Information processing systems, information processing methods, and programs

Abstract

To efficiently diagnose the infection status of plants and trees with pathogenic fungi over a wider area. [Solution] The information processing system comprises a control unit. The control unit acquires area information indicating an area where the fruit or sap of plants has been harvested, and biochemical analysis information of the extract of the plants. Based on the analysis information, it diagnoses the degree of infection of the plants in the area with pathogenic fungi, and outputs first diagnostic information including infection degree information indicating the diagnosed degree of infection, information indicating whether or not action is required to address the infection, and the area information.

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method, and a program for detecting the infection of pathogenic bacteria in plants from the analysis information of plant extracts.

Background Art

[0002] Conventionally, there has been a technique for diagnosing the infection of a plant with a pathogenic bacterium based on an image of the plant taken. For example, in Patent Document 1 below, farm demarcation data is received, a farm area is determined based on the farm demarcation data, input data associated with the farm area and including a plurality of pixel sets is retrieved from a plurality of data sources, and for each of the plurality of pixel sets, using one or more regression models, based on the input data, crop pest risk estimation for each of the plurality of pixel sets or crop disease risk estimation for each of the plurality of pixel sets or both are included to determine crop risk data, and based on the crop risk data, the farm area is classified into a plurality of farm sub-areas in which each farm sub-area defines its risk level category.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the technique using an image of a farm as described in Patent Document 1 above, it is necessary to take the image with equipment such as a satellite, an aircraft, a drone, etc. However, while a farm covers an extremely wide area, there are limits to the radio wave reach and operating battery capacity of those imaging devices, and it is not possible to efficiently diagnose the infection of diseases in a vast farm.

[0005] The object of the present invention is to provide an information processing system, information processing method, and program that can efficiently diagnose the infection status of plants with pathogenic fungi over a wider area. [Means for solving the problem]

[0006] An information processing system according to one embodiment of the present invention comprises a control unit. The control unit acquires area information indicating an area where the fruit or sap of a plant has been harvested, and biochemical analysis information of the extract of the plant. Based on the analysis information, it diagnoses the degree of infection of the plant disease fungus in the area, and outputs first diagnostic information including infection degree information indicating the diagnosed degree of infection, information indicating whether or not action is required to address the infection, and the area information.

[0007] Information processing methods according to other embodiments of the present invention are: Area information indicating the area where the fruit or sap of the plant was harvested, and biochemical analysis information of the extract of the plant are obtained. Based on the aforementioned analysis information, the degree of infection of plant disease fungi in the area is diagnosed. This includes outputting diagnostic information that includes infection level information indicating the diagnosed level of infection, information indicating whether or not a response to the infection is necessary, and area information.

[0008] A program according to yet another embodiment of the present invention is for a computer, A step of obtaining area information indicating the area where the fruit or sap of the plant was harvested, and biochemical analysis information of the extract of the plant. The steps include: diagnosing the degree of infection of plant disease fungi in the area based on the aforementioned analysis information; The system is configured to output diagnostic information that includes infection level information indicating the diagnosed level of infection, information indicating whether or not a response to the infection is necessary, and area information. [Effects of the Invention]

[0009] According to the present invention, it is possible to efficiently diagnose the infection status of plants with pathogenic fungi over a wider area. However, this effect is not limited to the present invention. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram shows the configuration of a palm disease diagnosis system according to one embodiment of the present invention. [Figure 2] This diagram shows the hardware configuration of a diagnostic server according to one embodiment of the present invention. [Figure 3] This diagram shows the configuration of the database of a diagnostic server according to one embodiment of the present invention. [Figure 4] This table shows the method for diagnosing the degree of infection (risk level) using a diagnostic server according to one embodiment of the present invention. [Figure 5] This is a flowchart showing the flow of disease infection diagnosis processing by a diagnostic server according to one embodiment of the present invention. [Figure 6] This figure shows an example of diagnostic information generated by a diagnostic server according to one embodiment of the present invention and output to a user terminal. [Figure 7] This figure shows an example of corresponding information displayed after transitioning from the diagnostic information in Figure 6. [Figure 8] This figure shows an example of a second infection area map generated by a diagnostic server according to one embodiment of the present invention. [Figure 9] This figure shows an example of treatment information displayed after transitioning from the second infection area map in Figure 7. [Modes for carrying out the invention]

[0011] Embodiments of the present invention will be described below with reference to the drawings.

[0012] [System Configuration] As shown in Figure 1, this plant disease diagnosis system includes a diagnostic server 100 on the Internet 50, a user terminal 200, a testing facility terminal 300, and an oil extraction plant terminal 400.

[0013] This system is a system for diagnosing the degree of infection (risk level) of plants (palm plants) in a plantation with a pathogenic fungus (Ganoderma) and proposing a countermeasure (treatment or prevention of Ganoderma) according to the degree of infection. Here, examples of palm plants include oil palm (Elaeis guineensis), coconut palm, etc. The diagnosis of this system is performed for the oil palm, which is one of the palm plants, and the extract is palm oil extracted at an oil mill or waste liquid generated in the oil extraction process, and the diagnosis is performed by biochemically analyzing the extract.

[0014] The diagnosis server 100 is a server (information processing device) operated by the operator of the above disease diagnosis service. The diagnosis server 100 is connected to the user terminal 200, the inspection institution terminal 300, and the oil mill terminal 400 via the Internet 50.

[0015] The user terminal 200 is a terminal used by the owner or administrator (user) of the above plantation, and is, for example, a smartphone, a mobile phone, a tablet PC (Personal Computer), a notebook PC, a desktop PC, etc.

[0016] The fruits (FFB (fresh fruit bunch)) of the oil palm harvested in the plantation are transported to an oil mill by a truck or the like and palm oil is extracted. For one oil mill, FFB is transported from about 10,000 ha of surrounding plantations. Harvesting is performed regularly, for example, in units of a predetermined area (for example, a section of about 10 ha) in the plantation. When transporting to the oil mill, information identifying the plantation and area of the harvesting source is also transmitted. Examples of the information for identifying the area, that is, the area information, include the ID of the plantation, the ID of the area, the address, the latitude and longitude, etc.

[0017] Samples of palm oil or wastewater extracted during the oil extraction process at this oil extraction plant are transported to a testing laboratory. At the testing laboratory, biochemical tests (e.g., quantitative PCR (Polymerase Chain Reaction)) are performed on the oil or wastewater to determine the degree of infection with Ganoderma lucidum in palm trees. The wastewater mentioned above is a liquid mainly composed of water generated during the oil extraction process (fumigation, pressing, oil-water separation, or refining process).

[0018] Palm oil or wastewater is targeted for PCR testing because, in situations where palm oil is suspected to be infected with Ganoderma lucidum, it is thought that Ganoderma lucidum spores or hyphae often adhere to the surface of palm fruit or enter the fruit via dust or spores. Therefore, it is believed that the DNA of these spores or hyphae can be detected in palm oil or wastewater after oil extraction. In plantations with a high degree of infection, the concentration of hyphae or spores in the air will be relatively high, making it possible to understand the infection status of the plantation from the detection values ​​of quantitative PCR.

[0019] The oil extraction plant terminal 400 is a terminal used by the oil extraction plant staff, and can be a smartphone, mobile phone, tablet PC, notebook PC, desktop PC, etc. The oil extraction plant terminal 400 transmits data on the amount of palm oil extracted (or wastewater) and the ratio of the amount of oil extracted (or wastewater) to the amount of FFB harvested (input) to the diagnostic server 100, as well as information identifying the harvested farm and its harvest area. If the oil extraction plant does not know the information of the farm's harvest area, the identification information of the farm's area may be transmitted to the diagnostic server 100 together with the farm's identification information from the farm's user terminal 200, rather than from the oil extraction plant terminal 400. The area is typically a part of the farm, but depending on the size of the farm, it may be the entire area of ​​the farm. The amount of FFB harvested corresponds to the "amount of fruit or sap harvested from plants."

[0020] The testing facility terminal 300 is a terminal used by the person in charge of the testing facility, and can be, for example, a smartphone, mobile phone, tablet PC, notebook PC, or desktop PC. The testing facility terminal 300 transmits the infection level test result data to the diagnostic server 100.

[0021] The oil extraction plant terminal 400 may, instead of sending data on the amount of oil extracted (waste liquid) and the ratio of the FFB to the harvest amount (intake amount), as well as farm / area identification information, to the diagnostic server 100, and the diagnostic agency terminal 300 may then send this data, along with the inspection result data, to the diagnostic server 100.

[0022] The farm owner or manager (user) requests a disease diagnosis service for their farm from a disease diagnosis service provider, for example, through the oil extraction plant mentioned above. The user terminal 200 receives disease diagnosis information from the diagnosis server 100, namely information on the infected areas and degree of infection (risk level) of palm oil ganoderma on their farm, and information on appropriate responses (treatment or prevention). As will be described later, the information on the infected areas is converted into map information based on the farm ID and area ID of the area, and is shown on the map, for example, as the first infected area map.

[0023] Furthermore, if the farm determines from the above diagnostic information that there is a suspected Ganoderma infection in an area of ​​its farm, the next step is to request the diagnostic server 100 to further investigate the infection situation in that area by imaging the area from the air or on the ground using remote sensing equipment to diagnose the degree of infection.

[0024] The remote sensing device will use at least one of the following: a low-altitude flying object (drone / multicopter), a low-Earth orbit microsatellite, or a ground-based mobile device (mobile robot, unmanned vehicle, etc.), and will be equipped with sensing means.

[0025] The remote sensing device includes an optical RGB camera or a spectral camera as a sensing means, and can acquire reflected wavelengths as sensing data and synthesize an image or image information based on this data.

[0026] The diagnostic server 100 then acquires sensing data (spectral images and / or RGB images, and other sensing data) from the remote sensing device, and determines the degree of Ganoderma infection in each palm tree (for example, on a per-tree basis) 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 remotely sensing palm trees at multiple degrees of Ganoderma infection.

[0027] The diagnostic server 100 then determines the appropriate action based on the previously stored information regarding the actions to be taken according to each level of infection, notifies the user of the infection level, and generates action suggestion information (second infection area map) that proposes the action to the user, and sends it to the user terminal 200.

[0028] [Hardware configuration of the diagnostic server] As shown in Figure 2, the diagnostic 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 diagnostic 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, an inspection information database, and a correspondence information database, in addition to programs such as applications that execute each step of the palm disease diagnosis process described later. This program can be implemented as a program product.

[0035] The communication unit 19 consists of various modules for wireless communication, such as a NIC (Network Interface Card) for Ethernet or a wireless LAN, and is responsible for communication processing between the user terminal 200, the inspection agency terminal 300, and the oil extraction plant terminal 400.

[0036] Although not shown in the diagram, the basic hardware configurations of the user terminal 200, the inspection agency terminal 300, and the oil extraction plant terminal 400 are substantially the same as those of the diagnostic server 100, and each has a basic configuration for functioning as a computer, including a CPU, memory unit, communication unit, etc.

[0037] [Diagnostic server database configuration] As shown in Figure 3, the diagnostic server 100 has a farm information database 31, an inspection information database 32, and a corresponding 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 diagnostic 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 name, farm ID, and location (address), as well as information about the area (plot) included in the farm, the type (seedling variety), age, generation, and height of the palm trees during or in the past, and information about the palm tree growing environment, such as the soil type and terrain type (slope), for each farm, by receiving it from, for example, the user terminal 200.

[0039] The inspection information database 32 stores the biochemical test results (detection values ​​related to infection) of palm oil ganoderma infection received from the inspection agency terminal 300, associating them with data such as farm ID, area ID, and oil extraction date. The diagnostic server 100 calculates an evaluation value based on the detection value, information on oil extraction volume (or waste liquid volume) and harvest volume received from the oil extraction plant terminal 400, and information on weather conditions and environmental factors described later, and determines the infection level (risk level) set according to the evaluation value.

[0040] Furthermore, the inspection information database 32 also stores information received from the oil extraction plant terminal 400 regarding the amount of oil extracted (waste liquid) / FFB harvested (delivered) in association with the farm ID and area ID.

[0041] The infection level (risk level) is, for example, indicated on a five-point scale (described later), but is not limited to this. Furthermore, the units (levels) of this infection level may differ from the units of infection level used in the subsequent infection level diagnosis using remote sensing by low-altitude flying objects (drones), as described above.

[0042] The corresponding information database 33 stores information on the measures to be taken according to the degree of infection of the Ganoderma diagnosed above, information on treatment actions such as spraying, injection into plants, irrigation, fertilizer addition, and microbial addition, information on additives such as pesticides, pesticide efficacy enhancers, fertilizers, fungicides, and microorganisms used in these treatments, more specifically, the type of additive, efficacy, method of use (amount and number of times for spraying on the surface of the tree trunk, injection into the inside of the tree trunk, soil irrigation, etc.), amount added (per plant / time), concentration added, history of past treatments, and information on the degree of improvement since the initial and previous disease diagnosis.

[0043] Of these, information regarding treatment may be stored in the corresponding information database 33 when the diagnostic server 100 transmits information regarding the degree of infection of palm oil determined from the disease diagnosis results to the testing facility terminal 300, and the testing facility terminal 300 receives treatment information corresponding to the degree of infection.

[0044] These databases are referenced and used as needed in the palm disease diagnosis process performed by the diagnostic server 100, which will be described later.

[0045] [Method for diagnosing the degree of infection] Figure 4 is a table showing the diagnostic method for determining the degree of infection (risk level) by the diagnostic server 100.

[0046] As shown in the figure, in this embodiment, the degree of infection (risk level) of palm trees with Ganoderma is classified into, for example, five stages (risk levels 1 to 5). Then, information on appropriate responses according to the risk level can be provided to the user.

[0047] Risk level 1, for example, is when the assessment value is less than 5, indicating that the likelihood of infection is extremely low. In the case of risk level 1, the only course of action is to encourage the user to monitor themselves regularly.

[0048] Risk level 2, for example, is when the evaluation value is between 5 and 10, indicating a low probability of infection. In the case of risk level 2, users are encouraged to monitor themselves regularly and, if necessary, undergo remote sensing inspections using low-altitude flying objects (drones), etc.

[0049] Risk level 3 indicates, for example, an assessment value of 10 or more but less than 100, suggesting a mild infection. In the case of risk level 3, users are encouraged to take measures such as remote sensing inspections using low-altitude flying objects (drones) and control measures using pesticides or pesticide efficacy enhancers.

[0050] Risk level 4 indicates, for example, an assessment value of 100 or more but less than 1000, suggesting that the infection is progressing (moderately). In the case of risk level 4, users are strongly encouraged to take measures such as remote sensing inspections using low-altitude flying objects (drones) and control measures using pesticides or pesticide efficacy enhancers.

[0051] Risk level 5, for example, indicates a rating of 1000 or higher, suggesting a rapidly progressing (severe) infection. In the case of risk level 5, users are strongly urged to take immediate control measures using pesticides or pesticide efficacy enhancers.

[0052] The diagnostic server 100 stores the information corresponding to the table in the corresponding information database 33 and refers to it during diagnosis.

[0053] [Operation of the diagnostic server] Next, the operation of the diagnostic server 100 configured as described above will be explained. This operation is performed through the cooperation of the hardware of the diagnostic server 100, such as the CPU 11 and communication unit 19, and the software stored in the storage unit 18. For convenience, in the following explanation, the CPU 11 of the diagnostic server 100 will be considered the main operator. Figure 5 is a flowchart showing the flow of the disease diagnosis process of the diagnostic server 100.

[0054] As shown in the figure, the CPU 11 of the diagnostic server 100 first determines whether or not it has received the farm ID, area ID, and oil extraction volume (or waste liquid volume) / harvest volume information (including harvest time information) from the oil extraction plant terminal 400 (step 51).

[0055] If the CPU determines that it has received the above farm / area identification information and oil extraction / harvest yield information (Yes in step 51), the CPU 11 stores the oil extraction / harvest yield information and harvest time information in the inspection information database 32, associating them with the farm ID and area ID.

[0056] The reason for receiving information on the amount of oil extracted (or waste liquid) / harvested amount as the amount of extract is to convert the PCR detection value into a "test value for a fixed weight of FFB" by multiplying it by the ratio of the amount of oil produced (waste liquid) to the amount of FFB to be diagnosed that was brought in. This prevents differences in detection results due to oil extraction efficiency. The evaluation value is calculated using the following formula. Evaluation value = Detected value × (Amount of extract / Amount of harvested fruit or sap from plants related to the extract)

[0057] Next, the CPU 11 determines whether or not it has received PCR test result data (detection value data) for palm oil (or waste liquid) corresponding to the above farm ID and area ID from the testing facility terminal 300 (step 53).

[0058] If the CPU determines that it has received the detected value data (Yes in step 53), it is preferable for the CPU 11 to acquire environmental factor information and weather condition information (step 54).

[0059] Furthermore, environmental factor information includes the amount of Ganoderma spores, mycelium, their metabolites, or fruiting bodies present in the air or soil of the harvesting area at the time of harvest. This information is obtained, for example, from the servers of a company that monitors the amount of these fungi, and environmental factor values ​​are calculated from it. This is because Ganoderma is a commensal fungus, and even when palm trees are healthy and not infected, the amount of the fungus can be detected in the environment. Therefore, it is considered that correcting the PCR detection value with the amount of the fungus in the environment allows for a more accurate assessment of the degree of infection.

[0060] Furthermore, weather condition information, such as weather (especially whether or not there was rainfall), wind speed, and temperature for a predetermined period prior to the FFB harvest (for example, the last 10 days), is obtained from a server of a weather information provider. This is because, for example, there is a predetermined temperature range that stimulates the formation of Ganoderma fruiting bodies (organs for spore dispersal), and the amount of spore dispersal and atmospheric spore concentration decrease during rainfall and strong winds. Therefore, it is thought that the degree of infection can be accurately grasped by calculating a value (weather condition value) corresponding to the level of spore concentration in the environment from these weather conditions and correcting the test values ​​accordingly.

[0061] Next, CPU11 calculates an evaluation value based on the above oil extraction (or waste liquid volume) / harvest yield information, the detected value, the environmental factor value, and the weather condition value (step 55). Specifically, the evaluation value is calculated using the following formula. Evaluation value = {(Detected value × Weather condition value) × (Oil extraction amount (ml) / FFB amount delivered (kg))} - Environmental factor value Here, the weather condition value may be the reciprocal of a value calculated such that it increases as the temperature, weather, and wind speed increase, for example, to the point where the spore concentration in the environment is high. Furthermore, the environmental factor value is determined according to the amount of Ganoderma spores, hyphae, their metabolites, or fruiting bodies present in the atmosphere or soil.

[0062] The above formula can also be expressed as follows: Evaluation value = {(Detected value × Weather condition value) × (Amount of extract / Amount of harvested plant fruit or sap related to the extract)} - ​​Environmental factor value Here, the amount of harvested fruit or sap from plants related to the extract refers to the amount of palm fruit before extraction if the extract is oil extracted or waste liquid, such as from palm trees, or, if the extract is sap, such as from rubber trees, it refers to the number of rubber trees from which the sap was harvested.

[0063] Next, CPU 11 determines the infection level (risk level) according to the calculated evaluation value, based on the table shown in Figure 4 above (step 56).

[0064] Next, the CPU 11 generates diagnostic information (first diagnostic information) including an infection area map (first infection area map) corresponding to the infection level determined above (step 57), and transmits the diagnostic information to the user terminal 200 (step 58).

[0065] Figure 6 shows an example of diagnostic information generated by the diagnostic server 100 and output to the user terminal 200.

[0066] As shown in the figure, the diagnostic information includes a first infection area map 61. This first infection area map 61 shows the area within the farm corresponding to the area ID on the map data of the farm corresponding to the farm ID. This area is indicated, for example, by an area display frame 62.

[0067] The diagnostic information also includes the username (or farm name), location information (latitude and longitude) 63 for the area mentioned above, and infection level information 64 indicating the determined infection level. For example, near the infection level information 64, there is a hyperlink 65 to access corresponding response information (treatment information). In addition, the diagnostic information also includes data on the harvest date and harvest amount of FFB.

[0068] Furthermore, in the example shown in the figure, since the infection level (risk level) is high at 4, recommendation information is displayed strongly recommending detailed remote sensing inspection of the area using a drone or the like, along with treatment, and a button 66 for requesting this inspection is also provided. When the user presses the application button 66, the application information for the remote sensing inspection is sent to the diagnostic server 100, and a remote sensing inspection using a drone or the like is performed for the area corresponding to the area ID.

[0069] Figure 7 shows an example of the corresponding information displayed when accessed via the hyperlink 65 in Figure 6.

[0070] As shown in the figure, the corresponding information may include, for example, a graph (top of the figure) showing the trend in the degree of Ganoderma infection (risk level) from past harvests to the current harvest, as shown in the figure (a), and a graph showing the trend in FFB yield.

[0071] Both graphs also show when treatments were performed in the past, making it possible to understand how the infection rate and harvest yield changed as a result of those treatments.

[0072] In addition, as shown in Figure (b), the corresponding information may also include information on pesticides and pesticide efficacy enhancers used for treatment according to the degree of infection, instructions on how to use these agents, and hyperlinks to the purchase pages for these agents.

[0073] As mentioned above, a typical example of treatment in this embodiment is the addition of pesticides and pesticide efficacy enhancers to increase the effectiveness of the pesticides. In this embodiment, the number of treatments is increased as the degree of infection increases, and pesticide efficacy enhancers are used together with the pesticide as additives, and their concentrations are set to be higher.

[0074] In this embodiment, examples of pesticide active ingredients that may 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.

[0075] Examples of pesticide efficacy enhancers include, but are not limited to, the spreading agents listed in the 2011 edition of the Pesticide Handbook (published by the Japan Plant Protection Association on February 25, 2011). Examples of pesticide efficacy enhancers include, but are not limited to, the product name "Kao Adjuvant A-134".

[0076] When remote sensing inspection is performed using the drone or the like, the diagnostic server 100 analyzes the image data captured from above the area, diagnoses the infection level for each sub-area within that area (for example, each individual palm tree), and transmits the diagnostic information (second diagnostic information) to the user terminal 200.

[0077] For sub-areas, it is preferable to define them as individual trees in the case of palm or rubber trees, or as predetermined areas or sections in the case of vegetation.

[0078] The second diagnostic information is output as a second infection area map, for example, which overlays the infection level (risk level) for each sub-area onto the first infection area map 61. Figure 8 shows an example of a second infection area map generated in this embodiment and displayed on the user terminal 200.

[0079] As shown in the figure, the second infection area map 71 is an enlarged version of the area corresponding to the area display frame 62 on the first infection area map 61, and the area R of each palm tree within that area is shown in a different color according to the determined degree of Ganoderma infection.

[0080] The CPU 11 colors each palm tree region R on the RGB image with a color corresponding to the determined infection level (for example, infection level 1 is colorless, infection level 2 is green, infection level 3 is yellow, infection level 4 is orange, and infection level 5 is red), and also refers to the corresponding information database 33 to set up a link for each palm tree region R that allows access to information indicating the determined treatment.

[0081] 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 level corresponding to that region is displayed, for example, via a pop-up.

[0082] Figure 9 shows an example of how treatment information (or medical treatment information) is displayed when one of the areas R in the second infection area map 71 is selected by the user.

[0083] As shown in the figure, a pop-up 80 displaying treatment information is displayed, starting from the region R selected by the user. The 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 degree of Ganoderma infection on the palm trees and the appropriate treatment. In the example shown in the figure, for example, information suggesting spraying pesticides and pesticide efficacy enhancers on the surface of the tree trunk is displayed. Other treatments (injection into the tree trunk, soil irrigation, and microbial addition) can also be displayed by the user switching screens on the pop-up.

[0084] 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 second infection map 71, prepare the necessary agents for treatment (treatment or prevention), such as pesticides and pesticide efficacy enhancers, and then start the treatment by starting navigation using the navigation button 82.

[0085] Furthermore, when a user selects an area R on the second infection area map 71, a pop-up 80 is displayed. In addition, when a user selects an infection level on the infection level map, the area of ​​palm trees that should be treated according to that infection level is highlighted on the map, and information indicating the area, etc., is output. The selection of each infection level may be possible, for example, by selecting the box for each infection level in the legend G.

[0086] [summary] As described above, according to this embodiment, the diagnostic server 100 can efficiently diagnose the infection status of plants (palm trees) with disease-causing fungi (Ganoderma) in a wider area.

[0087] As mentioned above, since FFB is brought in from approximately 10,000 hectares of surrounding farms to a single oil mill, the introduction of this system to one oil mill makes it possible to manage and monitor the infection level of Ganoderma in farms in an area of ​​approximately 10,000 hectares. Furthermore, the more farms and oil mills that use this system, and the more data is accumulated, the more accurate the infection level diagnosis becomes.

[0088] For example, in Malaysia, palm plantations cover approximately 5.7 million hectares, and there are about 450 palm oil mills. In Indonesia, palm plantations cover approximately 12 million hectares, and there are about 1,000 palm oil mills. Therefore, by introducing this system to these mills, it will be possible to monitor and manage the infection situation of Ganoderma over an extremely wide area.

[0089] [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.

[0090] In the embodiments described above, either the oil extracted from palm fruit or the waste liquid was used as the test subject for Ganoderma infection. However, both the oil and the waste liquid may be used as the test subjects. By diagnosing the degree of infection based on the results of both tests, the accuracy of the diagnosis can be improved.

[0091] In the embodiments described above, DNA from palm oil extraction or wastewater was tested by PCR. Alternatively, the degree of infection may be diagnosed by testing the Ganoderma fungus or the stress response of the palm tree on the said oil extraction or wastewater using antigen-antibody testing (ELISA). There have been previous studies in which Ganoderma fungus or palm stress factors were detected from palm leaves, trunks, and roots using ELISA. In addition, the degree of infection may be diagnosed using mass spectrometry methods such as GC (Gas Chromatography)-MS (Mass Spectrometry) or LC (Liquid Chromatography)-MS.

[0092] In other words, since plants regulate the transcription of genes such as mRNA that encode proteins under environmental stresses such as diseases, high temperatures, and water shortages, the risk of disease can be assessed by quantifying the amount of mRNA expression, protein, and metabolites that increase or decrease due to Ganoderma infection.

[0093] In the embodiment described above, the diagnostic server 100 calculated the final evaluation value by correcting the detected value of Ganoderma by PCR using environmental factor values ​​and weather condition values. In addition to this, or instead, the diagnostic server 100 may correct the calculated evaluation value based on the difference between the current evaluation value and past evaluation values. For example, the diagnostic server 100 may correct the evaluation value to a higher value if the current evaluation value is on an upward trend compared to the past three evaluation values, or if the current evaluation value is clearly higher than the average of the past three evaluation values.

[0094] In the embodiments described above, palm palm was given as an example of a plant belonging to the Arecaceae family, and Ganoderma was given as an example of a pathogenic fungus. However, the combination of plants and pathogenic fungi is not limited to these, and the degree of infection of pathogenic fungi may be diagnosed by biochemically analyzing extracts of other plants. For example, it may be used to diagnose infections of root rot in rubber plants (Moraceae), Panama disease or new Panama disease in banana plants (Musaceae), rust in soybeans (Fabaceae), coffee rust or CBD (Coffee Berry Disease) in coffee plants (Rubiaceae), rice blast in rice plants (Poaceae), and grape rust in grape plants (Vitaceae). In this case, for rubber plants, the sap, which is the harvested product, and the waste liquid generated during the stage of removing impurities from the sap (washing) are the subjects of biochemical analysis. For other plants, the fruit, which is the harvested product (extract), is the subject of biochemical analysis.

[0095] In the embodiments described above, the plant extracts used were oil extracted from palm fruit and wastewater discharged during oil extraction. However, the extracts are not limited to those derived from fruit, but include extracts from trees, trunks, stems, roots, leaves, etc. Preferably, the extracts are derived from fruit, trees, trunks, and roots, and more preferably from commercially harvested parts. The extracts also include wastewater discharged during the extraction process and waste products after extraction. More preferably, extracts of commercially harvested oils or juices, their wastewater, and waste products are used. For example, in the case of rubber, the collected sap (latex), wastewater from the solidification process, or wastewater from the washing process would be the subjects of analysis. In the case of grapes, the fruit washing solution or squeezed juice would be the subjects of analysis.

[0096] In this system, the extracts used to diagnose the degree of infection by pathogenic fungi are not limited to those harvested for commercial purposes, but also include extracts collected for diagnostic purposes.

[0097] In situations with a high risk of pathogenic infection, and in infected conditions, the number of pathogenic bacteria on the surface and inside plants is higher than normal, so pathogenic bacteria can be detected in the fruits, trunks, roots, etc. of plants. Furthermore, in situations with a high risk of pathogenic infection, and in infected conditions, the accuracy of the correspondence between detection values ​​and the degree of infection can be improved by combining the types of pathogenic bacteria, plants, and extracts, and more preferably by combining them with environmental information.

[0098] In the embodiment described above, the diagnostic server 100 pre-stored response information corresponding to the infection level in the response information database 33, and when diagnosing the infection level, it referred to the stored information to determine the response. Alternatively, the diagnostic server 100 may, without pre-storing response information, send a request to the testing facility terminal 300 requesting response information corresponding to the infection level when diagnosing the infection level, and generate first diagnostic information based on the response information sent from the testing facility terminal 300 in response.

[0099] In the embodiment described above, the diagnostic server 100, upon diagnosing the infection level, extracted corresponding response information from the response information stored in the response information database 33 and sent it to the user terminal 200. Alternatively, the diagnostic server 100, upon diagnosing the infection level, may notify the user terminal 200 only whether or not action is required for the palm trees, according to the infection level. In this case, storing corresponding response information is not essential.

[0100] In the embodiment described above, only one diagnostic server 100 is shown, but the processing performed by the diagnostic server 100 may be distributed and executed by multiple servers. For example, there may be separate servers that acquire data on oil extraction volume (or waste liquid volume) / FFB harvest volume, and separate servers that diagnose the degree of Ganoderma infection based on the oil extraction volume (or waste liquid volume) / harvest volume data and inspection results, and generate diagnostic information.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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]

[0105] 11…CPU 18...Storage section 19… Communications Department 31…Farm Information Database 32…Inspection Information Database 33... Correspondence Information Database 61… Map of the first infection area 63... Area location information 64…Infection level information 71… Map of the second infection area 100... Diagnostic Server 200... User terminal 300... Inspection facility terminal 400... Oil extraction plant terminal R... Palm tree region

Claims

1. Area information indicating the area where the fruit or sap of the plant was harvested, and biochemical analysis information of the extract of the plant are obtained. Based on the aforementioned analysis information, the degree of infection of plant disease fungi in the area is diagnosed. The system outputs first diagnostic information, which includes infection level information indicating the diagnosed level of infection, response necessity information indicating at least whether or not a response to the infection is necessary, and the area information. control unit An information processing system equipped with the following features.

2. The control unit, Information is obtained indicating the amount of the extract and the amount of the harvested fruit or sap of the plants. The degree of infection is diagnosed based on an evaluation value obtained by multiplying the biochemical detection value of the plant by the ratio of the amount of the extract to the amount of the harvested fruit or sap of the plant. The information processing system according to claim 1.

3. The control unit, The system stores information regarding responses to the aforementioned diseases of plants, depending on the degree of infection by the aforementioned disease-causing fungi. As the first diagnostic information, the stored correspondence information corresponding to the diagnosed infection level is output. The information processing system according to claim 1 or 2.

4. The aforementioned area information indicates an area that is a farm or part of a farm where the fruits or sap of the plants were harvested. The control unit generates and outputs a first infection area map, which shows the area on the map data of the farm, as the first diagnostic information. The information processing system according to claim 1 or 2.

5. The control unit transmits to the user's terminal at the farm the recommended information, which is a response information corresponding to the infection level, that it takes an aerial image of the area, analyzes the captured image data, and diagnoses the infection level for each sub-area within the area. The information processing system according to claim 3.

6. The control unit analyzes image data captured from above the area based on the corresponding information for the infection level and outputs second diagnostic information that diagnoses the infection level for each sub-area within the area. The information processing system according to claim 3.

7. The control unit outputs a second infection area map as second diagnostic information, which shows the infection level for each sub-area superimposed on the first infection area map. The information processing system according to claim 6.

8. The aforementioned correspondence information includes treatment information indicating the treatment to be taken according to the degree of infection of the plants, The control unit outputs the treatment information for each sub-area based on the second infection area map. The information processing system according to claim 7.

9. The control unit, Information is obtained that shows environmental factor values ​​calculated from the amount of the disease-causing fungi or the stress response of the plants detected in the air or soil at the time of harvest in the harvested area. The evaluation value is corrected by the environmental factor value. The information processing system according to claim 2.

10. The control unit, Obtain weather conditions or wind speed values ​​corresponding to the weather or wind speed during a predetermined period prior to the harvest of the fruit or sap in the aforementioned area. The evaluation value is corrected by the weather condition value. The information processing system according to claim 2.

11. The control unit, By obtaining past evaluation values ​​for the same area, The aforementioned evaluation value is corrected based on the difference between the current evaluation value and the aforementioned past evaluation value. The information processing system according to claim 2.

12. The control unit acquires biochemical analysis information of the oil extracted from the fruit of the plant or waste liquid as analytical information. The information processing system according to claim 1 or 2.

13. Area information indicating the area where the fruit or sap of the plant was harvested, and biochemical analysis information of the extract of the plant are obtained. Based on the aforementioned analysis information, the degree of infection of plant disease fungi in the area is diagnosed. The system outputs diagnostic information including infection level information indicating the diagnosed level of infection, information indicating whether or not a response to the infection is necessary, and the area information. Information processing methods.

14. On the computer, A step of obtaining area information indicating the area where the fruit or sap of the plant was harvested, and biochemical analysis information of the extract of the plant. The steps include: diagnosing the degree of infection of plant disease fungi in the area based on the aforementioned analysis information; A step of outputting diagnostic information including infection level information indicating the diagnosed level of infection, information indicating whether or not a response to the infection is necessary, and the area information. A program that executes the command.

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