Information treatment system and information treatment 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 a disease in a plant. [Background technology]

[0002] Conventionally, there has been a technology for diagnosing disease (infection with pathogenic bacteria) in a tree based on an image of the tree. For example, the following Patent Document 1 discloses a method of acquiring an image of a tree (hevea brasiliensis) to be diagnosed, inputting the image of the tree to be diagnosed into a diagnostic model that has been subjected to machine learning for diagnosing a tree having diseased leaves as a diseased tree from an image of the tree, using the image of the tree, information indicating the range in which the leaves are captured in the image, and information indicating whether the range has characteristics of disease (infection with white root rot) as training data, thereby diagnosing a tree having diseased leaves as a diseased tree, and outputting the result of the diagnosis. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2021-145557 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology of Patent Document 1 mentioned above only diagnoses whether or not a tree is diseased, but is unable to diagnose the extent to which the infection with the pathogen has progressed, and therefore is unable to take any measures according to the level of infection.

[0005] The problem of the present invention is to determine treatment depending on the stage of development of a plant disease. [Means for solving the problem]

[0006] An information processing system according to an embodiment of the present invention is an information processing system for monitoring a plant, and includes a storage unit and a control unit. The information processing system may be realized by a single device or may be realized by a plurality of devices. The memory unit stores treatment information regarding measures to be taken according to multiple stages of the disease progression of the plant, and a learning model that has been trained to learn the characteristics of each sensing data obtained by remote sensing the plant at multiple stages of the disease progression. The control unit acquires image data based on first sensing information obtained by capturing an image of the plant using a spectral camera or RGB camera, which is a first sensing means possessed by the remote sensing device, and determines the progression stage of the disease of the plant in the image data based on the acquired image data and the learning model. Furthermore, the control unit determines a treatment to be taken according to the determined stage of progression based on the determined stage of progression and the stored treatment information, and generates treatment proposal information that proposes the treatment.

[0007] An information processing method according to one embodiment of the present invention is an information processing method for monitoring a plant, comprising the steps of: storing treatment information regarding treatments to be taken according to a plurality of disease progression stages of the plant, and a learning model that has been trained on features of each of sensing data obtained by remote sensing the plant at the plurality of disease progression stages; acquiring image data based on first sensing information obtained by capturing an image of the plant using a spectral camera or an RGB camera, which is a first sensing means of a remote sensing device, and determining a progression stage of a disease of the plant in the image data based on the acquired image data and the learning model; This includes determining a treatment to be taken according to the determined stage of progression based on the determined stage of progression and the stored treatment information, and generating treatment proposal information that proposes the treatment. Effect of the Invention

[0008] According to the present invention, it is possible to determine treatment depending on the stage of progression of the disease in the plant. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing the configuration of a tree infection monitoring system according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating a hardware configuration of a monitoring server according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram showing a configuration of a database included in a monitoring server according to an embodiment of the present invention. [Figure 4] 11 is a flowchart showing the flow of a learning model generation process by a monitoring server according to one embodiment of the present invention. [Diagram 5] 11 is a flowchart showing the flow of a tree treatment method decision process performed by a monitoring server according to one embodiment of the present invention. [Figure 6] 1 is a table showing examples of tree treatment methods according to the infection progression stage determined in one embodiment of the present invention. [Figure 7] FIG. 2 illustrates an example of a progression stage map generated in one embodiment of the present invention. [Figure 8] FIG. 13 is a diagram showing an example of treatment suggestion information displayed when a region of a progression stage map generated in one embodiment of the present invention is selected by a user. [Figure 9] FIG. 13 illustrates an example of a therapeutic treatment map generated in one embodiment of the present invention. [Figure 10] 11 is a table showing examples of tree treatment methods according to the infection progression stage determined in another embodiment of the present invention. [Figure 11] FIG. 13 is a diagram showing an example of a progress stage map generated in another embodiment of the present invention. [Figure 12] FIG. 13 shows an example of a therapeutic treatment map generated in another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] [System configuration] 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 pathogen (Ganoderma) in the trees (palm trees) on the plantation, determines the stage (infection stage) of the infection in the infected palm trees, and proposes a method of treating (treating or preventing) Ganoderma according to the infection stage. Palm trees here include oil palms, coconut palms, etc.

[0013] The monitoring server 100 is a server (information processing device) operated by a provider of the above-mentioned monitoring service. The monitoring server 100 is connected to a user terminal 200 and a proposer terminal 300 via the Internet 50.

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

[0015] The owner or manager of a farm (user) requests a monitoring service provider to provide monitoring services for his or her farm, and the user terminal 200 receives the monitoring results from the monitoring server 100, i.e., information on the infection stage of Ganoderma in each palm tree on the 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 causes an image of the target area (all or part) of the user's farm to be captured from the air or the ground by a remote sensing device, for example, periodically (for example, twice a year).

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

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

[0019] Low-altitude flying objects are unmanned aerial vehicles (mainly drones) including multicopters 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 mobility.

[0020] A low-orbit microsatellite is an artificial satellite weighing less than 100 kg that orbits at high speed in a low orbit of about 400-600 km above the ground and is equipped with the above-mentioned sensing means / devices and basic means / devices for movement.

[0021] The ground mobile device is a device capable of moving on or near the ground, such as a mobile robot or an unmanned vehicle, and is equipped with the above-mentioned sensing means / device and mobile device basic means / device.

[0022] The remote sensing device includes an optical RGB camera or a spectral camera as a sensing means / device to obtain first sensing data, including reflected wavelengths, based on which an image or image information can be synthesized.

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

[0024] By using second sensing data such as a temperature sensor or radar, highly accurate information on temperature or plant morphology can be acquired and received, and by using this information in combination 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, and slight changes in the morphology of palm trees due to infection, such as slight changes in the angle and bending of leaves, it is possible to determine the presence or absence of infection and the infection stage.

[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 infection stage of Ganoderma in each palm tree in the data based on the acquired image data and other sensing data, as well as a learning model that has been trained to learn the characteristics of each sensing data obtained by remotely sensing palm trees at multiple infection stages of Ganoderma.

[0026] The monitoring server 100 then determines an action corresponding to the determined infection stage based on the action information regarding the action to be taken according to each infection stage received from the proposer terminal 300, notifies the infection status information, and generates action proposal information (infection stage map) that suggests the action to the user, and transmits it to the user terminal 200.

[0027] The proposer terminal 300 is a terminal used by an operator proposing a method of dealing with the Ganoderma infection, and is, for example, a smartphone, mobile phone, tablet PC, notebook PC, desktop PC, etc. The proposer terminal 300 proposes and calculates information on measures to be taken according to the infection stage of Ganoderma, typically information on treatment actions such as spraying, injecting into trees, irrigation, adding fertilizers, adding microorganisms, etc., information on additives such as pesticides, adjuvants, fertilizers, fungicides, microorganisms, etc. used in these treatments, more specifically, information on the type of additive, amount added (per tree), concentration added, etc., and treatment frequency, and transmits this information to the monitoring server 100.

[0028] [Monitoring server hardware configuration] As shown in FIG. 2, the monitoring server 100 includes a central processing unit (CPU) 11, a read only memory (ROM) 12, a random access memory (RAM) 13, an input / output interface 15, and a bus 14 that interconnects these components.

[0029] The CPU 11 appropriately accesses the RAM 13 etc. as necessary, and performs various arithmetic processing while centrally controlling each block of the monitoring server 100. The ROM 12 is a non-volatile memory in which the OS, programs, various parameters, and other firmware to be executed by the CPU 11 are fixedly stored. The RAM 13 is used as a working area for the CPU 11, and temporarily stores the OS, various applications being executed, 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 using, for example, a Liquid Crystal Display (LCD), an Organic ElectroLuminescence Display (OLED), a Cathode Ray Tube (CRT), or the like.

[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. When the operation reception unit 17 is a touch panel, the touch panel can be integrated with the display unit 16.

[0033] The storage unit 18 is, for example, a non-volatile memory such as a hard disk drive (HDD), a flash memory (SSD; Solid State Drive), or other solid-state memory. The storage unit 18 stores the OS, various applications, and various data.

[0034] As will be described later, particularly in this embodiment, 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. The programs can be realized 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 figure, the basic hardware configuration of the user terminal 200 and the proposer terminal 300 is also approximately the same as the hardware configuration of the monitoring server 100, and each has the basic configuration for functioning as a computer, such as a CPU, a memory unit, a communication unit, etc.

[0037] [Monitoring server database configuration] 3, the monitoring server 100 has, in the storage unit 18, a farm information database 31, a monitoring information database 32, and a treatment information database 33. Note that each of 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, i.e., basic information such as the farm name and location, information about growing or past palm trees such as the type of palm tree (seedling variety), tree age, generation, height, etc., 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 such information from, for example, the user terminal 200.

[0039] If the farm information database 31 is missing some information on the palm trees in the farm, the monitoring server 100 can calculate and supplement the missing information based on the sensing information as necessary. For example, the height can be calculated from the image information, and the age of the tree can be calculated from the height and type. The slope of the land can also be calculated using a radar-based sensor. Examples of cases where information is missing include cases where the information was not input by the user. This information can be included in the teacher data of a learning model that has been trained on the characteristics of the above-mentioned image data and other sensing data, as well as each sensing data obtained by remote sensing palm trees at multiple infection stages of Ganoderma, and can be used to determine the infection stage of Ganoderma in each palm tree based on the learning model.

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

[0041] The treatment information database 33 stores information on the treatments (treatment and prevention) decided and proposed based on the above monitoring results, i.e., the type and efficacy of agents (pesticides, adjuvants, fertilizers) used in the treatment, information on how the agents are used (amount and number of times of spraying on the trunk surface, injection into the trunk, soil irrigation, etc.), history information on past treatments, information on the degree of improvement from the treatments at the first or previous monitoring, infection stage map data sent to the user terminal 200 according to the above infection stage, etc.

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

[0043] These databases are used, with mutual reference as necessary, in the tree treatment method decision process by the tree infection monitoring system described below.

[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 above will be described. The operation is executed by the cooperation of hardware such as the CPU 11 and communication unit 19 of the monitoring server 100 and software stored in the storage unit 18. For convenience, in the following description, the CPU 11 of the monitoring server 100 is the subject of operation.

[0046] First, a description will be given of the learning model generation process performed by the monitoring server 100. Fig. 4 is a flowchart showing the flow of the learning model generation process.

[0047] As shown in the figure, the CPU 11 of the monitoring server 100 acquires remote sensing data of palm trees in multiple different palm tree plantations from micro-satellites, flying vehicles, and ground terminals, and then performs preprocessing on the data to generate a learning model (step 41).

[0048] Specifically, the distortion of the acquired image data (spectral image or RGB image) is corrected, and the wavelength shift between images is corrected so that multiple spectral images are superimposed for the same wavelength.

[0049] Next, the CPU 11 sets explanatory variables and objective variables for machine learning based on the pre-processed data (step 42). Specifically, the pre-processed sensing data and data such as the palm's location, age, type, generation, and soil type received from the user terminal 200 are set as explanatory variables for machine learning, and the infection stage of the disease that the palm has contracted (e.g., infection stage 0 to 4) is set as an objective variable for machine learning (classification problem).

[0050] Next, the CPU 11 generates training data and validation data from the preprocessed data set (step 43). That is, various teacher data with different weights and biases according to the infection stage of the disease are generated, and validation data used to evaluate the performance of hyperparameters that set the behavior of the machine learning algorithm is generated.

[0051] For example, as the infection stage of a diseased tree progresses, the area that can be determined from the spread of the leaves when viewed from above, the maximum diameter equivalent to a circle that can be drawn by connecting the tips of the leaves, or the leaf area becomes smaller, the temperature tends to change, and the color of the leaves 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 above sensing data and generates training data having parameters corresponding to each infection stage.

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

[0053] After preprocessing, the rest of the dataset other than the training data and validation data is left as test data for testing the versatility of the trained model.

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

[0055] Then, the CPU 11 evaluates the versatility of the trained model using the test data (step 45). By comparing the evaluation results of different trained models, an optimal machine learning model that describes the relationship between the explanatory variables and the objective variables is selected and generated.

[0056] Here, as the machine learning algorithm, for example, algorithms suitable for image processing, such as logistic regression, random forest, boosting, and SVM (support vector machine) may be used. Although the above shows an example of supervised learning, unsupervised learning may also be used. In addition, CNN (convolutional neural network), VAE (variational autoencoder), GAN (generative adversarial network), and deep learning algorithms may also be used.

[0057] Next, a tree treatment method decision process using the learning model generated above will be described. Fig. 5 is a flowchart showing the flow of the tree treatment method decision process by the monitoring server 100.

[0058] As shown in the figure, first, the CPU 11 of the monitoring server 100 judges whether or not a request to confirm the infection status of Ganoderma palms has been received from the user terminal 200 (step 51). The infection status confirmation request includes location information of the growing area of ​​the palms in the user's farm for which the user wishes to confirm the infection status. The infection status confirmation request may also include at least one piece of information selected from the growth information and environmental information of the palms for which the user wishes to confirm the infection status. Growth information includes seedling variety, tree age, generation, tree height, etc., and environmental information includes soil type and terrain type in which the plant is growing, etc.

[0059] Next, the CPU 11 transmits instruction information to instruct the remote sensing device to perform sensing 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, the sensing method (type of sensor to be used), etc. When multiple remote sensing devices are used, the instruction information is transmitted to each of them.

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

[0061] If it is determined that sensing data has been received (Yes in step 53), the CPU 11 uses the above-mentioned learning model based on the sensing data and information about the palm trees (or farms) whose infection status is being confirmed to determine the stage of infection with Ganoderma in each palm tree (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 depending on the infection stage determined above (step 55).

[0063] Fig. 6 is a table showing an example of a tree treatment method according to the progression stage in this embodiment. As shown in the figure, in this embodiment, the infection stages of Ganoderma are infection stage 0 (healthy), infection stage 1 (early infection), infection stage 2 (mid-infection), infection stage 3 (severe infection), and infection stage 4 (very serious infection), which are set and determined by the learning model. Note that the infection stage of a plant disease varies depending on the plant, and a standardized stage set by a government agency or academic research institute of the country where the plant grows may be used.

[0064] A typical example of the treatment in this embodiment is the addition of a pesticide and an adjuvant (pesticide efficacy enhancer) to enhance the effect of the pesticide. In this embodiment, the higher the infection stage, the more times the treatment is performed, and the use of an adjuvant together with the pesticide as an additive is set, and the concentrations of the adjuvants are set to be high.

[0065] Palm trees at infection stage 0 are healthy trees that are located around infected trees at infection stage 1 or higher. As shown in the figure, for palm trees at infection stage 0, an aqueous solution of pesticide and adjuvant (only pesticide is sufficient because the tree is healthy) is sprayed onto the trunk surface as a preventive measure. The aqueous solution refers to a dispersion liquid with water as the solvent, and may be expressed as a treatment liquid. Hereinafter, adding a pesticide or adjuvant can be said to be adding these treatment liquids.

[0066] The pesticide used is, for example, hexaconazole, at a concentration of 0.1%. The adjuvant used is Kao Adjuvant A-134, at a concentration of 0.1%. The total amount of both is 2L, and treatment is performed at least once a year.

[0067] Infection stage 1 palm trees have white buttons at the base, and symptoms on the leaves are difficult to identify with the naked eye. As shown in the figure, for infection stage 1 palm trees, treatment stage 1 is proposed, which includes spraying an aqueous solution of pesticide and adjuvant onto the trunk surface, injecting the aqueous solution into the trunk and irrigating the soil, and also adding microorganisms.

[0068] In all treatments, spraying on the trunk surface, injection into the trunk interior, and soil irrigation, hexaconazole is used as the pesticide and Kao Adjuvant A-134 is used 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 on the trunk surface and injection into the trunk interior, and 0.1-1.0% for soil irrigation. The combined amount of both added is 2L for spraying on the trunk surface and injection into the trunk interior, and 20L for soil irrigation at the base of the tree, and the frequency of treatment is at least twice a year for all treatments.

[0069] To add microorganisms, fertilizers containing microorganisms (microorganisms: Trichoderma, Hendersonia, Actinomycetes, etc.) are used. When the fertilizer is, for example, a product name Bio-fertilizer (Gano EF), the concentration and frequency of application are 50g / tree 3 times / year for young trees, 500g / whole 1 time / whole when replanting trees, 2kg / tree 1 time / year for trees aged 4 years or less, and 4kg / tree 1 time / year for trees aged 4 years or more.

[0070] Palm trees at infection stage 2 are judged comprehensively from the fact that white buttons and mushrooms can be seen at the base of the tree, 30% or less decay has been confirmed, and the leaves appear to have turned yellow. As shown in the figure, for palm trees at infection stage 2, in addition to spraying an aqueous solution of pesticides and adjuvants onto the trunk surface, injection of the aqueous solution into the trunk and soil irrigation are proposed as treatment stage 2, and the addition of microorganisms is also proposed.

[0071] In the treatment stage 2, the pesticides and adjuvants used in all treatments, whether spraying on the trunk surface, injecting into the trunk, or drenching the soil, are the same as those in the palms in the infection stage 1 above, but the dosage is proposed to be increased, and the frequency of treatments is increased to a minimum of four times per year for all treatments. Also, the addition of microorganisms will be carried out with the same agent, concentration, and frequency as those in the treatment stage 1 palms above.

[0072] Infection stage 3 palm trees have white buttons and mushrooms at the base of the tree, 50% or less decay, and can be confirmed by the phenomenon of leaves breaking. As shown in the figure, for infection stage 2 palm trees, treatment stage 3 is proposed, which includes spraying an aqueous solution of pesticides and adjuvants onto the trunk surface, injecting the aqueous solution into the trunk and irrigating the soil, and also adding microorganisms.

[0073] In treatment stage 3, the pesticides and adjuvants used in all treatments, whether trunk surface spray, trunk internal injection or soil irrigation, are the same as those in the palms in infection stages 1 and 2, but increased dosages are proposed and the frequency of treatments is increased to a minimum of 6 times per year for all treatments. Microorganisms are also added at the same agent, concentration and frequency as those in treatment stage 1 and treatment stage 2.

[0074] An infected palm is a palm that has fallen or died, and as shown in the figure, treatment stage 4 involves felling, replanting the palm tree, replacing the soil, and adding microorganisms.

[0075] The microorganisms used are the same as those in the above-mentioned therapeutic treatment steps 1 to 3, and the amount of microorganisms added and the number of treatments are 500 g / whole, once / whole.

[0076] In addition, examples of agricultural chemical raw materials used in the agricultural chemicals that can be used in this embodiment include, but are not limited to, those described in the Agricultural Chemicals Handbook 2011 Edition (Japan Plant Protection Association). In addition, the agricultural chemical efficacy enhancer composition of the present invention can be used safely without causing any phytotoxicity to various crops. In this embodiment, as described above, the fungicide hexaconazole ((RS)-2-(2,4-dichlorophenyl)-1-(1H-1,2,4-triazol-1-yl)hexan-2-ol) is used, but is not limited thereto, and the following may also be used.

[0077] Other fungicides include the sulfur-based fungicides zineb (zinc ethylene bisdithiocarbamate), maneb (manganese ethylene bisdithiocarbamate), manzeb (zinc ion-coordinated manganese ethylene bisdithiocarbamate), polycarbamate (bisdimethyldithiocarbamoylzinc ethylene bisdithiocarbamate), and the benzimidazole-based fungicide benomyl (methyl-1-(butylcarbamoyl)-2-benzimidazole carbamate). , thiophanate methyl (1,2-bis(3-methoxycarbonyl-2-thioureido)benzene), dicarboximide derivatives vinclozolin (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), triazine (2,4-dichloro-6-(2-chloroanilino)-1,3,5-triazine), triflumizole ((E)-4-chloro-α,α,α-trifluoro-N-(1-imidazol-1-yl-2-propoxyethylidane)-o-toluidine), iminoctadine acetate (1,1-iminodi(octamethylene)diguanidine), triacetate), organic copper (Oxine-copper), cupric hydroxide (Coside Bordeaux, etc.), antibiotic disinfectants (streptomycin, tetracycline, polyoxy, blasticidin S, kasugamycin, validamycin), triadimefon (1-(4-chlorophenoxy)-3,3-dimethyl-1-(1,2,4-triazol-1-yl)-2-butanone), isoprothiolane (diisopropyl 1,3-dithiolan-2-ylidene malonate), TPN (tetrachloroisophthalonitrile), etc., and preferred ones are organocopper (Oxine-copper), cupric hydroxide, trifluoromazole ((E)-4-chloro-α,α,α-trifluoro-N-(1-imidazol-1-yl-2-propoxyethylidane)-o-toluidine), iprodione (3-(3,5-dichlorophenyl)-N-isopropyl-2,4-dioxoimidazolidine-1-carboxamide), hexaconazole ((RS)-2-(2,4-dichlorophenyl)-1-(1H-1,2,4-triazol-1-yl)hexan-2-ol), and these may be used in combination of two or more.

[0078] In the case of insecticides, pyrethroid insecticides include permethrin ((3-phenoxybenzyl = (1RS,3RS)-(1RS,3RS)-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylate), cybermethrin ((RS)-α-cyano-3-phenoxybenzyl = (1RS,3RS)-(1RS,3RS)-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylate), and fenvalerate (α-cyano-3-phenoxybenzyl-2-(4-chlorophenyl)-3-methylbutanoate), and organophosphate 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]dimethyl phosphorothiothionate), Dimethoate (dimethyl S-(N-methylcarbamoylmethyl)dithiophosphate), Elsan (S-[α-(ethoxycarbonyl)benzyl]dimethyl phosphorothiothionate), Bayjit (O,O-dimethyl-O-(3-methyl-4-methylthiophenylthiophosphate)), Carbamate insecticides include Bassa (O-sec-butylphenyl methylcarbamate), MTMC (m-tolyl methylcarbamate), Meopal (3,4-dimethylphenyl-N-methylcarbamate), Methomyl (S methyl-N [(methylcarbamoyl)oxy]thioacetimide), and the like, and preferred ones include permethrin, DDVP (dimethyl 2,2-dichlorovinyl phosphate), and methomyl (S-methyl-N-[(methylcarbamoyl)oxy]thioacetimide).

[0079] Furthermore, examples of natural insecticides include pyrethrins derived from pyrethrum, piperonyl butoxide, rotenone derived from the legume shrub Derris, and nicotine (3-(1-methyl-2-pyrrolidinyl)pyridine sulfate). Examples of 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 parachlorobenzenesulfonate), phenisobromorate (isopropyl 4,4'-dibromobenzilate), tetradifon (2,4,5,4'-tetrachlorodiphenylsulfone), fenothiocarb (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), fenbutatin oxide (hexakis(β,β-dimethylphenylethyl)distannoxane), hexythiazox (trans-5-(4-chlorophenyl)-N -cyclohexyl-4-methyl-2-oxothiazolidine-3-carboxamide), amithrastin (3-methyl-1,5-bis(2,4-xylyl)-1,3,5-triazapenta-1,4-diene), etc., and preferred ones include phenisobromorate (isopropyl 4,4'-dibromobenzilate), amithrastin (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-phenoxypyrazol-4-ylmethyleneaminooxy)-p-toluate).

[0081] Examples of herbicides include acid amide herbicides such as Stam (3,4-dichloropropionanilide, DCPA). Examples of urea herbicides include DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea). Examples of bipyridylium herbicides include paraquat (1,1'-dimethyl-4,4'-bipyridium dichloride) and diquat (6,7-dihydrodipyrido[1,2-a:2',1'c]pyrazinedium dibromide). Examples of diazine herbicides include bromacil (5-bromo-3-sec-butyl-6-methyluracil). Examples of triazine herbicides include simazine (2-chloro-4,6-bis(ethylamino)-1,3,5-triazine). Examples of nitrile herbicides include 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 phenol 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 the amino acid herbicides include glyphosate (N-(phosphonomethyl)glycine or a salt thereof) and glyphosinate (ammonium-DL-homoalanin-4-yl(methyl)phosphinate).Examples of aliphatic herbicides include sodium trichloroacetate (TCA). Suitable herbicides include 2,6-dichlorobenzonitrile (DBN), 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU), 1,1'-dimethyl-4,4'-bipyridium dichloride (paraquat), and 6,7-dihydrodipyrido[1,2-a:2',1'c]pyrazinedium dibromide (diquat).

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

[0083] Other commercially available spreading agents include, for example, Makupika (polyoxyethylene methylpolysiloxane 93.0% by mass, manufactured by Ishihara Biosciences Co., Ltd.), Squash (sorbitan fatty acid ester 70% by mass, polyoxyethylene resin acid ester 5.5% by mass, manufactured by Kao Corporation), Avion-E (paraffin 24% by mass, manufactured by Avion Co., Ltd.), Petan V (paraffin 42.0% by mass, manufactured by Agro Kanesho Co., Ltd.), Submerge (sodium alkylbenzene sulfonate 50% by mass, manufactured by Syngenta), and Brake. Examples of such products include Xulu (80% polyoxyalkyleneoxypropylheptamethyltrisiloxane, 20% polyoxyalkylenepropenyl ether by mass, manufactured by Sankei Chemical Co., Ltd.), Agridex (71.5% paraffin oil by mass, manufactured by BASF), Agristic (40% alkylaryl polyglycol ether by mass, manufactured by BASF), D-Flava (82% oleic acid by mass, manufactured by Tita Agung Jaya), and Miracle 240 (75% trisiloxane by mass, manufactured by Evonik).

[0084] The adjuvant contains a surfactant, and such surfactants include nonionic surfactants, anionic surfactants, cationic surfactants and 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 alkylsulfonamides, polyoxypropylene block copolymers, polyoxyethylene oleyl ethers, polyoxyalkylene alkylphenols, polyoxyalkylene alkyl polyglycosides, and mixtures of two or more of these.

[0086] Cationic surfactants include alkylamine ethylene oxide adducts, alkylamine propylene oxide adducts, such as tallowamine ethylene oxide adducts, oleylamine ethylene oxide adducts, soyamine ethylene oxide adducts, cocoamine ethylene oxide adducts, synthetic alkylamine ethylene oxide adducts, octylamine ethylene oxide adducts, dialkylamine derivatives, and mixtures thereof. The dialkylamine derivatives include dialkylmonomethylhydroxyethylammonium propionate, dialkylmonomethylbenzalkonium chloride, dialkylmonomethylethylammonium ethyl sulfate, and the like.

[0087] Typical anionic surfactants can be obtained in aqueous solution or in solid form, and examples thereof include sodium mono- and di-alkylnaphthalene sulfonates, sodium alpha-olefin sulfonate, sodium alkanesulfonate, alkyl sulfosuccinates, alkyl sulfates, polyoxyalkylene alkyl ether sulfates, polyoxyalkylene alkylaryl ether sulfates, polyoxyalkylene styryl phenyl ether sulfates, mono- and di-alkylbenzene sulfonates, alkylnaphthalene sulfonates, formaldehyde condensates of alkylnaphthalene sulfonates, alkyldiphenyl ether sulfonates, olefinic sulfonic acids, etc. salts, mono- and di-alkyl phosphates, polyoxyalkylene mono- and di-alkyl phosphates, polyoxyalkylene mono- and diphenyl ether phosphates, polyoxyalkylene mono- and di-alkyl phenyl ether phosphates, polycarboxylates, fatty acid salts, linear and branched alkyl polyoxyalkylene ether acetates or salts thereof, alkenyl polyoxyalkylene ether acetates or salts thereof, linear and branched alkylamido polyoxyalkylene ether acetates or salts thereof, stearic acid and its salts, oleic acid and its salts, N-methyl fatty acid taurides, mixtures of two or more thereof, and the like (including sodium, potassium, ammonium, and amine salts).

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

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

[0090] After the CPU 11 generates treatment proposal information (infection stage map) and transmits it to the user terminal 200, after a predetermined period of time (e.g., several months, six months, etc.) has elapsed, it may newly sense the palm tree using a remote sensing device to obtain sensing data, and again generate treatment proposal information (infection stage map) based on the sensing data and transmit it to the user terminal 200.

[0091] FIG. 7 is a diagram showing an example of an infection stage map that is generated in this embodiment and displayed on the application of the user terminal 200.

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

[0093] The CPU 11 colors each palm area R on the RGB image in 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, by referring to the treatment information database 33, sets a link for each palm area 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 treatment or therapeutic procedure determined for the infection stage corresponding to that region is displayed, for example, by a pop-up.

[0095] FIG. 8 is a diagram showing an example in which treatment information (or therapeutic treatment information) is displayed when one area R of the infection stage map is selected by the user.

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

[0097] Also, for example, at the bottom of the pop-up 80, a navigation button 82 is displayed for launching a map application and navigating the user terminal 200 to the location of each palm. The user can check the treatment of each palm on the infection stage map, prepare agents such as pesticides and adjuvants required for treatment (treatment or prevention), and start navigation using the navigation button 82 to begin treatment.

[0098] Furthermore, not only is a pop-up 80 displayed when a user selects each area R on the infection stage map, but when a user selects each infection stage on the infection stage map, the range of palm trees for which treatment according to the infection stage should be performed may be highlighted on the map, and information indicating the area, etc. may be output. Each infection stage may be selected, for example, by selecting a box for each infection stage in the legend G.

[0099] Furthermore, when determining the treatment for each palm tree, the CPU 11 may take into consideration not only the infection stage of Ganoderma in that palm tree, but also the infection stage of Ganoderma in palm trees adjacent to that palm tree, thereby enabling more accurate treatment proposals.

[0100] That is, the CPU 11 may set a link to information indicating a predetermined treatment similar to the treatment for palms determined to be in a diseased stage (e.g., infection stages 1 to 4) in the region R of palms determined to be in a non-disease stage (infection stage 0) on the infection stage map and may not set the link in the region of palms not adjacent to the palms determined to be in a diseased stage. The above-mentioned predetermined treatment may be the same treatment as the treatment for palms determined to be in infection stage 1.

[0101] Furthermore, the CPU 11 may change the treatment for a palm tree with infection stage 0 depending on whether the palm tree is adjacent to a palm tree with infection stage 1 or higher in multiple directions or adjacent to the palm tree in only one direction. That is, even if the palm tree is healthy, if it is adjacent to an infected palm in multiple directions, it is considered that there is a high possibility 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 in only one direction.

[0102] Furthermore, the CPU 11 may set links to information indicating different treatments for the palm region R adjacent to the palm determined to be at infection stage 2 (or infection stage 3) and the palm region R adjacent to the palm determined to be at infection stage 3 (or infection stage 4) on the infection stage map among the palm determined to be at infection stage 1 (or infection stage 2). That is, even for a palm determined to be at infection stage 1, if it is adjacent to a palm determined to be at infection stage 2 and if it is adjacent to a palm determined to be at infection stage 3, the latter is expected to have a worse infection stage, so the importance of treatment (concentration of pesticides or adjuvants) may be increased.

[0103] The CPU 11 may also display a treatment map as shown in FIG. 9 upon request, by determining the treatment stage for a palm determined to be at infection stage 1 or infection stage 2, taking into consideration the areas of adjacent palms as described above. In the example of FIG. 9, an example is shown in which the area R is surrounded by different types of lines for different treatment stages, superimposed on the infection stage map, but the area R may be displayed in different colors for different treatment stages separately from the infection stage map. The infection stage map allows the disease state of the palm to be understood, and the treatment treatment of the plantation can be supported based on the treatment treatment map. The treatment treatment map can also display the order amount of additives such as the amount of pesticides and adjuvants prepared by the plantation. Furthermore, it is also possible to display a map for each treatment means, such as an area for adding pesticides, an area for adding pesticides and adjuvants, etc.

[0104] In determining the stage of treatment according to the infection stage of the surrounding plants, the adjacent range of the plants may be set appropriately according to the type of the plants. The adjacent range may be set in units of the number of plants or in units of plots (areas). Setting information on the adjacent range may be input each time via the user terminal 200 or the operation reception unit 17 of the monitoring server 100, or information on the adjacent range previously determined according to the type of plants may be stored in the storage unit 18.

[0105] As shown in Fig. 9, in this embodiment, the adjacent range is set in units of adjacent palm trees. However, the adjacent range is not limited to one tree. Also, for example, in the case of a disease caused by spore dispersion, the disease may affect a section that includes multiple plants, so the adjacent range may be set in units of sections (areas), as in the case of soybeans, which will be described later.

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

[0107] [Variations] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and it goes without saying that various modifications can be made without departing from the spirit of the present invention.

[0108] In the above embodiment, the tree infection monitoring system monitors the infection status of the trees in the farm with the pathogen, determines the infection stage, and proposes a treatment (cure or preventive) method according to the infection stage. In addition, the tree infection monitoring system may perform a process to confirm whether the treatment was effective after the treatment.

[0109] Specifically, the monitoring server 100 performs the processing of FIG. 5 described above, and after the user takes the necessary measures (such as spraying pesticides on palm trees) according to the infection stage map, the monitoring server 100 performs the monitoring processing of steps 52 to 54 of FIG. 5 again after a certain period of time (e.g., several months) has elapsed.

[0110] Once the infection stage of the palm tree has been determined by the second monitoring process (step 54), the result of this determination is compared with the infection stage determination result from the first monitoring process stored in the monitoring information database 32 to confirm the control effect of the treatment carried out after the first monitoring process.

[0111] Specifically, if the result of the above comparison is that the infection stage at the time of the second monitoring process for a particular palm tree is lower than the infection stage at the time of the first monitoring process, CPU 11 determines that there has been a control effect, and if the two infection stages are the same or the infection stage at the time of the second monitoring process is more advanced than the infection stage at the time of the first monitoring process, CPU 11 determines that there has been no control effect.

[0112] The information regarding the confirmed control effect is stored in the treatment information database 33 as information regarding the degree of improvement from the treatment at the initial or previous monitoring, and is transmitted from the monitoring server 100 to the user terminal 200 in response to a user request or automatically.

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

[0114] In the above embodiment, the monitoring server 100 stores treatment information corresponding to the infection stage in advance in the treatment information database 33, and when the infection stage is determined, the monitoring server 100 determines the treatment by referring to the stored information. Alternatively, the monitoring server 100 may not store treatment information in advance, but may transmit request information requesting treatment information corresponding to the infection stage to the proposer terminal 300 when the infection stage is determined, and may generate the above progression stage map by determining the treatment based on the treatment information transmitted from the proposer terminal 300 in response.

[0115] In the above embodiment, only one monitoring server 100 is shown, but the processes executed by the monitoring server 100 may be distributed and executed by a plurality of servers. For example, a server that stores remote sensing data and treatment information and a server that generates the learning model and determines the infection stage of Ganoderma based on the model may exist separately.

[0116] In the above embodiment, the monitoring server 100 may determine the next monitoring time 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 monitoring time. For example, the monitoring frequency may be set to be higher as the ratio of plants with serious disease progression stages at a certain time is higher, or the disease progression speed is faster in the time series.

[0117] The information on treatments according to the infection stage (pesticides, adjuvants, microorganisms to be used, and their amounts and frequency of use) shown in Fig. 6 above is merely an example and may be changed as appropriate depending on the plant to be treated, etc. Also, the display modes of the infection stage maps shown in Figs. 7 and 8 are merely an example and may be changed as appropriate.

[0118] In the above embodiment, palm is used as an example of a plant, and Ganoderma is used as a pathogen, but the combination of plant and pathogen is not limited to this, and may be used to judge disease in herbaceous plants. For example, the present invention may be used to judge infection with Panama disease or new Panama disease in bananas, rust disease in soybeans, root rot disease in rubber, blast disease in rice, and rust disease in grapes. Furthermore, the present invention may be used to judge disease in herbaceous plants such as corn, potato, and rice. The present invention is preferably used for diseases that are difficult to distinguish early infection with the naked eye. In addition, in the present invention, plant disease is not limited to infection with infectious diseases caused by fungi such as mushrooms and molds, bacteria, viruses, etc., but also includes diseases other than infectious diseases such as pests and parasites. The "progression stage" includes not only the infection stage of an infectious disease caused by a plant, but also the progression stage of a disease other than an infectious disease caused by a plant. However, the present invention is preferably used for treating plants infected with an infectious disease.

[0119] Hereinafter, an embodiment will be described in which the plant is soybean and the pathogen is soybean rust (hereinafter, simply referred to as rust). There are two known species of rust, an Asian species (Phakopsora pachyrhizi) and a Latin American species (P. meibomiae), and the Asian species is particularly problematic.

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

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

[0122] Preventive treatment of soybeans at infection stage 1 is also suggested by spraying with an aqueous solution of pesticide and adjuvant (pesticide alone is fine since the soybeans are healthy) or adding microorganisms.

[0123] As the pesticide, for example, hexaconazole is used, and its concentration is set to 0.01% to 0.001%. As the adjuvant, Kao Adjuvant A-134 is used, and its concentration is set to 0.1% to 1%. The total amount of both added is 100L to 200L / 1000m. 2 The treatment frequency is set at least once a year.

[0124] To add microorganisms, fertilizer containing microorganisms (microorganism type: Bacillus, etc.) is used. When the product name CEASE (BioWorks) is used as the fertilizer, the concentration and number of treatments are 0.05% to 0.3%, the amount of addition is 100L to 200L / 1000m2, and the number of treatments is at least once a year.

[0125] To add silicon, a silicon-containing fertilizer is used. For example, when the product name AgroSilicio (manufactured by Harsco Minerais Ltda) is used as the fertilizer, the concentration and number of treatments are 100 to 800 kg / 1000 m. 2 The treatment frequency is set at least once a year.

[0126] In soybeans at infection stage 1, small light brown to yellowish brown spots are seen on the lower leaves and on relatively mature leaves of the main stem from the lateral branches, or these spots are raised. As shown in the figure, for soybeans at infection stage 1, treatment stage 1 is proposed, which involves spraying an aqueous solution of pesticide and adjuvant onto the plant surface, as well as adding a microorganism.

[0127] In both the spraying onto the plant surface and the soil irrigation treatment, hexaconazole is used as the pesticide and Kao Adjuvant A-134 is used as the adjuvant. The concentration is 0.1% to 1%. The total amount of both is 100L to 200L / 1000m 2 The treatment frequency is set at least once a year.

[0128] To add microorganisms, fertilizer containing microorganisms (microorganism type: Bacillus, etc.) is used. For example, when the product name CEASE (BioWorks) is used as the fertilizer, the concentration and number of treatments are 0.05% to 0.3%, and the amount of addition is 100L to 200L / 1000m. 2 The treatment frequency is set at least once a year.

[0129] In soybeans at infection stage 2, the leaves turn yellow and turn brown to dark brown, or uredospores (light brown powder) are observed to be ejected. As shown in the figure, for soybeans at infection stage 2, spraying an aqueous solution of pesticide and adjuvant onto the plant surface is proposed as treatment stage 2.

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

[0131] In soybeans at infection stage 3, polygonal, slightly swollen, dark brown spots (teliospores) can be seen under the epidermis around the uredipore layer. As shown in the figure, for soybeans at infection stage 3, spraying with an aqueous solution of pesticide and adjuvant is proposed as treatment stage 3.

[0132] In the treatment stage 3, the pesticides and adjuvants used are the same as those in the infection stage 1, but 0.04 to 0.15% azoxystrobin is added as the pesticide. The number of treatments is increased to at least twice a year for each treatment.

[0133] Soybeans at infection stage 4 are those in which the damaged leaves have turned yellow and fallen off, and as shown in the figure, treatment stage 4 involves removing the infected soybean plant body.

[0134] FIG. 11 is a diagram showing an example of a progress stage map generated in this embodiment.

[0135] The infection stage map of palm shown in FIG. 7 shows the area R of each palm in a different color for each infection stage of Ganoderma determined above. As shown in the figure, the infection stage map in this embodiment also shows the area Rs of each soybean in a different color for each infection stage of rust determined above, but in addition to this, in this embodiment, the CPU 11 divides the entire area of ​​the progression stage map into a plurality of sections Rf and displays the infection stage for each section Rf, for example, by numbers. The infection stage may be indicated by a change in color or other display mode instead of a number. The section Rf corresponds to a division of the farm into a preset area (for example, 1 ha).

[0136] That is, the infection stage of the soybean with the most advanced infection stage among the soybeans contained in each section Rf is shown as the infection stage of the section Rf with a corresponding number, for example (0.5, 1, 2, 3). Note that 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 than in the case of palm trees described above, and in the case of rust disease, germination, maturation of the lesions, and scattering of spores can cause the infection to spread more widely from diseased soybeans to healthy soybeans around them, and therefore it is considered appropriate to capture the disease stage in a wider area Rf, not just in the soybeans adjacent to the diseased soybeans.

[0138] In the progression stage map of this embodiment, a pop-up showing the treatment information shown in FIG. 10 is also displayed, similar to the pop-up 80 in FIG. 8, starting from the area Rs or section Rf selected by the user.

[0139] Moreover, instead of the infection stage map, the CPU 11 may display a treatment map similar to that shown in Fig. 9. Fig. 12 is a diagram showing an example of a treatment map generated in this embodiment.

[0140] The palm treatment map shown in Fig. 9 is a map in which the regions R are surrounded by different types of lines for different treatment stages, superimposed on the infection stage map. On the other hand, as shown in the figure, in this embodiment, the infection stage of the soybean that is the most infected in the section Rf among the soybeans contained in each section Rf is set as the infection stage of the section Rf, and each section Rf is displayed in a different display mode according to the treatment (treatment or prevention) according to the infection stage of the Rf.

[0141] In the example shown in the same figure, the treatment map is superimposed on the above-mentioned infection stage map, with the section Rf surrounded by different types of lines for each different treatment stage, but separate from the infection stage map, the section Rf may also be shown in different colors for each different treatment stage.

[0142] Thus, in the case of soybeans, when determining the treatment stage according to the infection stage of the surrounding soybeans, the adjacent range is set by the above-mentioned section Rf (area), the infection stage is 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 section Rf.

[0143] 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 the palm tree or soybean, the monitoring server 100 may determine and execute whether to display the infection stage of the plant and its adjacent range on a single plant basis, as in the palm tree example, or on a plot basis, as in the soybean example (i.e., whether to treat on a single plant basis or on a plot basis), according to pre-settings or input from the user terminal 200.

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

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

[0146] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed in this specification or any hardware known to be programmed to realize or perform the described functions.

[0147] If the hardware is a processor that is considered to be a type of circuitry, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and / or processor. Also, the configuration described as a unit or means in this specification can be implemented as a 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...Procedure information 82…Navigation button 100…Monitoring server 200...User terminal 300…Proposer terminal R: Palm area Rs: Soybean area Rf…Map division area

Claims

1. An information processing system for monitoring a plant, comprising: A storage unit that stores treatment information regarding treatments to be taken according to a plurality of disease progression stages of the plant, and a learning model that has learned features of each sensing data obtained by remote sensing the plant at the plurality of disease progression stages; acquiring image data based on first sensing information obtained by capturing an image of the plant using a spectral camera or an RGB camera, which is a first sensing means of the remote sensing device, and determining a progression stage of a disease of the plant in the image data based on the acquired image data and the learning model; determining a treatment to be taken according to the determined progression stage based on the determined progression stage and the stored treatment information, and generating treatment proposal information that proposes the treatment; Control unit and An information processing system comprising:

2. the remote sensing device acquires second sensing information of the plant other than the first sensing information by a second sensing means; the control unit determines a progression stage of a disease of a plant in the image data based on the first sensing information, the second sensing information, and the learning model.

2. The information processing system according to claim 1.

3. The control unit transmits the generated treatment proposal information to a user terminal of a user who owns or manages the plant.

3. The information processing system according to claim 1 or 2.

4. the control unit receives information regarding at least one of the seedling variety, tree age, generation, tree height, soil type in which the plant is growing, and terrain type from the user terminal, and determines a progression stage of the disease of the plant based on the received information, the image data, the remote sensing data, and the learning model.

4. An information processing system according to claim 1.

5. When information on the seedling variety, the age, the generation, and the height of the plant is missing from the information received from the user terminal, the control unit calculates and complements the missing information based on the sensing information.

5. The information processing system according to claim 4.

6. The control unit transmits information regarding the progression stage to a proposer terminal that proposes the treatment, receives treatment information corresponding to the progression stage transmitted from the proposer terminal, and stores the treatment information in the storage unit.

6. An information processing system according to claim 1.

7. the control unit receives, from the user terminal, location information of an area in the user's farm where the plurality of plants are growing, transmits the location information to the remote sensing device, and receives, from the remote sensing device, the image data of the area captured based on the location information.

5. The information processing system according to claim 3.

8. The control unit generates, as the generated treatment proposal information, a progression stage map image in which an area of ​​each plant on the image is shown in a different color according to the different progression stages.

8. An information processing system according to claim 3.

9. The control unit sets a link for enabling access to information indicating the treatment in each area of ​​the progression stage map image. The information processing system according to claim 8.

10. the control unit sets the link to information indicating a predetermined treatment similar to the treatment for the plant determined to be at the diseased stage in a region of the plant determined to be at the non-disease stage and adjacent to the plant determined to be at the diseased stage on the disease progression stage map image, and does not set the link in a region of the plant not adjacent to the plant determined to be at the diseased stage. The information processing system according to claim 9.

11. The advanced stage includes a first disease stage, a second disease stage more advanced than the first disease stage, and a third disease stage more advanced than the second disease stage; the control unit sets, on the disease progression stage map image, links to information indicating different treatments in a first adjacent region of a plant adjacent to a plant determined to be at the second disease stage, among the plants determined to be at the first disease stage, and in a second adjacent region of a plant adjacent to a plant determined to be at the third disease stage.

11. The information processing system according to claim 8.

12. The control unit divides an entire area represented by the progression stage map image into a plurality of sections, and displays each section of the progression stage map image in a different display mode in accordance with a treatment corresponding to a progression stage of a plant that is the most advanced among the plants included in each section. The information processing system according to claim 8.

13. The control unit acquires new image data of the plant captured by the remote sensing device after a predetermined period has elapsed after generating the treatment proposal information, generates the treatment proposal information based on the image data, and transmits the treatment proposal information to the user terminal.

12. An information processing system according to claim 3.

14. An information processing device for monitoring a plant, A storage unit that stores treatment information regarding treatments to be taken according to a plurality of disease progression stages of the plant, and a learning model that has learned features of each sensing data obtained by remote sensing the plant at the plurality of disease progression stages; receiving image data based on first sensing information obtained by capturing an image of the plant using a spectral camera or an RGB camera, which is a first sensing means of the remote sensing device, and determining a progression stage of a disease of the plant in the image data based on the received image data and the learning model; determining a treatment to be taken according to the determined progression stage based on the determined progression stage and the stored treatment information, and generating treatment proposal information that proposes the treatment; Control unit and An information processing device comprising:

15. the remote sensing device acquires second sensing information of the plant other than the first sensing information by a second sensing means; the control unit determines a progression stage of a disease of a plant in the image data based on the first sensing information and the second sensing information received from the remote sensing device and the learning model; 15. The information processing device according to claim 14.

16. The control unit transmits the generated treatment proposal information to a user terminal of a user who owns or manages the plant.

16. The information processing device according to claim 14 or 15.

17. the control unit receives information regarding at least one of the seedling variety, tree age, generation, tree height, soil type in which the plant is growing, and terrain type from the user terminal, and determines a progression stage of the disease of the plant based on the received information, the image data, the remote sensing data, and the learning model.

17. An information processing device according to claim 14.

18. The control unit generates, as the generated treatment proposal information, a progression stage map image in which an area of ​​each plant on the image is shown in a different color according to the different progression stages.

18. An information processing device according to claim 16 or 17.

19. The control unit sets a link for enabling access to information indicating the treatment in each area of ​​the progression stage map image. The information processing device according to claim 18.

20. the control unit sets the link to information indicating a predetermined treatment similar to the treatment for the plant determined to be at the diseased stage in a region of the plant determined to be at the non-disease stage and adjacent to the plant determined to be at the diseased stage on the disease progression stage map image, and does not set the link in a region of the plant not adjacent to the plant determined to be at the diseased stage. The information processing device according to claim 19.

21. The control unit divides an entire area represented by the progression stage map image into a plurality of sections, and displays each section of the progression stage map image in a different display mode in accordance with a treatment corresponding to a progression stage of a plant that is the most advanced among the plants included in each section. The information processing device according to claim 18.

22. An information processing method for monitoring a plant, comprising: storing treatment information regarding treatments to be taken according to a plurality of disease progression stages of the plant, and a learning model that has been trained on features of each of sensing data obtained by remote sensing the plant at the plurality of disease progression stages; acquiring image data based on first sensing information obtained by capturing an image of the plant using a spectral camera or an RGB camera that is a first sensing means included in a remote sensing device; determining a stage of disease progression of a plant in the image based on the acquired image data and the learning model; Based on the determined progression stage and the stored treatment information, a treatment to be taken according to the determined progression stage is determined, and treatment proposal information for proposing the treatment is generated. Information processing methods.

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