JP6932682B2 - Programs, information processing methods, and information processing equipment - Google Patents

Description

The present disclosure relates to programs, information processing methods, and information processing devices.

In satellite remote sensing, for example, it is possible to obtain information on electromagnetic waves reflected or radiated from an object existing in the target area of sensing. This information differs depending on the type of the object, the environmental conditions, and the like, and this difference can be used to identify and classify the state of the object.

In recent years, technologies for utilizing data such as vegetation indicators collected by such satellite remote sensing for agriculture have been developed. For example, Patent Document 1 discloses a vegetation growth analysis system that predicts plant growth based on satellite data and meteorological data. This system collects satellite data and meteorological data for multiple years or multiple cycles of vegetation growth of the target plant, creates an analytical model for vegetation growth based on the collected data, and based on that analytical model, the target plant. Predict vegetation indicators. This makes it possible to predict the yield at an early stage. In addition, a yield prediction method using satellite data by other methods, a system for evaluating crops from image data instead of satellite data, and a support management system using satellite data are known (for example). See Patent Documents 2-4).

Japanese Unexamined Patent Publication No. 2015-188333 JP-A-2017-125705 Japanese Unexamined Patent Publication No. 2010-166851 Japanese Unexamined Patent Publication No. 2017-07820

In addition to the above-mentioned vegetation index prediction, the data collected by satellite remote sensing is used for harvest prediction and yield evaluation. On the other hand, the target of evaluation was crops, and there was no mechanism for evaluating farming by farmers based on objective data.

Therefore, this disclosure has been made in view of such circumstances, and includes programs, information processing methods, and information processing devices that enable appropriate evaluation of farming by each farmer based on objective data. The purpose is to provide.

The program according to the embodiment of the present disclosure is a first acquisition step of acquiring farming information for each farmer in an information processing apparatus, and acquiring farming information regarding the type of crop produced by the farmer and the production area of the crop. And, from the artificial satellite, the first sensing data over time of the area including the production area, which includes the production information regarding the growth amount, quality, and / or the growth process of the crop, is acquired. 2 Based on the acquisition step and the production information specified by each farmer using the farming information, a score regarding the evaluation of the crop yield and / or the growth process is given according to the type of crop of each farmer. Perform scoring steps and.

According to the present disclosure, the farming of each farmer can be appropriately evaluated based on objective data.

FIG. 1 shows an example of the configuration of the evaluation system 1 according to the present disclosure. FIG. 2 shows an example of the hardware configuration of the information processing apparatus according to the present disclosure. FIG. 3 shows an example of a block diagram showing a functional configuration of the server 110 according to the first embodiment. FIG. 4 shows an example of the data structure stored in the farming information DB 331. FIG. 5 shows an example of an image of satellite data stored in the satellite data DB 332. FIG. 6 shows an example of an image of ground data stored in the ground data DB 333. FIG. 7 shows an example of the data structure stored in the score DB 333. FIG. 8 shows an example of the data structure stored in the group DB 333. FIG. 9 shows an example of a table in which the types of crops are associated with the wavelength bands to be evaluated. FIG. 10 shows an example of a sequence diagram showing the processing performed by the evaluation system 1 in the first embodiment. FIG. 11 shows an example of a flowchart of processing in the server 110 according to the first embodiment. FIG. 12 shows an example of a flowchart of processing in the server 110 in the second embodiment.

Hereinafter, embodiments according to an example of the present disclosure will be described with reference to the drawings. However, the embodiments described below are merely examples, and are not intended to exclude the application of various modifications and techniques not specified below. That is, an example of the present disclosure can be implemented in various modifications without departing from the spirit of the present disclosure. Further, in the description of the following drawings, the same or similar parts are designated by the same or similar reference numerals, and the drawings are schematic and do not necessarily match the actual dimensions and ratios. Further, the drawings may include parts having different dimensional relationships and ratios from each other.

<First Embodiment>
The program, information processing method, and information processing apparatus according to the first embodiment are the first sensing data from the satellite (hereinafter, simply "satellite data") including production information regarding the growth amount, quality, and / or growth process of the crop. (Also also referred to as) is used to evaluate the harvest outcome and / or growth process for each type of crop that the farmer grows on a given land. The growing process refers to all processes related to the growth of crops from soil preparation to harvesting.

According to the first embodiment, it is possible to objectively evaluate the harvesting result and / or the growing process of each farmer's crop. Then, by performing such an evaluation process over the entire area included in the satellite data, it is possible to compare and evaluate the harvest results of crops grown under the same or similar growing conditions or different growing conditions at a plurality of points. It will be possible. Further, if the comparative evaluation of the harvest results of the crops becomes possible, it is possible to judge the superiority or inferiority of the breeding method that has reached the harvest results from the difference in the harvest results of the same or similar crops.

The information on the superiority or inferiority of the growing method obtained in this way provides useful data for the farmer to improve the growth amount and / or quality of the crop. If there is such a mechanism that allows farmers to share information on the superiority or inferiority of the growing method, it is possible to expect an overall improvement or homogenization of the growth amount and / or quality of the crop. In general, the cost of using satellite remote sensing is high for each farmer to use for evaluating the crop yield and growth process. However, in a service that shares information on the superiority and inferiority of breeding methods among farmers, individual agriculture is directly or indirectly shared with the cost of using satellite remote sensing as a usage fee for the service. It is also possible to reduce the cost borne by the person.

<System configuration>
In this first embodiment, for example, the server 110, which is an information processing device, constructs an evaluation system 1 that evaluates the harvesting result and / or the growing process of a crop based on farming information and satellite data. FIG. 1 shows an example of the configuration of the evaluation system 1. In this evaluation system 1, for example, a server 110 that evaluates the harvest result and / or growth process of a crop based on farming information and satellite data, and first terminal 120A and second terminal 120B that transmit farming information to the server 110. And the third terminal 120C, which transmits satellite data to the server 110, are connected via the network 130. Here, the first terminal 120A and the second terminal 120B are different terminals used by different farmers, the first terminal 120A is a terminal used by the first farmer described later, and the second terminal 120B is. This is a terminal used by a second farmer, which will be described later.

Further, the evaluation system 1 may be connected to the fourth terminal 120D, which transmits other information to the server 110, via the network 130, if necessary. Here, other information includes, for example, sensing data over time from one or more ground sensors installed in the area included in the satellite data, and the soil and / or at the point where the ground sensors are installed. Examples include climate-related sensing data (hereinafter, also simply referred to as “ground data”). The fourth terminal 120D that transmits ground data to the server 110 may be a device attached to each ground sensor itself, or is a device that receives and aggregates information from each ground sensor and transmits it to the server 110. You may.

Hereinafter, a mode in which the server 110 acquires farming information, satellite data, and ground data from the first terminal 120A, the second terminal 120B, the third terminal 120C, or the fourth terminal 120D via the network 130 will be described. The means by which the server 110 acquires farming information, satellite data, and ground data is not limited to the method of acquiring from the first terminal 120A, the second terminal 120B, the third terminal 120C, or the fourth terminal 120D, and the input interface is provided. Information may be acquired by inputting through or reading a storage medium in which such information is recorded.

<Hardware configuration of the first embodiment>
FIG. 2 shows an example of the hardware configuration of the information processing apparatus 200 (server 110 and terminals 120A to C) included in the evaluation system 1. The information processing device 200 includes a processor 201, a memory 202, a storage 203, an input / output interface (input / output I / F) 204, and a communication interface (communication I / F) 205. Each component of the HW of the information processing apparatus 200 is connected to each other via a bus B, for example, without limitation.

The information processing device 200 realizes the functions and / or methods described in the present disclosure in cooperation with the processor 201, the memory 202, the storage 203, the input / output I / F 204, and the communication I / F 205. do.

The processor 201 executes a function and / or a method realized by a code or an instruction contained in a program stored in the storage 203. The processor 201 is not limited to, for example, a central processing unit (CPU), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), a microprocessor (microprocessor), a processor core (processor core), and a multiprocessor. , ASIC (Application-Specific Integrated Circuit), FPGA (Field Programmable Gate Array) and the like.

The memory 202 temporarily stores the program loaded from the storage 203 and provides a work area to the processor 201. Various data generated while the processor 201 is executing the program are also temporarily stored in the memory 202. The memory 202 includes, but is not limited to, a RAM (Random Access Memory), a ROM (Read Only Memory), and the like.

The storage 203 stores the program. The storage 203 includes, but is not limited to, an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, and the like.

The communication I / F 205 transmits / receives various data via the network 130. The communication may be executed by wire or wirelessly, and any communication protocol may be used as long as mutual communication can be executed. The communication I / F 205 has a function of executing communication with another information processing device via the network 130. The communication I / F 205 transmits various data to another information processing device according to an instruction from the processor 201. Further, the communication I / F 205 receives various data transmitted from other information processing devices and transmits the various data to the processor 201.

The input / output I / F 204 includes an input device for inputting various operations to the information processing device 200 and an output device for outputting the processing result processed by the information processing device 200. The input / output I / F 204 may be integrated with the input device and the output device, or may be separated into the input device and the output device.

The program of the present disclosure may be provided stored in a computer-readable storage medium. The storage medium can store the program in a "non-temporary tangible medium". Programs include, but are not limited to, software programs and computer programs as examples.

At least a part of the processing in the information processing apparatus 200 may be realized by cloud computing composed of one or more computers. At least a part of the processing in the information processing apparatus 200 may be performed by another information processing apparatus. In this case, at least a part of the processing of each functional unit realized by the processor 201 may be performed by another information processing device.

<Functional configuration of the first embodiment>
The functional configuration of the server 110 will be described with reference to FIG. FIG. 3 shows an example of a block diagram showing a functional configuration of the server 110 according to the first embodiment. The server 110 is an example of an information processing device that performs evaluation processing in the evaluation system 1, and includes input / output I / F 311, communication I / F 312, a control unit 320, and a storage unit 330. The input / output I / O 311 corresponds to the input / output I / F 204 of FIG. 2, and the communication I / F 312 corresponds to the communication I / F 205 of FIG. The storage unit 330 is realized by using the memory 202 and / or the storage 203.

The functional unit disclosed in FIG. 3 is realized by the cooperation of the processor 201, the memory 202, the storage 203, the input / output I / F 204, and the communication I / F 205 included in the information processing device 200. For example, the processor 201 of the information processing device 200 shown in FIG. 2 expands various programs (control programs, arithmetic programs, etc.) stored in the storage 203 into the memory 202 (for example, RAM). Then, the processor 201 interprets and executes various programs expanded in the memory 202 to control each hardware component, thereby realizing the functional configuration described below.

The control unit 320 includes a reception unit 321, a scoring unit 322, a grouping unit 323, a licensing unit 324, and a matching unit 325. The receiving unit 321 and the scoring unit 322, the grouping unit 323, the licensing unit 324, and the matching unit 325 are realized by the processor 201 reading and executing the program 334 stored in the storage unit 330. NS. Further, the storage unit 330 stores the farming information DB 331, the satellite data DB 332, the ground data DB 333, the score DB 334, the group DB 335, and the program 336. The functional configuration realized by the server 110 may be omitted, replaced, or added as appropriate according to the embodiment or the embodiment. Hereinafter, the storage unit 330 will be described, and then the control unit 320 will be described.

The farming information DB 331 stores farming information related to the cultivation of crops in each production area, and the farming information for each farmer received by the receiving unit 321 from the first terminal 120A, the second terminal 120B, etc. via the network 130. This is an example of a database that stores. As farming information, information on identifying the farmer, the type of crop produced by the farmer, the longitude / latitude and area of the production area of the crop, information on watering and harvesting time of the crop, and information on the crop were used. Information on agricultural production materials such as fertilizers, pesticides, and agricultural machinery, information on usage such as date and time of use, range of use, purpose of use, amount of use, and number of times of use of the agricultural production materials, management information on sales volume and unit price of crops, etc. Can be mentioned.

FIG. 4 schematically shows a part of the data structure stored in the farming information DB 331. In the example of FIG. 4, farming information that can be acquired from the first terminal 120A is stored for each production area. The farming information shown in FIG. 4 includes, for example, information for identifying a farmer (farmer ID), the type of crop produced by the farmer, the longitude / latitude and area of the production area of the crop, and the watering and harvesting time of the crop. Information on agricultural production materials such as fertilizers, pesticides, and agricultural machinery used for crops, and information on usage such as the date and time of use, amount of use, and number of times of use of the agricultural production materials.

The satellite data DB 332 stores the sensing data from the satellite regarding the area including the production area over time, and is an example of a database that stores the satellite data received from the third terminal 120C by the receiving unit 321 via the network 130. Is. The satellite data includes satellite data including production information on crop growth, quality and / or growth process, as well as first environmental information on the environment of the production area such as soil and / or climate that can be read from the spectral data. It may be. The first environmental information is not particularly limited as long as it can be read from satellite data. For example, soil data such as soil composition, underground temperature, water content, acidity (pH), temperature, precipitation, sunshine time, etc. Meteorological data can be mentioned.

Examples of satellite data in the present disclosure include spectral data of electromagnetic waves reflected or radiated at each point. The spectral data includes production information on crop growth, quality and / or growth process, and environmental information on soil and / or climate in the range of use. For example, when the spectral data is divided into several wavelength bands, the region of 0.52 to 0.6 μm shows plant activity, and the region of 0.63 to 0.69 μm is for determining the type of plant. It is said that the region showing chlorophyll absorption, the region of 1.55 to 1.75 μm shows the water content of the plant, and the region of 10.4 to 12.5 μm shows the surface temperature.

In addition to this, the satellite data may include information indicated by an index calculated by four arithmetic operations of reflectance in a plurality of wavelength bands. For example, the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), which are known as biological indicators, show that chlorophyll in plants strongly uses wavelengths in the red region of sunlight for photosynthesis. It is an index based on absorption and reflection of wavelengths in the near-infrared region, and is calculated from the reflectances in the near-infrared region, red region, and blue region.

Furthermore, by creating in advance a correlation equation that holds between a specific wavelength band and the quality (for example, protein amount) and growth amount of a specific crop, the scoring unit 322 of the server 110 can use satellite data. It is also possible to arbitrarily estimate the quality and growth of a specific crop. In this way, the information estimated from the satellite data by the correlation formula is also one of the information included in the satellite data.

The satellite data stored in the satellite data DB 332 is, for example, two-dimensional area data having a predetermined resolution or three-dimensional area data in consideration of the height difference, and may have electromagnetic spectrum data for each pixel or point. .. FIG. 5 schematically shows an image of satellite data stored in the satellite data DB 332. In the example of FIG. 5, the satellite data (t) at the time point t and the spectral data (l, t) at the point l at the time point t are shown. For example, based on the value of a specific wavelength region in the spectral data (l, t) or an index calculated based on the specific wavelength region, the scoring unit 322 of the server 110 determines the crop in the production area (l) at the time point t. It is possible to specify the production information regarding the growth amount, quality and / or the growth process of the plant, and the first environmental information regarding the environment of the production area such as the soil and / or the climate.

The ground data DB 333 stores the sensing data from the ground sensor regarding the area including the production area over time, and is a database that stores the ground data received from the third terminal 120C by the receiving unit 321 via the network 130. This is an example. Ground data includes a second environmental information on soil and / or climate in the production area. The second environmental information is not particularly limited as long as it is data that can be acquired by a sensor installed on the ground. For example, soil data such as soil composition, underground temperature, water content, and acidity (pH), air temperature, and precipitation. , Meteorological data such as sunshine hours. The ground data is stored in association with the position information on which the ground sensor is installed.

As shown in FIG. 5, the satellite data can be obtained as area data having a constant resolution. On the other hand, the ground data is the data at the point where the ground sensor is installed, and does not have the information at the point where the ground sensor is not installed. FIG. 6 schematically shows an image of the ground data stored in the ground data DB 333. In the example of FIG. 6, ground data (t) regarding the pH of the soil at time point t and ground data (t') obtained by supplementing the ground data (t) with satellite data (t) are shown.

The ground data at the point (l3) where the ground data cannot be obtained because the ground sensor is not installed, such as the ground data (t), may be complemented by estimation. As an example, when the acidity of the points (l1, l2) at the two points is obtained from the ground data, the acidity of the point (l3) at the third point is the acidity at the points (l1, l2). Examples thereof include a method of estimating and complementing by an estimation formula (pH (x)) based on degrees (l1, l2). However, the complement method is not limited to this.

Further, instead of this, an estimation formula (for example, pH (x)') obtained by calculating the correlation formula between the acidity of the points (l1, l2) and the satellite data and correcting from the value obtained from the satellite data is obtained. You may. In this way, based on the correlation between satellite data and ground data, by complementing or correcting one with the other, it is possible to estimate the third environmental information regarding soil and / or climate in the production area where the ground sensor is not installed. Is. As a result, the accuracy of the environmental information of the production area can be further improved. The third environmental information can be stored in the ground data DB 333.

The score DB 334 is an example of a database that stores the score related to the evaluation of the harvest result and / or the growth process of the crop given to the farmer, and stores the score calculated by the scoring unit 322 of the server 110. As the score to be memorized, for example, a score regarding the amount of growth of the crop, a score regarding the quality of the crop, a score regarding the evaluation of the growth process from soil preparation to harvest suitable for each crop, or a score obtained by comprehensively evaluating these scores. Can be mentioned. The score to be evaluated is not limited to this, and a configuration may be used in which all processes from soil preparation to harvest are scored as evaluation targets.

FIG. 7 schematically shows a part of the data structure stored in the score DB 334. In the example of FIG. 7, the score regarding the evaluation of the harvesting result and / or the growing process of the crop given to the farmer is stored for each crop. These scores are numerical values such as the reflectance of a specific wavelength band in the spectrum data of satellites related to the growth of crops and soil preparation, and the numerical index obtained by the four-rule calculation of the reflectance of multiple wavelength bands. Can be represented by.

Further, as another method, for example, a method in which a threshold value is set in advance, the growth amount is evaluated as "High" if the threshold value is equal to or higher than the threshold value, and the growth amount is evaluated as "Low" if the threshold value is less than the threshold value. Be done. The method for determining the threshold value in the method is not particularly limited, and for example, it can be determined by specifying a set of information on the amount of growth in a specific crop A and obtaining a statistical tendency (clustering) from this set. The classification is not limited to two classifications, "High" and "Low", and can be divided into three or more classifications.

The group DB 335 stores the farmers classified into groups based on the information on the crops to be produced and / or the soil and / or climate of the production area, and the group group 323 of the server 110 classifies the groups. This is an example of a database to be stored. Based on this, it is possible to identify a farmer who grows a specific crop A and has a similar growing environment. As a result, it is possible to more appropriately share information on the growing method among farmers who have similar crop growing environments. Specifically, farmers with similar crop growing environments can be matched, and a method of obtaining better harvest results in similar growing environments can be shared.

FIG. 8 schematically shows a part of the data structure stored in the group DB 335. In the example of FIG. 8, the group ID specified by the type of crop, the type of soil, and the type of climate is determined, and the farmers corresponding to this group ID are associated with each other. The types of soil in the production area for characterizing the group include, for example, satellite data DB332 and areas such as areas with high and low surface temperatures, areas with high and low pH, and areas with high and low underground water content. It can be determined based on the soil information stored by the ground data DB333. In addition, the types of climate of the production areas for characterizing the group include, for example, satellite data DB332 and ground, such as areas with high or low sunshine, areas with high or low rainfall, and areas with high or low temperature. It can be determined based on the climate information stored in the data DB 333. The type of soil and climate of these production areas can be determined based on the first to third environmental information stored in the satellite data DB 322 and the ground data DB 333.

Next, returning to FIG. 3, the control unit 320 will be described.

The receiving unit 321 performs a process of receiving farming information, satellite data, or ground data from the first terminal 120A, the second terminal 120B, the third terminal 120C, or the fourth terminal 120D via the input / output I / F 204. Has a function to perform.

Regarding the input of farming information, for example, each farmer can perform the input to the first terminal 120A and the second terminal 120B. At this time, from the viewpoint of reducing the input burden on the farmer, instead of inputting the farming information by text, the image data of the production area may be input. The image data input here is a copy of the production area, and preferably includes the position information associated with the image data and the shooting date and time associated with the image data. The image data acquired by the first terminal 120A and the second terminal 120B is analyzed by the functional unit of the first terminal 120A and the second terminal 120B using augmented reality (AR) technology, and the area of the production area obtained by the analysis. After receiving such information as the receiving unit 321 of the server 110, the location of the production area can be specified and the area can be obtained. The image data received from the first terminal 120A and the second terminal 120B may be analyzed by the receiving unit 321 of the server 110 using augmented reality (AR) technology to obtain the area of the production area. In addition, the server 110 can specify the position of the production area from the position information attached to the image data. The image data may be a panoramic photograph, although it depends on the range of use for shooting.

Furthermore, the image data is photographed according to the growing stage of the crop, such as when sowing seeds, when watering, when fertilizers and pesticides are used, and when harvesting, and the first terminal 120A together with information on the growing stage corresponding to the photographed image data. And may be input to the second terminal 120B. The server 110 is based on the position information attached to the image data input to the first terminal 120A and the second terminal 120B, the shooting date and time, and the information on the growing stage, and the farming information on the growing of crops such as the usage method of the agricultural production materials. Can be stored in the farming information DB 331. As a result, it is possible to reduce the burden on the farmer regarding the input of the production area, the time of use and the number of times of use of the agricultural production material.

Further, the server 110 may receive information such as pests in the range of use of agricultural production materials from the first terminal 120A and the second terminal 120B as image data. Pests can also be automatically determined from the image data.

The satellite data can be received directly from the third terminal 120C, which receives the satellite data from the artificial satellite, via the network or indirectly via another terminal. The satellite data may be received regularly or irregularly. As an irregular example, for example, when farming information is input, satellite data may be received based on the input farming information.

The ground data is received via the fourth terminal 120D. Sensing data on soil and / or climate is accumulated in the fourth terminal 120D from a sensor installed on the ground. Further, the ground data may be associated with the measurement position information and the measurement time information.

Ground data improves the accuracy of acquired environmental information, such as the purpose of supplementing data that cannot be acquired by satellite data or ground data alone, and the purpose of further improving the accuracy of satellite data, and more appropriately groups farmers. It can be used for the purpose. As an example for the purpose of supplementing data that cannot be acquired only by satellite data or ground data, ground data at a point where ground data cannot be obtained due to the absence of a ground sensor, such as the above-mentioned example of soil acidity, is used. In some cases, it is complemented by estimation. In addition, as an example for the purpose of improving the accuracy of satellite data, for example, when the same physical quantity can be measured by satellite data and ground data such as surface temperature and soil water content, the measured value of ground data is measured. Based on the above, there is a case where the value calculated from the wavelength band of the satellite data is calibrated.

Based on the production information specified from the satellite data DB 332 using the farming information of each farmer, the scoring unit 322 tells each farmer the crop harvest result and / Alternatively, a process of assigning a score regarding the evaluation of the growth process is performed. More specifically, values related to growth amount, quality, soil condition, etc. are extracted from the satellite data received by the receiving unit 321, and based on the values, scores related to the growth amount, quality, and / or growth process of the crop are obtained. Can be attached.

Here, as the values related to the growth amount, quality, soil condition, etc. extracted from the satellite data, for example, a numerical index obtained by four-rule calculation of the reflectance of a specific wavelength band and the reflectance of a plurality of wavelength bands, etc. Can be mentioned. The wavelength band used when extracting values related to the amount of growth, quality, soil condition, etc. can be selected as appropriate according to the type of crop. For example, since green crops and red crops have different absorption and reflection wavelength bands of electromagnetic waves (light), a more suitable wavelength band is appropriately selected and used when extracting values related to growth amount and quality. be able to. Further, regarding the state of soil and the like, a wavelength band suitable for evaluating them can be appropriately selected and used according to the amount of water and the amount of nutrients. In addition, the selected wavelength band may be adjusted and used by assigning a parameter value for each type of crop.

Based on the information on the type of crop included in the farming information, it is possible to more appropriately evaluate the state such as the growth amount and quality of the crop by adjusting the wavelength band etc. to be focused on for each production area of the crop. can. As a result, the accuracy of the evaluation of the harvest result and / or the growth process of the crop is further improved, and the superiority or inferiority of the growing method that has reached the harvest result can be judged more appropriately. The scoring unit 322 may use a table in which the type of crop and the wavelength band to be evaluated are associated with each other in order to more appropriately evaluate the state such as the growth amount and quality of the crop.

FIG. 9 shows an example of a table in which the types of crops are associated with the wavelength bands to be evaluated. In the example of FIG. 9, for each type of crop, a wavelength band to be considered in the evaluation of growth amount or quality and a coefficient to be integrated into the reflectance of the wavelength band are stored. For example, according to this table, the index of crop quality can be calculated (extracted) by the following formula.
Index of crop growth = reflectance of wavelength band 1 x coefficient + reflectance of wavelength band 2 x coefficient

In addition, the index of crop growth can be calculated (extracted) by the following formula. In addition, the farming associated with the crop can be evaluated in the same manner.
Index of crop growth = reflectance of wavelength band 1 x coefficient + reflectance of wavelength band 2 x coefficient

Further, the index for evaluating the growth process can be produced in the same manner as described above. As an example, the evaluation of the soil preparation process in the growth process will be described. For example, for each type of crop, data such as water content, nutrient content or pH of soil suitable for growing is prepared (hereinafter, also referred to as "ideal soil data"), and this ideal soil data and the wavelength band indicating it or Prepare a table associated with the spectrum. Then, by comparing the satellite data of the actual crop production area with the wavelength band or spectrum corresponding to the ideal soil data, the ideal soil preparation was achieved for the soil preparation of the actual crop production area. It is possible to evaluate whether or not the soil condition is different from the ideal soil condition. In addition, not limited to this, the evaluation of growth processes other than soil can be evaluated in the same manner for those that can be observed by satellite data. Furthermore, this evaluation of the growth process is not limited to soil preparation, but can be performed in all processes from soil preparation to harvesting.

Further, as a process of assigning a score regarding the evaluation of the growth amount, quality, and growth process of the crop based on the value extracted as described above, the extracted value may be used as it is as a score, or as described above. In addition, the extracted values may be classified by clustering such as "High" or "Low" and used as a score. The scoring unit 322 may calculate a score that comprehensively evaluates the growth amount and quality of the crop based on the score regarding the growth amount of the crop, the score regarding the quality of the crop, and the score regarding the evaluation of the growth process. The score that comprehensively evaluates the growth amount and quality of the crop can be calculated as, for example, the product or the sum of the score related to the growth amount of the crop, the score related to the quality of the crop, and the score related to the evaluation of the growing process, but the score is limited to this. Not done.

The grouping unit 323 groups the farmers into a predetermined group based on the crops produced by the farmers acquired from the farming information DB 331 and / or the soil and / or climate of the satellite data DB 332 or the ground data DB 333 production area. Perform the classification process. As an example of such processing, a plurality of groups that specify the type of crop, the type of soil in the production area, and the type of climate in the production area are created in advance, and the type of crop stored in the farming information DB 331. Then, a method of allocating the farmers to the corresponding group based on the first to third environmental information stored in the satellite data DB 332 and the ground data DB 333 can be mentioned.

Here, the type of soil and climate of the production area for characterizing the group can be determined based on, for example, the soil information and climate information stored by the satellite data DB 332 and the ground data DB 333. More specifically, it can be determined by clustering the first to third environmental information stored in the satellite data DB 322 and the ground data DB 333 into several types.

The licensing unit 324 processes the farmer to issue a license regarding the use of farming information of other farmers. The conditions for issuing a license are, for example, a first farmer who wants to know farming information for achieving a specific harvest result (hereinafter, also simply referred to as "first farmer") and a farmer who has achieved a specific harvest result. An agreement has been formed with the second farmer who has the information (hereinafter, also simply referred to as "second farmer") to refer the farming information of the second farmer to the first farmer. There are no particular restrictions.

As such consensus building, for example, the licensing department 324 changes from the first farmer who wants to know the farming information for achieving a specific harvest result to the second farmer who has the farming information for achieving a specific harvest result. In some cases, an application for reference to farming information is accepted, the second farmer is notified that the application has been made, and the second farmer receives a notification that he / she agrees to disclose his / her farming information. Be done. Further, as will be described later, an agreement has been reached that the matching unit 325 matches the first farmer and the second farmer, and the first farmer is referred to the farming information of the second farmer between the two. It is also mentioned when it is done.

The license is granted as a right for the first farmer to refer to the farming information of the second farmer. The farming information stored in the farming information DB 331 includes various information such as details of how to use agricultural production materials, watering of crops, and information on harvest time. The licensing unit 324 has a license that allows all farming information to be referred to, a license that allows only some farming information to be referred to, and the like, depending on the amount of farming information to be referred to.
It is also possible to issue multiple types of licenses. In this case, the scope of farming information disclosed by the license can be agreed upon at the time of consensus building.

The method of disclosing the farming information to the licensed first farmer is not particularly limited, and examples thereof include a method of allowing the first farmer who has accessed the server 110 to view the information. In addition, the licensing unit 324 may accept the consideration for the above license from the first farmer and perform a settlement process in which all or part of the consideration is paid to the second farmer. As a result, the second farmer can obtain a new source of income other than farming by providing his / her own farming information.

The matching unit 325 performs a process of matching farmers classified into the same group by the grouping unit based on the score. For example, among the farmers classified into the same group, the first farmer who has a low evaluation score of the crop yield and / or the growth process of the previous period is given a harvest result and / or the growth process of the above-mentioned crop. Match the second farmer with the highest rating score. As a result, the farming information of the second farmer can be obtained between the first farmer who wants to know the farming information for achieving a specific harvest result and the second farmer who has the farming information for achieving a specific harvest result. When an agreement is reached to refer to the first farmer, the licensing unit 324 issues a license to the first farmer.

By performing such a matching process, it is possible to share information on the superiority or inferiority of the growing method among farmers who grow the same crop in a similar environment. As a result, it is possible to expect overall improvement and homogenization of crop growth and / or quality while suppressing the costs borne by individual farmers.

<Operation processing of the first embodiment>
The processing of the program according to the first embodiment will be described with reference to FIGS. 10 to 11. The processing procedure described below is only an example, and each processing may be changed as much as possible within the scope of the technical idea of the present disclosure, and steps can be omitted, replaced, and added as appropriate. be.

First, the processing sequence of the evaluation system 1 will be described. FIG. 10 shows an example of a sequence diagram showing the processing performed by the evaluation system 1 in the first embodiment. In the first acquisition step S1001, the first terminal 120A and the second terminal 120B each transmit farming information, the server 110 acquires these information, and in the second acquisition step S1002, the third terminal 120C is satellite data. Is transmitted, and the server 110 acquires this. In addition to these, in the third acquisition step S1003, the fourth terminal 120D is, as other information, sensing data over time from one or more ground sensors installed in the area included in the satellite data. The server 110 may acquire the sensing data (ground data) regarding the soil and / or the climate at the point where the ground sensor is installed. The acquisition order of each of these data is not particularly limited.

After executing the scoring step and the grouping step described later (S1004 to S1007), the server 110 executes the matching step to match the first farmer and the second farmer (S1008). The server 110 sends a notification to the first terminal 120A of the first farmer to recommend the second farmer together with the score (S1009), and the first farmer refers to the score of the second farmer. If it is desired to acquire farming information, a license purchase application is submitted to the server 110 (S1010). The server 110 that has received the license purchase application from the first terminal 120A notifies the second terminal 120B of the second farmer to that effect (S1011). The second farmer who approves the license purchase of the first farmer sends a message to that effect to the server 110 (S1012), and the server 110 which receives the approval issues a license to the first farmer (S1013). ..

In the above processing sequence, the license fee may be paid from the first business operator to the second business operator before and after the issuance of the license. Further, by registering the information that the second farmer approves all the license purchase applications in the server 110, the approval step (S1011 to S1012) on the second farmer side is omitted. May be good. Since steps S1004 to S10008 performed by the server 110 correspond to steps S1104-1108 described later, the description thereof is omitted here.

Next, the processing of the server 110 will be described. FIG. 11 shows an example of a flowchart of processing in the server 110 according to the first embodiment.

(Processing to acquire various data)
First, the receiving unit 321 of the server 110 obtains the first acquisition step S1101 for each farmer to acquire farming information regarding the type of crop produced by the farmer and the production area of the crop, and the growth amount, quality, and quality of the crop. / Or the second acquisition step S1102, which acquires satellite data including production information regarding the growing process, is executed. In addition to these, the receiving unit 321 of the server 110 may execute the third acquisition step S1103 to acquire the ground data including the second environmental information regarding the soil and / or the climate in the production area.

In the first to third acquisition steps S1101 to S1103, the server 110 that has acquired the farming information, satellite data, and ground data may perform analysis processing of the acquired data, if necessary.

For example, the farming information acquired in the first acquisition step S1101 includes the type of crop produced by the farmer and the farming information regarding the production area of the crop, but as described above, the image data of the production area is acquired instead. You can also do it. When the image data is acquired as a part of the farming information in the first acquisition step S1101, the receiving unit 321 of the server 110 analyzes the image data by augmented reality (AR) technology or the like and uses the area. And the process of specifying the position of the range of use and the date and time of use from the position information and the shooting date and time attached to the image data.

Further, when the ground data is acquired in the third acquisition step S1103, the receiving unit 321 of the server 110 may perform the calibration process between the satellite data and the ground data in the calibration step S1104. Such calibration processes include, for example, a process of calibrating a physical quantity contained in ground data based on a physical quantity contained in satellite data, and a process of calibrating a physical quantity contained in satellite data based on a physical quantity contained in ground data. Can be mentioned. The calibration process is a process of using one of the two values and correcting the other value to a more accurate value. Further, the physical quantity means a quantity that can be quantified as a numerical value in physics and a quantity calculated from the calculation thereof.

Further, in the complement step S1105, the receiving unit 321 of the server 110 may perform complement processing between the satellite data and the ground data. Such supplementary processing includes, for example, a process of supplementing a physical quantity partially lacking in ground data based on the physical quantity contained in the ground data and satellite data, and a process of complementing a physical quantity partially lacking in satellite data based on ground data and satellite. A process of complementing based on the physical quantity contained in the data can be mentioned. The complementary process is a process of estimating a value that is insufficient in one data based on the data and the other data.

(Processing that expresses the evaluation of crop harvest results and / or growth process as a score)
Next, in the scoring step S1106, the scoring unit 322 of the server 110 gives the farmer a score regarding the evaluation of the crop harvesting result and / or the growing process for each type of crop based on the production information included in the satellite data. Perform the process of attaching. In the scoring process, as described above, as an example, values related to growth amount and quality can be extracted from satellite data, and a score can be calculated based on the values. Here, the extraction of the value from the satellite data can be performed using, for example, a table in which the type of crop and the wavelength band to be evaluated are associated with each other. Specifically, according to the table, the reflectance value of the wavelength band required for the evaluation of the target crop is extracted from the satellite data, and the calculated value is used for calculation to obtain the growth amount and quality. You can extract the value for.

Then, based on the extracted growth amount and quality, and the values related to the growth process such as soil, a score regarding the evaluation of the growth amount and quality of the crop and / or the growth process is given.

(Processing to divide farmers into groups)
In the grouping step S1107, the grouping unit 324 of the server 110 classifies the farmers into groups based on the information on the type of crop and / or the soil and / or climate of the production area. For example, a group characterized by the type of crop, the type of soil in the production area, and the type of climate in the production area is created in advance, and the farming information of the farmer and the first to third environmental information in the farmer's production area are created. Farmers can be divided into groups based on. Further, in creating the group in advance, a process of clustering the first to third environmental information stored in the satellite data DB 322 and the ground data DB 333 into several types may be performed.

(Processing to match farmers)
In the matching step S1108, the matching unit 325 of the server 110 performs a process of matching farmers whose conditions are matched based on the score. The matching mode is not particularly limited, but for example, a farmer with a high score may be matched with a farmer with a low score, or a farmer with a low score may be matched with a farmer with a high score. Conceivable. This is expected to share information on excellent growing methods among farmers and improve overall crop growth and / or quality.

Further, as another aspect, for example, matching of farmers with high scores or matching of farmers with low scores can be considered. This allows farmers with high scores to further improve crop growth and / or quality, and farmers with low scores to refer to how farmers with similar low scores grow. It is expected that students will learn unrecommended training methods.

The above-mentioned matching may match farmers belonging to the same group, or may match farmers belonging to different groups. When matching farmers belonging to the same group, farmers who grow the same crop in a similar environment are matched. Therefore, by matching, the first farmer with a poor score can obtain direct and effective information for improving the growth amount and / or the quality from the second farmer with a good score. On the other hand, when matching farmers belonging to different groups, farmers who grow crops in different crops or in different environments are matched. In this case, for example, for the first farmer who is thinking of growing a new crop or starting to grow a crop in a new environment, the second farmer who has already achieved excellent harvest results should start growing. It is possible to obtain basic information.

(Processing to grant a license to farmers)
Finally, in the licensing step S1109, the licensing unit 326 of the server 110 performs a process of issuing a license for using the farming information of another farmer to the farmer. The license may be issued to matched farmers or unmatched farmers. Here, when issuing a license to an unmatched farmer, for example, the score of another farmer is made visible to the farmer, and the farmer who browses the score of the other farmer. Therefore, there are cases where an application for obtaining a license for farming information of other farmers is accepted. That is, there is a case where the farmer himself selects another farmer and receives the license without going through the matching process.

<Second Embodiment>
Subsequently, the second embodiment will be described. The program, the information processing method, and the information processing apparatus according to the second embodiment are embodiments in which the score given by the scoring step can be used as credit information that can be used for financing of a financial institution or the like.

In the following description, the same functional configurations as those in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted. Further, the description of the same actions and effects as those of the first embodiment will be omitted. Hereinafter, the differences from the first embodiment will be mainly described.

In this second embodiment, for example, the server 110 has a credit information creation unit as one of the functional units, and the credit information creation unit creates credit information of a farmer having the score based on the score. Perform the creation step. Here, the credit information preferably includes the scores for the past several years. FIG. 12 shows an example of a flowchart of processing in the server 110 in the second embodiment.

According to such an aspect, it is possible to create credit information commensurate with the farming ability of the farmer. As a result, for example, on the farmer side, it can be expected that it will be easier for farmers to borrow funds for new agricultural production materials and reserves for expanding production areas from financial institutions. In addition, on the financial institution side, it is possible to objectively evaluate the farming ability of farmers, which is difficult to evaluate, and even when considering the risk that income stability will be impaired by weather, etc., it is constant for farmers. It becomes possible to put the credit of.

Credit information preferably has a score for the past few years. Further, the credit information is preferably information in which the score for each period is associated with the environmental information for that period. This makes it possible to evaluate the harvest performance of the farmer in a poor harvesting environment. In other words, even in a poor harvest environment, if the farmer's harvest results and / or growth process can be evaluated as relatively high, the farmer is at risk of losing income stability due to weather and other factors. It can be evaluated as having a reduceable farming ability.

As described above, the present invention is not limited to the above-described embodiments and examples, and various modifications can be made without departing from the gist thereof. That is, the above embodiment is merely an example in all respects and is not interpreted in a limited manner.

For example, when the satellite data received by the server 110 is purchased from an external organization, the server 110 may receive the satellite data by linking with the API of the external organization system instead of transmitting the satellite data by the third terminal 120C. , The server 110 may directly or indirectly acquire the satellite data downloaded from the external engine system.

In addition, based on satellite data, the process of acquiring production information on the growth amount, quality, and / or growth process of crops, and information on the environment of the production area such as soil and / or climate can be performed by, for example, API cooperation or download. The satellite data acquired and stored in the storage 203 of the server 110 may be performed by a program expanded in the memory 202 of the server 110. Instead of this, for example, a program deployed in the memory 202 of the server 110 performs the above processing on the satellite data stored in the storage of the external engine system on the cloud by linking with API, and if necessary. The obtained production information or the like may be used for information processing by the server 110.

Further, each processing step may be changed as much as possible within the scope of the technical idea of the present disclosure. For example, the order of the scoring step S1106 and the grouping step S1107 is not particularly limited, and either of them may be performed first. good.

1 ... Evaluation system, 110 ... Server, 120A ... 1st terminal, 120B ... 2nd terminal, 120C ... 3rd terminal, 120D ... 4th terminal, 130 ... Network, 200 ... Information processing device, 201 ... Processor, 202 ... Memory , 203 ... Storage, 204 ... Input / output interface (input / output I / F), 205 ... Communication interface (communication I / F), 311 ... Input / output I / F, 312 ... Communication I / F, 320 ... Control unit, 321 ... Receiving unit, 322 ... Scoring unit, 323 ... Grouping unit, 324 ... Licensing unit, 325 ... Matching unit, 330 ... Storage unit, 331 ... Farming information DB, 332 ... Satellite data information DB, 333 ... Ground data DB, 334 ... Score DB, 335 ... Group DB, 336 ... Program

Claims (9)

For information processing equipment
Agricultural information for each farmer, the first acquisition step of acquiring farming information regarding the type of crop produced by the farmer and the production area of the crop, and
Second acquisition from the artificial satellite, which is the first sensing data over time in the area including the production area and includes the first sensing data including the production information regarding the growth amount, quality, and / or growth process of the crop. Steps and
With the third acquisition step of acquiring the second sensing data over time, including the second environmental information regarding the soil and / or the climate in the production area, from the ground sensor installed in the production area. ,
Based on the correlation between the first sensing data and the second sensing data, a complementary step that complements the third environmental information regarding soil and / or climate in the production area where the ground sensor is not installed, and
Based on the production information specified by each farmer using the farming information, a scoring step of scoring the evaluation of the crop yield and / or the growth process according to the type of crop of each farmer, and a scoring step.
Performing a grouping step of classifying the farmer into groups based on information about the crops produced by the farmer and / or the soil and / or climate of the production area.
A program that classifies the farmers into groups using the third environmental information as information about the soil and / or climate of the production area in the grouping step. The first sensing data includes first environmental information about soil and / or climate in the production area.
The program of claim 1, wherein in the grouping step, the first environmental information is used as information about the soil and / or climate of the production area to classify the farmers into groups. The program according to claim 1 or 2 , wherein in the grouping step, the farmers are classified into groups by using the second environmental information as information on the soil and / or climate of the production area. The program according to any one of claims 1 to 3 , wherein the farmers classified into the same group are subjected to a matching step of matching based on the score. The program according to any one of claims 1 to 4 , which causes the farmer to perform a licensing step of issuing a license for using farming information of another farmer. The program of claim 1, wherein the step of creating credit information for the farmer having the score is performed based on the score. In the first acquisition step
The program according to any one of claims 1 to 6 , wherein a part of the farming information is created based on the image data of the production area and the position information accompanying the image data. Information processing device
Agricultural information for each farmer, the first acquisition step of acquiring farming information regarding the type of crop produced by the farmer and the production area of the crop, and
Second acquisition from the artificial satellite, which is the first sensing data over time in the area including the production area and includes the first sensing data including the production information regarding the growth amount, quality, and / or growth process of the crop. Steps and
With the third acquisition step of acquiring the second sensing data over time, including the second environmental information regarding the soil and / or the climate in the production area, from the ground sensor installed in the production area. ,
Based on the correlation between the first sensing data and the second sensing data, a complementary step that complements the third environmental information regarding soil and / or climate in the production area where the ground sensor is not installed, and
Based on the production information specified by each farmer using the farming information, a scoring step of scoring the evaluation of the crop yield and / or the growth process according to the type of crop of each farmer, and a scoring step.
Performing a grouping step of classifying the farmer into groups based on information about the crops produced by the farmer and / or the soil and / or climate of the production area.
An information processing method for classifying the farmers into groups using the third environmental information as information on the soil and / or climate of the production area in the grouping step. Farming information on the type of crop produced by the farmer and the production area of the crop, and the first sensing data over time of the area including the production area from the artificial satellite, the growth amount, quality, and the like of the crop. From the first sensing data including production information on the and / or growth process and the ground sensor installed in the production area, the second sensing data over time, which is the second sensing data on the soil and / or climate in the production area. Regarding the soil and / or climate in the production area where the ground sensor is not installed, based on the acquisition of the second sensing data including the environmental information and the correlation between the first sensing data and the second sensing data. A receiver that complements the third environmental information,
Based on the production information specified by each farmer using the farming information, a scoring unit that gives a score regarding the evaluation of the crop yield and / or the growth process according to the type of crop of each farmer, and a scoring unit.
It has a grouping section that classifies the farmers into groups based on information about the crops produced by the farmers and / or the soil and / or climate of the production area.
An information processing device in which the grouping unit classifies the farmers into groups by using the third environmental information as information on the soil and / or climate of the production area.

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