, manufactured by BASF Corporation), AgriStick (40% by mass of alkylallyl polyglycol ether, manufactured by BASF Corporation), D-Flava (82% by mass of oleic acid, manufactured by Chita Agungjaya Corporation), and Miracle 240 (75% by mass of trisiloxane, manufactured by Evonik Corporation).

[0084] Adjuvants contain surfactants, which include nonionic surfactants, anionic surfactants, cationic surfactants, amphoteric surfactants, or mixtures thereof.

[0085] Examples of nonionic surfactants include polyoxyethylene alkyl ethers, polyoxyethylene alkylaryl ethers, polyoxyethylene alkylaryl ether formaldehyde condensates, polyoxyalkylene aryl ethers, polyoxyalkylene alkyl sorbitol esters, polyoxyalkylene alkyl glycerol esters, polyoxyalkylene block copolymers, polyoxyalkylene block copolymer alkyl glycerol esters, polyoxyalkylene alkyl sulfonamides, polyoxypropylene block copolymers, polyoxyethylene oleyl ethers, polyoxyalkylene alkylphenols, polyoxyalkylene alkyl polyglycosides, and mixtures of two or more of these.

[0086] Examples of cationic surfactants include alkylamine ethylene oxide adducts, alkylamine propylene oxide adducts, such as taloamine ethylene oxide adduct, oleylamine ethylene oxide adduct, soyamine ethylene oxide adduct, cocoamine ethylene oxide adduct, synthetic alkylamine ethylene oxide adduct, octylamine ethylene oxide adduct, dialkylamine derivatives, and mixtures thereof. Examples of the dialkylamine derivatives include dialkylmonomethylhydroxyethylammonium propionate, dialkylmonomethylbenzalkonium chloride, and dialkylmonomethylethylammonium ethyl sulfate.

[0087] Typical anionic surfactants are available in aqueous or solid form. Examples include sodium mono- and di-alkylnaphthalene sulfonates, sodium alpha-olefin sulfonates, sodium alkanesulfonates, alkyl sulfosuccinates, alkyl sulfates, polyoxyalkylene alkyl ether sulfates, polyoxyalkylene alkylaryl ether sulfates, polyoxyalkylene styrylphenyl ether sulfates, mono- and di-alkylbenzene sulfonates, alkylnaphthalene sulfonates, formaldehyde condensates of alkylnaphthalene sulfonates, alkyldiphenyl ether sulfonates, and olefinic sulfonic acids. Examples include salts, mono and dialkyl phosphates, polyoxyalkylene mono and dialkyl phosphates, polyoxyalkylene mono and diphenyl ether phosphates, polyoxyalkylene mono and dialkylphenyl ether phosphates, polycarboxylates, fatty acid salts, linear and branched alkyl polyoxyalkylene ether acetate or salts thereof, alkenyl polyoxyalkylene ether acetate or salts thereof, linear and branched alkylamide polyoxyalkylene ether acetate or salts thereof, stearic acid and its salts, oleic acid and its salts, N-methyl fatty acid taurides, and mixtures of two or more of these (including sodium, potassium, ammonium, and amine salts).

[0088] Furthermore, amphoteric surfactants include alkyldimethylaminoacetic acid betaine, alkylcarboxymethylhydroxyethylimidazolinium betaine, alkylamidopropyl betaine, alkyldimethylamine oxide, and the like, and one or more of these can be used as a mixture.

[0089] Returning to Figure 5, the CPU 11 then generates an infection stage map, which visualizes the progression of Ganoderma infection in each palm tree on a map image, as treatment suggestion information based on the determined infection stage and the decided treatment (step 56), and transmits the generated progression stage map to the user terminal 200 (step 57).

[0090] The CPU 11 may, after initially generating treatment suggestion information (infection stage map) and sending it to the user terminal 200, have a predetermined period of time (for example, several months, half a year, etc.) elapse, then have the remote sensing device sense palm trees again to acquire sensing data, generate treatment suggestion information (infection stage map) again based on this sensing data, and send it to the user terminal 200.

[0091] Figure 7 shows an example of an infection stage map generated in this embodiment and displayed on the application on the user terminal 200.

[0092] As shown in the figure, the infection stage map shows the region R of each palm tree on the RGB image (aerial photograph) of the target area of ​​the palm oil plantation, captured by the remote sensing device (mainly a drone), in different colors according to the determined infection stage of Ganoderma.

[0093] The CPU 11 colors each palm tree region R on the RGB image with a color corresponding to the determined infection stage (for example, infection stage 0 is colorless, infection stage 1 is green, infection stage 2 is yellow, infection stage 3 is orange, and infection stage 4 is red), and also refers to the treatment information database 33 to set up a link for each palm tree region R that allows access to information indicating the determined treatment.

[0094] When each of the regions R is selected by the user of the user terminal 200, for example by tapping, information indicating the determined treatment or medical procedure for the infection stage corresponding to that region is displayed, for example, by a pop-up.

[0095] Figure 8 shows an example of how treatment information (or therapeutic treatment information) is displayed when one region R on the infection stage map is selected by the user.

[0096] As shown in the figure, a pop-up 80 displaying treatment information is displayed, starting from the region R selected by the user. Pop-up 80 displays the sequence number and coordinate information (latitude and longitude information) of the palm trees in region R, as well as treatment information 81 indicating the infection stage of Ganoderma on the palm trees and the corresponding treatment. In the example shown in the figure, for example, information suggesting spraying pesticides and adjuvants on the surface of the tree trunk is displayed. Other treatments mentioned above (injection into the tree trunk, soil irrigation, and microbial addition) can also be displayed by the user switching screens on the pop-up.

[0097] Furthermore, at the bottom of the pop-up 80, for example, a navigation button 82 is displayed to launch a map application and navigate the user terminal 200 to the location of each palm tree. The user can check the treatment for each palm tree on the infection stage map, prepare the necessary agents such as pesticides and adjuvants for treatment (treatment or prevention), and then start the treatment by starting navigation using the navigation button 82.

[0098] Furthermore, when a user selects an area R on the infection stage map, a popup 80 may be displayed. In addition, when a user selects an infection stage on the infection stage map, the area of ​​palm trees that should be treated according to that infection stage may be highlighted on the map, and information such as its area may be output. The selection of each infection stage may be possible, for example, by selecting the box for each infection stage in the legend G.

[0099] Furthermore, when the CPU 11 determines the treatment for each palm tree, it may take into account not only the infection stage of the ganoderma on the palm tree in question, but also the infection stage of the ganoderma on palm trees adjacent to it. This allows for more accurate treatment recommendations.

[0100] In other words, the CPU 11 may, on the infection stage map, set links to information indicating a predetermined treatment similar to that applied to palm trees determined to be in the diseased stage (e.g., infection stage 1-4) in areas R of palm trees that are adjacent to palm trees determined to be in the diseased stage, while not setting such links in areas of palm trees that are not adjacent to palm trees determined to be in the diseased stage. The predetermined treatment may be the same as that applied to palm trees determined to be in the diseased stage.

[0101] Furthermore, CPU11 may change the treatment of a palm tree at infection stage 0 depending on whether it is adjacent to a palm tree at infection stage 1 or higher in multiple directions or adjacent in only one direction. In other words, even if a palm tree is healthy, if it is adjacent to an infected palm tree in multiple directions, it is considered to have a high probability of future infection, so the importance of prevention (concentration of pesticides and adjuvants) may be increased compared to a healthy palm tree adjacent to an infected palm tree in only one direction.

[0102] Furthermore, the CPU 11 may set links to information indicating different treatments for areas R of palm trees adjacent to palm trees determined to be at infection stage 2 (or infection stage 3) among palm trees determined to be at infection stage 1 (or infection stage 2) on the infection stage map, and for areas R of palm trees adjacent to palm trees determined to be at infection stage 3 (or infection stage 4). In other words, even among palm trees determined to be at infection stage 1, if they are adjacent to palm trees at infection stage 2, the infection stage is expected to worsen more in the latter case, so the importance of treatment (concentration of pesticides or adjuvants) may be increased for the latter case.

[0103] Furthermore, the CPU 11 may, for palm trees determined to be at infection stage 1 or infection stage 2, consider the areas of adjacent palm trees as described above, determine the treatment stage, and display a treatment map as shown in Figure 9 upon request. In the example in Figure 9, areas R are enclosed by different types of lines for each different treatment stage, superimposed on the infection stage map. However, areas R may also be shown in different colors for each different treatment stage, separate from the infection stage map. The infection stage map allows for understanding the disease status of palm trees, and the treatment map supports infection control treatment in the plantation. The treatment map can also display the order quantities of pesticides and additives such as adjuvants to be prepared by the plantation. In addition, maps for each treatment method can be displayed, such as areas for pesticide application and areas for application of both pesticides and adjuvants.

[0104] In determining the treatment stage according to the infection stage of surrounding plants, the adjacent plant range can be appropriately set according to the type of plant. The adjacent range may be set in units of the number of plants or in units of sections (areas). The setting information regarding the adjacent range may be entered each time via the user terminal 200 or the operation reception unit 17 of the monitoring server 100, or predetermined adjacent range information according to the type of plant may be stored in the storage unit 18.

[0105] As shown in Figure 9, in this embodiment, the adjacent range is defined on a unit of one adjacent palm tree. However, the adjacent range is not limited to one tree. Furthermore, for example, in the case of diseases where spores are dispersed, the disease extends to a section that covers multiple plants, so the adjacent range can be defined on a section (area) basis, as in the soybean example described later.

[0106] [summary] As described above, according to this embodiment, the tree infection monitoring system can determine and propose treatment to the user according to the infection stage of the plant (palm tree) disease (Ganoderma).

[0107] [Differentiation] Although embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above, and various modifications can be made without departing from the spirit of the present invention.

[0108] In the embodiment described above, the tree infection monitoring system monitored the infection status of trees in the orchard with pathogens, determined the infection stage, and proposed treatment (cure or prevention) methods according to the infection stage. In addition, the tree infection monitoring system may perform a process to confirm whether or not the treatment was effective after it has been administered.

[0109] Specifically, the monitoring server 100 performs the processing shown in Figure 5 above, and after the user has taken the necessary measures according to the infection stage map (such as spraying pesticides on palm trees), it performs the monitoring process again in steps 52 to 54 of Figure 5 after a certain period of time (for example, several months).

[0110] The infection stage of the palm trees is determined by the second monitoring process (step 54). The determination result is then compared with the infection stage determination result from the first monitoring process stored in the monitoring information database 32 to confirm the effectiveness of the control measures taken after the first monitoring process.

[0111] Specifically, CPU11 determines that the control was effective if, based on the above comparison, the infection stage during the second monitoring treatment for a particular palm tree was lower than the infection stage during the first monitoring treatment. Conversely, if both infection stages are the same, or if the infection stage during the second monitoring treatment is more advanced than the infection stage during the first monitoring treatment, CPU11 determines that the control was ineffective.

[0112] The confirmed pest control effectiveness information is stored in the treatment information database 33 as information on the degree of improvement since the initial and previous monitoring treatments, and is transmitted from the monitoring server 100 to the user terminal 200 upon request from the user or automatically.

[0113] In this way, the tree infection monitoring system can also diagnose the effectiveness of the treatments proposed and implemented by the user by performing the monitoring process multiple times and identifying the differences in the infection stage.

[0114] In the embodiment described above, the monitoring server 100 pre-stored treatment information corresponding to the infection stage in the treatment information database 33, and when it determined the infection stage, it referred to the stored information to decide on the treatment. Alternatively, the monitoring server 100 may, without pre-storing treatment information, send a request to the proposer terminal 300 requesting treatment information corresponding to the infection stage when it determined the infection stage, and then determine the treatment based on the treatment information sent from the proposer terminal 300 to generate the above-mentioned progression stage map.

[0115] In the embodiment described above, only one monitoring server 100 is shown, but the processing performed by the monitoring server 100 may be distributed and executed across multiple servers. For example, there may be separate servers for storing remote sensing data and treatment information, and separate servers for generating the learning model and determining the infection stage of Ganoderma based on it.

[0116] In the above-described embodiment, the monitoring server 100 may determine the timing of the next monitoring based on the determination of the disease progression stage (infection stage), or based on the time-series information of the determination of the disease progression stage, and provide the user with suggested information on the timing of the monitoring. For example, the monitoring frequency may be set to increase if the proportion of plants with a severe disease progression stage is high at a certain time, or if the rate of disease progression is fast over time.

[0117] The information regarding treatments corresponding to infection stages (pesticides, adjuvants, microorganisms used, and their amounts and frequency of application) shown in Figure 6 above is merely an example and should be modified as appropriate depending on the target plant. Similarly, the display methods of the infection stage maps shown in Figures 7 and 8 are also merely examples and may be modified as needed.

[0118] In the embodiments described above, palm oil was used as an example plant and Ganoderma as the pathogen, but the combination of plant and pathogen is not limited to these, and it may also be used to determine disease in herbaceous plants. For example, it may be used to determine infection with Panama disease or new Panama disease in bananas, rust disease in soybeans, root rot in rubber plants, blast disease in rice plants, or rust disease in grapes. Furthermore, it may also be used to determine disease in herbaceous plants such as corn, potatoes, and rice. It is suitably used for diseases where the initial infection is difficult to discern with the naked eye. In addition, in the present invention, plant disease is not limited to infection with fungi such as mushrooms and molds, bacteria, viruses, etc., but also includes disease other than infectious diseases such as those caused by pests and parasites. "Progression stage" includes not only the infection stage of infectious diseases that have affected plants, but also the progression stage of diseases other than infectious diseases that have affected plants. However, the present invention is suitably used for treatment of plants infected with infectious diseases.

[0119] The following describes an embodiment where the plant is soybean and the pathogen is soybean rust fungus (hereinafter also simply referred to as rust disease). Two types of rust disease are known: Asian (Phakopsora pachyrhizi) and Central and South American (P. meibomiae), but the Asian type is particularly problematic.

[0120] Figure 10 is a table showing examples of tree treatment methods according to the stage of infection progression determined in this embodiment.

[0121] As shown in the figure, in this embodiment, five stages of rust infection are set and determined by the learning model described above: infection stage 0 (healthy), infection stage 1 (early infection), infection stage 2 (mid-infection), infection stage 3 (severe infection), and infection stage 4 (very severe infection).

[0122] For preventive measures against soybeans in infection stage 1, spraying with an aqueous solution of pesticide and adjuvant (pesticide alone may be sufficient if the plants are healthy) or adding microorganisms is also suggested.

[0123] For pesticides, hexaconazole is used, for example, at an additive concentration of 0.01% to 0.001%. For adjuvants, Kao Adjuvant A-134 is used, at an additive concentration of 0.1% to 1%. The total amount of both added is 100L to 200L / 1000m 2 It is stated that the treatment should be performed at least once a year.

[0124] For the addition of microorganisms, fertilizers containing microorganisms (microorganism types: Bacillus, etc.) are used. When a fertilizer such as CEASE (BioWorks) is used, the addition concentration and number of treatments are 0.05% to 0.3%, the addition amount is 100L to 200L per 1000m2, and the number of treatments is at least once per year.

[0125] For silicon addition, silicon-containing fertilizers are used. For example, if the fertilizer used is AgroSilicio (manufactured by Harsco Minerais Ltda), the addition concentration and number of applications are 100-800 kg / 1000 m³. 2 It is stated that the treatment should be performed at least once a year.

[0126] Soybeans in infection stage 1 show small light brown to yellowish-brown spots, or raised spots, on the lower leaves and relatively mature leaves of the main stem rather than the lateral branches. As shown in the figure, for soybeans in infection stage 1, treatment stage 1 is proposed, which involves spraying the plant surface with an aqueous solution of pesticide and adjuvant, in addition to adding microorganisms.

[0127] Whether applied by spraying the plant surface or irrigating the soil, hexaconazole is used as the pesticide and Kao Adjuvant A-134 as the adjuvant. The recommended concentration is 0.1% to 1%. The total amount of both added is 100L to 200L per 1000m 2 It is stated that the treatment should be performed at least once a year.

[0128] For microbial addition, fertilizers containing microorganisms (microorganism types: Bacillus, etc.) are used. For example, when using a fertilizer such as CEASE (BioWorks), the addition concentration and number of applications are 0.05% to 0.3%, and the addition amount is 100L to 200L / 1000m 2 It is stated that the treatment should be performed at least once a year.

[0129] In soybeans at infection stage 2, the leaves further yellow and turn brown to dark brown, or they may release summer spores (a light brown powder). As shown in the figure, for soybeans at infection stage 2, treatment stage 2 is proposed, which involves spraying the plant surface with an aqueous solution of pesticide and adjuvant.

[0130] In treatment stage 2, the pesticides and adjuvants used are the same as those used in infection stage 1, but the number of treatments is increased to a minimum of two times per year for each treatment stage.

[0131] Soybeans at infection stage 3 show polygonal, slightly swollen, dark brown spots (winter spore layer) beneath the epidermis around the summer spore layer. As shown in the figure, for soybeans at infection stage 3, treatment stage 3 is proposed, which involves spraying with an aqueous solution of pesticide and adjuvant.

[0132] In treatment stage 3, the pesticides and adjuvants used are the same as those in infection stage 1, plus the addition of azoxystrobin 0.04-0.15% as a pesticide. The number of treatments is increased to a minimum of two times per year for all treatment stages.

[0133] Soybeans in infection stage 4 are those whose affected leaves have yellowed and fallen off. As shown in the figure, treatment stage 4 involves removing the infected soybean plants.

[0134] Figure 11 shows an example of a progress map generated in this embodiment.

[0135] The palm tree infection stage map shown in Figure 7 displayed each palm tree's region R in a different color according to the determined infection stage of Ganoderma. As shown in the same figure, the infection stage map in this embodiment also displays each soybean's region Rs in a different color according to the determined infection stage of rust. In addition, in this embodiment, the CPU 11 divides the entire region of the progression stage map into multiple sections Rf, and displays the infection stage in each section Rf, for example, with a number. The infection stage may be indicated by a change in color or other display method instead of a number. Each section Rf corresponds to a division of the farm into units of a predetermined area (for example, 1 ha).

[0136] In other words, the infection stage of the soybean with the most advanced infection stage in each plot Rf is indicated by a corresponding number (e.g., 0.5, 1, 2, 3), representing the infection stage of that plot Rf. Infection stage 0.5 refers to a stage where the soybean is infected but has not yet reached infection stage 1.

[0137] This is because, in the case of soybeans, the distance between soybean plants is shorter compared to the case of palm oil mentioned above. Furthermore, in the case of rust disease, as germination occurs, lesions mature, and spores are dispersed, the infection can spread more widely from diseased soybeans to the surrounding healthy soybeans. Therefore, it is considered appropriate to assess the disease stage not only in soybeans adjacent to diseased soybeans, but also in a wider area Rf.

[0138] In the progress map of this embodiment, a pop-up displaying the treatment information shown in Figure 10 is displayed, similar to the pop-up 80 in Figure 8, starting from the region Rs or section Rf selected by the user.

[0139] The CPU 11 may also display a treatment procedure map similar to that in Figure 9, instead of the infection stage map described above. Figure 12 shows an example of a treatment procedure map generated in this embodiment.

[0140] The palm oil treatment map shown in Figure 9 was superimposed on the infection stage map, with different treatment stages enclosed by different types of lines. On the other hand, as shown in the same figure, in this embodiment, the infection stage of the soybean with the most advanced infection stage in each section Rf is set as the infection stage of that section Rf, and each section Rf is displayed in a different display manner according to the treatment (treatment or prevention) corresponding to the infection stage of that Rf.

[0141] In the example shown in the figure, the treatment procedure map is superimposed on the infection stage map, with each different treatment stage enclosed by a different type of line. However, the different treatment stages could also be represented separately from the infection stage map, using different colors for each section Rf.

[0142] Thus, in the case of soybeans, when determining the treatment stage according to the infection stage of surrounding soybeans, the adjacent area can be defined by the above-mentioned section Rf (area), the infection stage can be determined based on the soybean with the highest infection stage in that section Rf, and the treatment stage can be determined based on the infection stage of that section Rf and the adjacent sections Rf.

[0143] Furthermore, as described above, in the infection stage map and treatment stage map, depending on the type of plant (characteristics of how the infection spreads), such as palm oil and soybeans, the monitoring server 100 may decide and execute, according to prior settings or input from the user terminal 200, whether to display the infection stage of a plant and its adjacent area on a per-plant basis, as in the example of palm oil, or to display it on a section basis, as in the example of soybeans (i.e., whether to treat on a per-plant basis or on a section basis).

[0144] Of the inventions described in the claims of this application, the invention described as "information processing method" is one in which each step is performed automatically by at least one device such as a computer through information processing by software, and not by a human using a computer or other device. In other words, the "information processing method" is an information processing method using computer software, and not a method in which a human operates a computer as a calculating tool.

[0145] The functions realized by the components described herein may be implemented in a circuit or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to realize the functions described herein. A processor is considered to be a circuit or processing circuitry, including transistors and other circuits. A processor may be a programmed processor that executes a program stored in memory.

[0146] In this specification, circuitry, unit, and means are hardware programmed to perform or execute the functions described herein. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein.

[0147] If the hardware is a processor that is considered to be of the type of circuitry, then the circuitry, means, or unit is a combination of hardware and software used to constitute the hardware and / or processor. Furthermore, configurations described as units or means in this specification can be implemented as circuitry. [Explanation of Symbols]

[0148] 11…CPU 18...Storage section 19… Communications Department 31…Farm Information Database 32…Monitoring Information Database 33… Treatment Information Database 80… Pop-up 81… Treatment Information 82... Navigation button 100…Monitoring Server 200... User terminals 300... Proposer's terminal R... Palm tree region Rs...Soybean area Rf...Map division area

Claims

1. A plant monitoring information processing system, A storage unit that stores treatment information relating to treatments to be taken according to multiple stages of progression of the disease of the plant, and a learning model that has learned the characteristics of each sensing data obtained by remotely sensing the plant at multiple stages of progression of the disease, Image data based on first sensing information obtained by imaging the plant with a spectral camera or RGB camera, which is a first sensing means of the remote sensing device, is acquired, morphological change information indicating changes in the plant's morphology is acquired by a LiDAR or SAR, which is a second sensing means of the remote sensing device, and the progression stage of the plant's disease in the image data is determined based on the acquired image data, the morphological change information, and the learning model. Based on the determined progression stage and the stored treatment information, the system determines the treatment to be taken according to the determined progression stage and generates treatment proposal information that proposes the treatment. Control unit and An information processing system equipped with the following features.

2. The control unit transmits the generated treatment suggestion information to the user terminal of the user who owns or manages the plant. The information processing system according to claim 1.

3. The control unit receives information from the user terminal regarding at least one of the following: the variety of the plant seedling, its age, its generation, its height, the type of soil in which the plant grows, and the type of terrain. Based on the received information, the image data, remote sensing data, and the learning model, the control unit determines the stage of disease progression in the plant. The information processing system according to claim 2.

4. The control unit, when it finds missing information in the information received from the user terminal regarding the plant's seedling variety, age, generation, and tree height, calculates and fills in the missing information based on the sensing information. The information processing system according to claim 3.

5. The control unit transmits information regarding the progress stage to the proposer terminal proposing the action, and receives action information corresponding to the progress stage transmitted from the proposer terminal and stores it in the storage unit. The information processing system according to claim 1 or 2.

6. The control unit receives location information from the user terminal for an area in the user's farm where multiple plants are growing, transmits the location information to the remote sensing device, and receives the image data from the remote sensing device that captures the area based on the location information. The information processing system according to claim 2.

7. The control unit generates a progression stage map image as the generated treatment suggestion information, in which each plant region on the image is shown in a different color according to the different progression stages. The information processing system according to claim 2.

8. The control unit sets up links in each region of the progress map image that allow access to information indicating the procedure. The information processing system according to claim 7.

9. The control unit sets a link to information indicating a predetermined treatment similar to that applied to the treatment applied to the diseased plant in the region of the plant determined to be in the diseased stage, among the plants determined to be in the uninfected stage on the progression stage map image, and does not set the link in the region of the plant that is not adjacent to the plant determined to be in the diseased stage. The information processing system according to claim 8.

10. The aforementioned progression stages include a first disease stage, a second disease stage that is more advanced than the first disease stage, and a third disease stage that is more advanced than the second disease stage. The control unit sets links to information indicating different treatments in the first adjacent area of ​​plants adjacent to plants determined to be in the second disease stage, and in the second adjacent area of ​​plants adjacent to plants determined to be in the third disease stage, on the progression stage map image. The information processing system according to claim 7.

11. The control unit divides the entire area represented by the progression map image into multiple sections, and displays each section of the progression map image in a different display mode according to the treatment taken for the plant that is most advanced in the progression stage within that section. The information processing system according to claim 7.

12. The control unit, after generating the treatment suggestion information, acquires new image data of the plant captured by the remote sensing device after a predetermined period has elapsed, generates the treatment suggestion information based on the image data, and transmits it to the user terminal. The information processing system according to claim 2.

13. An information processing device for monitoring plants, A storage unit that stores treatment information relating to treatments to be taken according to multiple stages of progression of the disease of the plant, and a learning model that has learned the characteristics of each sensing data obtained by remotely sensing the plant at multiple stages of progression of the disease, The remote sensing device receives image data based on first sensing information obtained by imaging the plant with a spectral camera or RGB camera, which is a first sensing means of the remote sensing device, and obtains morphological change information indicating changes in the plant's morphology other than the first sensing information, which is acquired by a LiDAR or SAR, which is a second sensing means of the remote sensing device, and determines the stage of disease progression of the plant in the image data based on the received image data, the morphological change information, and the learning model. Based on the determined progression stage and the stored treatment information, the system determines the treatment to be taken according to the determined progression stage and generates treatment proposal information that proposes the treatment. Control unit and An information processing device equipped with the following.

14. The control unit transmits the generated treatment suggestion information to the user terminal of the user who owns or manages the plant. The information processing apparatus according to claim 13.

15. The control unit receives information from the user terminal regarding at least one of the following: the variety of the plant seedling, its age, its generation, its height, the type of soil in which the plant grows, and the type of terrain. Based on the received information, the image data, remote sensing data, and the learning model, the control unit determines the stage of disease progression in the plant. The information processing apparatus according to claim 14.

16. The control unit generates a progression stage map image as the generated treatment suggestion information, in which each plant region on the image is shown in a different color according to the different progression stages. The information processing apparatus according to claim 14.

17. The control unit sets up links in each region of the progress map image that allow access to information indicating the procedure. The information processing apparatus according to claim 16.

18. The control unit sets a link to information indicating a predetermined treatment similar to that applied to the treatment applied to the diseased plant in the region of the plant determined to be in the diseased stage, among the plants determined to be in the uninfected stage on the progression stage map image, and does not set the link in the region of the plant that is not adjacent to the plant determined to be in the diseased stage. The information processing apparatus according to claim 17.

19. The control unit divides the entire area represented by the progression map image into multiple sections, and displays each section of the progression map image in a different display mode according to the treatment taken for the plant that is most advanced in the progression stage within that section. The information processing apparatus according to claim 16.

20. A method for processing information to monitor plants, The system stores information on treatments to be taken according to multiple stages of the progression of the disease in the plant, and a learning model that has learned the characteristics of each sensing data obtained by remotely sensing the plant at multiple stages of the progression of the disease. Image data based on first sensing information obtained by imaging the plant with a spectral camera or RGB camera, which is a first sensing means of the remote sensing device, is acquired. The remote sensing device acquires morphological change information, other than the first sensing information, that indicates a change in the plant's morphology, which is obtained by a second sensing means, LiDAR or SAR, provided by the remote sensing device. Based on the acquired image data, the morphological change information, and the learning model, the disease progression stage of the plant in the image is determined. Based on the determined progression stage and the stored treatment information, the system determines the treatment to be taken according to the determined progression stage and generates treatment proposal information that proposes the treatment. Information processing methods.

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