JP2020080739A - Program, information processing method, and information processor - Google Patents

Abstract

To provide a program, an information processor and an information processing method for smoothly providing a corporation that provides agricultural production materials with information useful for business support.SOLUTION: Provided is a program that allows an information processor to execute: a first acquisition step S1101 of acquiring farming information by each farmer which is regarding a type of crop produced by the farmer and a production area of the crop; a second acquisition step S1102 of acquiring first sensing data with time of an area including the production area which includes the production information regarding a production amount, quality and/or growth process of the crop from a satellite; and a scoring step S1106 of scoring regarding an evaluation of a yield and/or a growth process of crop corresponding to a type of the crop of each farmer, on the basis of the production information specified by using the farming information of each farmer.SELECTED DRAWING: Figure 11

Description

The present disclosure relates to a program, an information processing method, and an information processing device.

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

In recent years, techniques for utilizing data such as vegetation indexes 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 vegetation growth of target plants for multiple years or cycles, creates an analysis model for vegetation growth based on the collected data, and based on the analysis model, Predict vegetation index. This allows early yield prediction. In addition, a yield prediction method using satellite data by another method, a system that evaluates crops from image data instead of satellite data, and a support management system that uses satellite data are known (for example, See Patent Documents 2 to 4).

JP, 2005-188333, A JP, 2017-125705, A JP, 2010-166851, A JP, 2017-097820, A

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

Therefore, the present disclosure has been made in view of such circumstances, and provides a program, an information processing method, and an information processing apparatus that enable appropriate evaluation of farming of each farmer based on objective data. The purpose is to provide.

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

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

FIG. 1 shows an example of the configuration of an evaluation system 1 according to the present disclosure. FIG. 2 illustrates an example of the hardware configuration of the information processing device 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 a 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 a data structure stored in the score DB 333. FIG. 8 shows an example of a data structure stored in the group DB 333. FIG. 9 shows an example of a table in which the types of crops and the wavelength bands to be evaluated are associated with each other. FIG. 10 shows an example of a sequence diagram showing a process performed by the evaluation system 1 according to 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 according to the second embodiment.

Hereinafter, an embodiment 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 various modifications and application of techniques not explicitly shown below. That is, the example of the present disclosure can be variously modified and implemented without departing from the spirit thereof. In the following description of the drawings, the same or similar reference numerals are given to the same or similar portions, and the drawings are schematic and do not necessarily match actual dimensions and ratios. Furthermore, the drawings may include portions having different dimensional relationships and ratios.

<First Embodiment>
The program, the information processing method, and the information processing apparatus according to the first embodiment include first sensing data (hereinafter, simply referred to as “satellite data”) from a satellite that includes production information regarding the growth amount, quality, and/or growth process of a crop. (Also referred to as "."), the farmer evaluates the harvest result and/or the growth process for each type of crop grown on a predetermined land. The growth 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 harvest result and/or the growth process of the crop of each farmer. By performing such an evaluation process on the entire area included in the satellite data, it is possible to compare and evaluate the harvest results of the crops grown under the same or similar growing conditions or different growing conditions at a plurality of points. It will be possible. Further, if it is possible to compare and evaluate the harvest results of the crops, 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 thus obtained becomes useful data for the farmer to improve the growth amount and/or quality of the crop. If there is such a mechanism that enables farmers to share information regarding the superiority and inferiority of the growing method, it is possible to expect overall improvement and uniformity of the growth amount and/or quality of crops. In addition, in general, the cost of using satellite remote sensing is high for each farmer to use for evaluation of crop yields and growth processes. However, in services that share information about the merits and demerits of breeding methods among farmers, the costs for using satellite remote sensing can be shared directly or indirectly as a fee for using the services, and individual farming It is also possible to suppress the cost borne by the person.

<System configuration>
In the first embodiment, for example, the server 110, which is an information processing device, constructs the evaluation system 1 that evaluates the harvest result and/or 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 crop yield and/or growth process based on farming information and satellite data, and a first terminal 120A and a second terminal 120B that transmit farming information to the server 110. And the third terminal 120C that 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 a first farmer described later, and the second terminal 120B is It is a terminal used by a second farmer described later.

Moreover, the 4th terminal 120D which transmits other information to the server 110 may be connected to the evaluation system 1 via the network 130 as needed. Here, the other information is, for example, temporal sensing data from one or a plurality of ground sensors installed in an area included in the satellite data, and the soil and/or the soil at the point where the ground sensor is installed. Sensing data related to climate (hereinafter, also simply referred to as “ground data”) is included. The fourth terminal 120D that transmits ground data to the server 110 may be a device attached to each ground sensor itself, or a device that receives and aggregates information from each ground sensor and transmits the information to the server 110. May be.

Hereinafter, a description will be given of 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, The means by which the server 110 obtains farming information, satellite data, and ground data is not limited to the method obtained from the first terminal 120A, the second terminal 120B, the third terminal 120C, or the fourth terminal 120D, and an input interface is used. The information may be acquired by inputting via the memory or reading a storage medium in which the information is recorded.

<Hardware configuration of the first embodiment>
FIG. 2 shows an example of the hardware configuration of the information processing device 200 (server 110 and terminals 120A to 120C) 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. The respective components of the HW of the information processing device 200 are connected to each other via the bus B by way of example and not limitation.

The information processing apparatus 200 realizes the functions and/or methods described in the present disclosure 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. To do.

The processor 201 executes a function and/or a method realized by a code or an instruction included in a program stored in the storage 203. The processor 201 is, by way of example and not limitation, a central processing unit (CPU), MPU (Micro Processing Unit), GPU (Graphics processing Unit), microprocessor (microprocessor), processor core (processor core), multiprocessor (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. The memory 202 also temporarily stores various data generated while the processor 201 executes the program. The memory 202 includes, for example and without limitation, a RAM (Random Access Memory), a ROM (Read Only Memory), and the like.

The storage 203 stores the program. The storage 203 includes, for example and without limitation, a HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, and the like.

The communication I/F 205 transmits and receives various data via the network 130. The communication may be executed by wire or wireless, and any communication protocol may be used as long as mutual communication can be executed. The communication I/F 205 has a function of performing 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 another information processing device and transfers the data to the processor 201.

The input/output I/F 204 includes an input device that inputs various operations on the information processing device 200 and an output device that outputs a processing result processed by the information processing device 200. In the input/output I/F 204, an input device and an output device may be integrated, or an input device and an output device may be separated.

The program of the present disclosure may be provided in a state of being stored in a computer-readable storage medium. The storage medium can store the program on a “non-transitory tangible medium”. Programs include, by way of example and not limitation, software programs and computer programs.

At least a part of the processing in the information processing device 200 may be realized by cloud computing including one or more computers. At least a part of the processing in the information processing device 200 may be performed by another information processing device. In this case, at least a part of the processing of each functional unit realized by the processor 201 may be configured to 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 an evaluation process in the evaluation system 1, and includes an input/output I/F 311, a communication I/F 312, a control unit 320, and a storage unit 330. The input/output I/F 311 corresponds to the input/output I/F 204 in FIG. 2, and the communication I/F 312 corresponds to the communication I/F 205 in 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 cooperation of the processor 201 included in the information processing device 200, the memory 202, the storage 203, the input/output I/F 204, and the communication I/F 205. For example, the processor 201 of the information processing apparatus 200 illustrated in FIG. 2 expands various programs (control program, calculation program, etc.) stored in the storage 203 in 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 receiving unit 321, a scoring unit 322, a grouping unit 323, a licensing unit 324, and a matching unit 325. The receiving unit 321, the scoring unit 322, the grouping unit 323, the licensing unit 324, and the matching unit 325 are realized by the processor 201 by reading and executing the program 334 stored in the storage unit 330. It Further, the storage unit 330 stores a farming information DB 331, a satellite data DB 332, a ground data DB 333, a score DB 334, a group DB 335, and a program 336. The functional configuration realized by the server 110 may be appropriately omitted, replaced, or added depending on the embodiment or the example. The storage unit 330 will be described below, and then the control unit 320 will be described.

The farming information DB 331 stores farming information regarding 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 and the second terminal 120B via the network 130. It is an example of a database for storing. As farming information, information used to identify the farmer, the type of crop produced by the farmer, the longitude and latitude of the area where the crop is produced, the area, the information on watering and harvesting of the crop, and used for the crop Information on agricultural production materials such as fertilizers, pesticides, agricultural machines, and information on how to use the agricultural production materials such as date and time, range of use, purpose of use, amount used and number of times, management information on sales volume and unit price of crops, etc. Is 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 site. The farming information shown in FIG. 4 includes, for example, information identifying a farmer (farmer ID), the type of crop produced by the farmer, the longitude and latitude of the production area of the crop, the area, the watering time of the crop, and the harvest time. Information on agricultural production materials such as fertilizers, pesticides, and agricultural machines used for crops, and information on usage methods such as 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 sensing data from satellites regarding an area including a production site over time, and is an example of a database that stores satellite data received by the receiving unit 321 from the third terminal 120C via the network 130. Is. The satellite data includes satellite data including production information on the growth amount, quality, and/or growth process of crops, and also includes first environmental information regarding the environment of the production site such as soil and/or climate that can be read from the spectrum data. It may be. The first environmental information is not particularly limited as long as it is data that can be read from satellite data. For example, soil data such as soil components, ground temperature, water content, acidity (pH), temperature, precipitation, sunshine duration, etc. Meteorological data is included.

Examples of satellite data in the present disclosure include spectrum data of electromagnetic waves reflected or radiated at each point. The spectral data includes production information regarding the growth amount, quality, and/or growth process of crops, and environmental information regarding soil and/or climate in the range of use. For example, when the spectral data is divided into several wavelength bands, a region of 0.52 to 0.6 μm shows plant activity, and a region of 0.63 to 0.69 μm is used for determining the type of plant. Chlorophyll absorption is indicated, the region of 1.55 to 1.75 μm indicates the water content of plants, and the region of 10.4 to 12.5 μm indicates the surface temperature.

In addition to this, the satellite data may include information indicated by an index calculated by four arithmetic operations of reflectances in a plurality of wavelength bands. For example, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), which are known as biological indicators, are plant chlorophylls that strongly affect the 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 reflectance in the near infrared region, red region, and blue region.

Furthermore, the scoring unit 322 of the server 110 uses the satellite data by previously creating a correlation expression that holds between the specific wavelength band and the quality (eg, protein amount) or growth amount of a specific crop. It is also possible to arbitrarily estimate the quality and growth of a particular crop. In this way, the information estimated from the satellite data by the correlation equation 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 considering height difference, and may have electromagnetic wave 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 spectrum data (l, t) at the point 1 at the time point t are shown. For example, the scoring unit 322 of the server 110 causes the scoring unit 322 of the server 110 to use the value of the specific wavelength region in the spectrum data (l, t) or an index calculated based on the specific wavelength region, at the production site (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, and the first environmental information regarding the environment of the production place such as soil and/or climate.

The ground data DB 333 stores sensing data from a ground sensor regarding an area including a production site over time, and is a database that stores ground data received by the receiving unit 321 from the third terminal 120C via the network 130. This is an example. The ground data includes secondary environmental information regarding 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, and includes, for example, soil data such as soil components, ground temperature, water content, acidity (pH), temperature, precipitation. , Weather data such as sunshine hours. The ground data is stored in association with the position information where the ground sensor is installed.

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

The ground data at the point (13) where the ground data cannot be obtained because the ground sensor is not installed like the ground data (t) may be supplemented by estimation. As an example, when the acidity at the point (l1, l2) at the two points is obtained from the ground data, the acidity at the point (l3) at the third point is calculated as the acidity at the point (l1, l2). A method of estimating and complementing by an estimation formula (pH(x)) based on the degrees (11, 12) can be mentioned. However, the complementary method is not limited to this.

Instead of this, a correlation equation between the acidity at the point (11, 12) and the satellite data is calculated, and an estimated equation (for example, pH(x)') corrected from the value obtained from the satellite data is obtained. May be. 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 ground sensors are not installed. Is. Thereby, the accuracy of the environmental information of the production area can be further improved. The third environment information can be stored in the ground data DB 333.

The score DB 334 stores a score related to the evaluation of the harvest result and/or the growth process of the crop given to the farmer, and is an example of a database that stores the score calculated by the scoring unit 322 of the server 110. As the score to be stored, for example, a score related to the amount of crop growth, a score related to crop quality, a score related to evaluation of a growth process from soil preparation suitable for each crop to harvest, or a score obtained by comprehensively evaluating these scores. Is mentioned. In addition, the score to be evaluated is not limited to this, and the configuration may be such that all the processes from soil preparation to harvesting 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, a score relating to the evaluation of the harvest result and/or the growth process of the crop given to the farmer is stored for each crop. These scores are, for example, numerical values such as reflectance of a specific wavelength band in the spectrum data of satellites related to crop growth and soil making, and numerical indexes obtained by four arithmetic operations of reflectance of multiple wavelength bands. Can be represented by

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

The group DB 335 stores the farmers classified into groups based on the crops to be produced and/or the soil and/or the climate of the production area, and the groups classified by the grouping unit 323 of the server 110. It 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 whose farming environment is close. As a result, it is possible to more appropriately share the information about the growing method among farmers who have similar growing environments for crops. Specifically, it is possible to match farmers having similar crop growing environments with each other, and share a method of obtaining a better harvest result in a similar growing environment.

FIG. 8 schematically shows a part of the data structure stored in the group DB 335. In the example of FIG. 8, a 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 linked. Examples of soil types in the production areas for characterizing the groups include satellite data DB 332 such as areas with high and low surface temperatures, areas with high and low pH, and areas with high or low ground water content. It can be determined based on the soil information stored by the ground data DB 333. As the type of climate of the production area for characterizing the group, for example, areas with high or low sunshine, areas with high or low rainfall, areas with high temperature or low temperature, such as satellite data DB332 and terrestrial It can be determined based on the climate information stored by the data DB 333. The types of soil and climate in 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 below.

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. Have the function to do.

The farming information can be input, for example, by each farmer 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, image data of the production area may be input. The image data input here is a copy of the production site, and preferably includes position information attached to the image data and a shooting date and time attached to the image data. The image data acquired by the first terminal 120A and the second terminal 120B is analyzed by the functional units 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 is analyzed. After receiving the information such as the above by the receiving unit 321 of the server 110, the place of the production place 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 the augmented reality (AR) technology to obtain the area of the production site. Further, the server 110 can specify the position of the production place from the position information attached to the image data. Note that the image data may be a panoramic photograph, although it depends on the size of the usage range for photographing.

Further, the image data is photographed according to the growing stage of the crop such as sowing time, watering, using fertilizer or pesticide, and harvesting time, and the first terminal 120A together with information on the growing stage corresponding to the taken image data. And may be input to the second terminal 120B. The server 110, based on the position information associated with the image data input to the first terminal 120A and the second terminal 120B, the shooting date and time, and information about the growing stage thereof, farming information related to the cultivation of crops such as the method of using 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 with respect to the input of the production area, the use period and the number of uses of the agricultural production material, and the like.

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 be automatically determined from the image data.

The satellite data can be received directly from the third terminal 120C that 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. An example of the irregular period is, for example, when the farming information is input, the satellite data is received based on the input farming information.

The ground data is received via the fourth terminal 120D. The fourth terminal 120D stores sensing data regarding soil and/or climate from a sensor installed on the ground. The ground data may be associated with the measurement position information and the measurement time information.

Ground data improves the accuracy of the environmental information to be acquired, such as the purpose of complementing satellite data or data that cannot be acquired only by ground data, and the purpose of further improving the accuracy of satellite data, and more appropriately groups farmers. It can be used for any purpose. As an example for the purpose of supplementing data that cannot be acquired only by satellite data or ground data, as in the example of soil acidity mentioned above, ground data at points where ground data cannot be obtained because ground sensors are not installed are used. There is a case in which it is supplemented 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 with the satellite data and the ground data such as the surface temperature and the water content of the soil, the measured value of the ground data There is a case where the value calculated from the wavelength band of satellite data is calibrated based on.

The scoring unit 322, based on the production information identified from the satellite data DB 332 by using the farming information of each farmer, according to the type of the crop that each farmer grows, the harvesting results of the crops and/or Alternatively, a process for attaching a score relating to the evaluation of the growth process is performed. More specifically, from the satellite data received by the receiving unit 321, a value related to growth amount, quality, soil condition, etc. is extracted, and based on the value, a score related to the growth amount and quality of the crop and/or the growth process. Can be attached.

Here, as the values related to the growth amount, the quality, the soil condition, etc. extracted from the satellite data, for example, the reflectance in a specific wavelength band, the numerical index obtained by the four arithmetic operations of the reflectance in a plurality of wavelength bands, etc. Is mentioned. It should be noted that the wavelength band and the like used when extracting the values relating to the growth amount, quality, soil condition, etc. can be selected appropriately according to the type of crop. For example, the green and red crops have different absorption and reflection wavelength bands of electromagnetic waves (light), so when extracting values relating to growth and quality, a more suitable wavelength band is appropriately selected and used. be able to. Further, regarding the state of soil, etc., a wavelength band suitable for evaluating them can be appropriately selected and used according to the amount of water and the amount of nutrients. It should be noted that a configuration may be used in which the selected wavelength band is given a parameter value for each crop type and adjusted and used.

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

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

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

Furthermore, the index for evaluation of the growth process can be produced in the same manner as above. As an example, the evaluation of the soil making process in the growing process will be described. For example, data (hereinafter, also referred to as “ideal soil data”) such as soil water content, nutrient amount, or pH suitable for cultivation for each type of crop is prepared, and the ideal soil data and the wavelength band indicating the data are prepared. A table in which the spectra are associated with each other is prepared. Then, by comparing the satellite data of the actual crop production area with the wavelength band or spectrum corresponding to the ideal soil data, it is possible to achieve the ideal soil creation 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. Further, the present invention is not limited to this, and the evaluation of growth processes other than soil can also be performed in the same manner as long as it 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, based on the value extracted as described above, as a process of attaching a score relating to the evaluation of the growth amount and quality of the crop, the growth value of the crop, the extracted value may be used as the score as it is, 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 the quality of the crop, based on 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 growth process. The score that comprehensively evaluates the growth amount and quality of the crop can be calculated, for example, as the product or 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 growth process, but is not limited to this. Not done.

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

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

The licensing unit 324 performs a process of issuing a license regarding the use of farming information of other farmers to the 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 farming worker 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 is no particular limitation.

As such consensus building, for example, the licensing unit 324 may change 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 that has achieved a specific harvest result. For example, when an application for referencing farming information is received, the second farmer is notified that the application has been made, and the second farmer receives a notification that the second farmer agrees to disclose his or her farming information. Be done. Further, as will be described later, an agreement is reached that the matching unit 325 matches the first farmer and the second farmer, and causes the first farmer to refer to the farming information of the second farmer between them. The case where it was done is also mentioned.

The license is given 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 on how to use agricultural production materials, information on watering of crops and harvest time. The licensing unit 324, depending on the amount of farming information to be referred to, a license that allows all farming information to be referred to, a license that allows only part of the farming information to be referred to, etc.
It is also possible to issue multiple types of licenses. In this case, the range of farming information disclosed by the license can be agreed upon at the time of forming the agreement.

The method of disclosing farming information to the first farmer who has been issued a license is not particularly limited, and for example, there is a method of allowing the first farmer who has accessed the server 110 to browse the information. Further, the licensing unit 324 may perform payment processing for accepting the consideration of the license from the first farmer and paying all or part of the consideration to the second farmer. Thereby, the second farmer can obtain a new income source other than farming by providing his/her farming information.

The matching unit 325 performs a process of matching the farmers classified into the same group by the grouping unit based on the score. For example, among the farmers who are classified into the same group, the first farmers who have a low score of the evaluation of the harvest performance and/or the growth process of the crops in the previous period are given the harvest performance and/or the growth process of the crops. Match the second farmer with a high evaluation score. As a result, 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 that achieves a specific harvest result, the farming information of the second farmer is provided. When an agreement is reached to refer the first farmer, the licensing unit 324 issues a license to the first farmer.

By performing such matching processing, it is possible to share information regarding the superiority or inferiority of the growing method among farmers who grow the same crop under similar environments. As a result, it is possible to expect an overall improvement and uniformization of the growth amount and/or quality of crops while suppressing the cost borne by individual farmers.

<Operation processing of the first embodiment>
Processing of the program according to the first embodiment will be described with reference to FIGS. Note that the processing procedure described below is merely an example, and each processing may be modified as much as possible within the scope of the technical idea of the present disclosure, and steps may be omitted, replaced, and added as appropriate. is there.

First, the processing sequence of the evaluation system 1 will be described. FIG. 10 shows an example of a sequence diagram showing a process performed by the evaluation system 1 according to the first embodiment. In 1st acquisition step S1001, 1st terminal 120A and 2nd terminal 120B each transmit farming information, the server 110 acquires these information, and in 2nd acquisition step S1002, 3rd terminal 120C shows 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 time-dependent sensing data from one or a plurality of ground sensors installed in an area included in the satellite data, as other information, The server 110 may acquire the information by transmitting 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.

The server 110 executes a scoring step, a grouping step, and the like, which will be described later (S1004 to S1007), and then executes a matching step to match the first farmer with the second farmer (S1008). The server 110 transmits a notification recommending the second farmer with the score to the first terminal 120A of the first farmer (S1009), and the first farmer refers to the score of the second farmer, If it is desired to obtain farming information, a license purchase application is made 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 has received 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 license issuance. In addition, the second farmer registers the information to approve all purchase requests for licenses in the server 110, thereby omitting the approval step (S1011 to S1012) on the second farmer side. Good. Steps S1004 to S1008 performed by the server 110 correspond to steps S1104 to 1108 to be described later, and thus the description thereof will be 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.

(Process of acquiring various data)
First, the receiving unit 321 of the server 110 obtains, for each farmer, farming information on the types of crops produced by the farmer and the production areas of the crops, a first acquisition step S1101, and the growth amount, quality, and And/or a second acquisition step S1102 of acquiring satellite data including production information regarding the growth process. Further, in addition to these, the receiving unit 321 of the server 110 may execute a third acquisition step S1103 that acquires ground data including second environment information regarding soil and/or climate in the production area.

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

For example, the farming information acquired in the first acquisition step S1101 includes farming information on the type of crop produced by the farmer and the production area of the crop, but instead of this, image data of the production area is acquired instead. You can also In the first acquisition step S1101, when the image data is acquired as a part of the farming information, the reception unit 321 of the server 110 analyzes the image data by using augmented reality (AR) technology or the like, and uses the area. And a process of identifying the position of the use range and the use date and time from the position information and the shooting date and time associated with the image data.

Further, when the ground data is acquired in the third acquisition step S1103, the reception unit 321 of the server 110 may perform the calibration process between the satellite data and the ground data in the calibration step S1104. Examples of such a calibration process include a process of calibrating a physical quantity included in ground data based on a physical quantity included in satellite data, and a process of calibrating a physical quantity included in satellite data based on a physical amount included 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. In addition, the physical quantity means an amount which can be quantified as a numerical value in physics and an amount calculated from the calculation.

Further, in the complementing step S1105, the receiving unit 321 of the server 110 may perform complementing processing between satellite data and ground data. Examples of such complementary processing include, for example, processing for complementing a physical quantity partially lacking in ground data based on a physical quantity included in ground data and satellite data, and a physical quantity partially lacking in satellite data for ground data and satellites. The process of complementing based on the physical quantity contained in the data is mentioned. The complementary process is a process of estimating a value lacking in one data based on the data and the other data.

(Process of expressing the evaluation of crop yield and/or growth process as a score)
Next, in scoring step S1106, the scoring unit 322 of the server 110 provides the farmer with a score related to the evaluation of the crop yield and/or the growth process for each crop type based on the production information included in the satellite data. Perform processing to attach. In the scoring process, as described above, as an example, it is possible to extract a value related to the growth amount or the quality from the satellite data and calculate the score based on the value. 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 value of the reflectance in the wavelength band required for the evaluation of the target crop is extracted from the satellite data, and the value obtained is used to calculate the growth rate and quality. A value for can be extracted.

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

(Process to divide farmers into groups)
In the grouping step S1107, the grouping unit 324 of the server 110 performs a process of classifying the farmers into groups based on the information on the type of crop and/or soil and/or climate of the production area. For example, a group characterized by the type of crop, the type of soil of the production site, and the type of climate of the production site is created in advance, and the farming information of the farmer and the first to third environmental information of the farmer in the production site are created. Farmers can be assigned to groups based on. In addition, when creating a group in advance, a process of clustering the first to third environment 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 the farmers who match the conditions based on the score. The mode of matching is not particularly limited, but for example, it is possible to match a high-scoring farmer with a low-scoring farmer or a low-scoring farmer with a high-scoring farmer. Conceivable. As a result, it is expected that information on excellent breeding methods will be shared among farmers and that the overall growth amount and/or quality of crops will be improved.

Further, as another aspect, for example, it is conceivable to match the farmers with high scores or the farmers with low scores. As a result, a farmer with a high score can further improve the growth amount and/or quality of a crop, and a farmer with a low score can refer to the growing method of a farmer with a low score. It is expected that you will learn the training method which is not recommended.

The matching may be performed by matching farmers who belong to the same group, or may be performed by matching farmers who belong to different groups. When the farmers belonging to the same group are matched with each other, the farmers who grow the same crop under similar environments are matched with each other. Therefore, the matching enables the first farmer with the poor score to obtain the direct and effective information for improving the growth amount and/or the quality from the second farmer with the excellent score. On the other hand, when the farmers belonging to different groups are matched, the farmers who grow different crops or crops under different environments are matched. In this case, for example, for a first farmer who is thinking of starting a new crop or a new environment for growing a crop, a second farmer who has already achieved excellent harvesting results is required to start the cultivation. It becomes possible to obtain basic information.

(Process to give license to farmer)
Finally, in the licensing step S1109, the licensing unit 326 of the server 110 performs a process of issuing a license regarding the use of farming information of another farmer to the farmer. The license may be issued to a matched farmer or an unmatched farmer. Here, when issuing a license to an unmatched farmer, for example, the score of another farmer is set to be viewable to the farmer, and the farmer who browsed the score of another farmer is set. From the case of accepting applications for licenses for farming information of other farmers. That is, there is a case in which the farmer himself selects another farmer and receives the license without performing the matching process.

<Second Embodiment>
Next, the second embodiment will be described. The program, the information processing method, and the information processing apparatus according to the second embodiment are such that the score provided in the scoring step can be used as credit information that can be used for financing of a financial institution.

In the following description, the same functional configurations as those of 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. The differences from the first embodiment will be mainly described below.

In the second embodiment, for example, the server 110 has a credit information creating unit as one of the functional units, and the credit information creating unit creates credit information of a farmer having the score based on the score. Perform the creation steps. Here, it is preferable that the credit information includes scores for the past several years. FIG. 12 shows an example of a flowchart of processing in the server 110 according to the second embodiment.

According to this aspect, it is possible to create, for the farmer, credit information commensurate with the farm management ability. As a result, for example, it can be expected that, for example, the farmer will be able to easily borrow funds for new agricultural production materials, reserves for expanding production areas, and the like from financial institutions. In addition, it is possible for financial institutions to objectively evaluate the farming ability of farmers, which is difficult to evaluate, and even when considering the risk that income stability will be adversely affected by the weather, etc. It becomes possible to put the trust of.

The credit information preferably has a score for the last few years. Further, it is preferable that the credit information is information in which the score for each period is associated with the environmental information of the period. This makes it possible to evaluate the harvest performance of the farmer in an unfavorable environment. That is, even in a poor environment, if it can be evaluated that the harvest performance and/or the growth process of the farmer is relatively high, the farmer may be at risk of impairing the income stability due to the weather or the like. It can be evaluated as having a farming capacity that can be reduced.

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 spirit of the invention. That is, the above embodiments are merely examples in all respects, and should not be construed as limited.

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

In addition, processing for obtaining production information regarding the amount of crops, quality, and/or growth process based on satellite data and information regarding the environment of the production area such as soil and/or climate is 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 the program expanded in the memory 202 of the server 110. Instead of this, for example, a program loaded in the memory 202 of the server 110 on the cloud by API cooperation performs the above processing on the satellite data stored in the storage of the external institution system, and if necessary, Then, the obtained production information and the like may be used for information processing by the server 110.

Furthermore, 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 any 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 apparatus, 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 ...Reception 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 (11)

In the information processing device,
A first acquisition step for acquiring farming information for each farmer, the farming information relating to the type of crop produced by the farmer and the production area of the crop;
A second acquisition that acquires, from a satellite, the first sensing data of the area including the production site over time, the first sensing data including production information regarding the growth amount, quality, and/or growth process of the crop. Steps,
Based on the production information identified using the farming information of each farmer, a scoring step of attaching a score relating to the evaluation of the crop achievement and/or the growth process of the crop according to the type of the crop of each farmer, A program that runs The program according to claim 1, which executes a grouping step of classifying the farmer into groups based on information on crops produced by the farmer and/or soil and/or climate of the production area. The first sensing data includes first environmental information regarding soil and/or climate in the production area,
The program according to claim 2, wherein, in the grouping step, the farmer is classified into a group by using the first environmental information as information about soil and/or climate of the production area. A third acquisition step of acquiring second sensing data, which is time-dependent second sensing data including second environmental information regarding soil and/or climate in the production site, from a ground sensor installed in the production site. Run and
The program according to claim 2 or 3, wherein, in the grouping step, the farmer is classified into a group by using the second environmental information as information on soil and/or climate of the production area. Based on the correlation between the first sensing data and the second sensing data, a complementary step of complementing third environmental information on soil and/or climate in a production area where the ground sensor is not installed,
The program according to claim 4, wherein, in the grouping step, the farmer is classified into a group by using the third environmental information as information on soil and/or climate of the production area. The program according to any one of claims 2 to 5, which causes the farmers classified into the same group to perform a matching step of matching based on the score. The program according to any one of claims 2 to 6, which causes the farmer to perform a licensing step of issuing a license regarding use of farming information of another farmer. The program according to claim 1, which executes a step of creating credit information of the farmer having the score based on the score. In the first acquisition step,
The program according to any one of claims 1 to 8, wherein a part of the farming information is created based on the image data of the production area and position information attached to the image data. The information processing device
A first acquisition step for acquiring farming information for each farmer, the farming information relating to the type of crop produced by the farmer and the production area of the crop;
A second acquisition that acquires, from a satellite, the first sensing data of the area including the production site over time, the first sensing data including production information regarding the growth amount, quality, and/or growth process of the crop. Steps,
Based on the production information identified using the farming information of each farmer, a scoring step of attaching a score relating to the evaluation of the crop achievement and/or the growth process of the crop according to the type of the crop of each farmer, An information processing method for executing. The farming information on the type of crops produced by the farmer and the production area of the crops, and the first sensing data of the area including the production area from the artificial satellite over time, the amount of growth of the crops, the quality, And/or a first sensing data including production information about a growing process, and a receiving unit,
Based on the production information identified using the farming information of each farmer, a score assigning unit that attaches a score relating to the evaluation of crop yield and/or growth process according to the type of crop of each farmer, An information processing apparatus having: