Information processing device and method

Abstract

A server 20 of a farm management system 1 according to the present invention executes, as functions of same: a step (S101) for acquiring scan data obtained by scanning farmland; a step (S103) for estimating a farming status of the farmland by analyzing the scan data; a step (S104) for estimating and calculating an emission amount of greenhouse gas (GHG) in the farmland on the basis of the estimation result of the farming status of the farmland; a step (S105) for acquiring, on the basis of the estimated result of the farming status of the farmland, prescribed information for providing information pertaining to the farmland; and a step (S106) for outputting the estimated result of the farming status of the farmland, the information about the calculated emission amount of GHG, and the acquired prescribed information.

Description

Information Processing Apparatus and Method

[0001] The present disclosure relates to an information processing apparatus and a method.

[0002] In recent years, due to the increasing awareness of environmental protection, efforts have been made to reduce the emissions of greenhouse gases (GHGs: Greenhouse Gases). GHGs include, for example, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorocarbon gases.

[0003] Such efforts to reduce GHG emissions are also the same in the agricultural field. Therefore, in agricultural management of farmland, an agricultural management method with low GHG emissions and low environmental impact is recommended.

[0004] Patent Document 1 discloses a system for estimating the emission amount of methane gas, which is a type of GHG, from a field. The methane emission estimation system described in Patent Document 1 acquires observation data of paddy fields from satellites and estimates the flooding status of paddy fields using a learned model to estimate the emission amount of methane gas from paddy fields.

[0005] Japanese Patent Application Laid-Open No. 2023-174067

[0006] By the way, as described above, due to social demands, legal obligations, etc., in order to reduce GHG emissions in various industries, it is required to calculate and manage GHG emissions, and GHG emissions are also calculated in the system described in Patent Document 1. In addition, the Ministry of the Environment is also considering calculating GHG emissions from agricultural soil. Furthermore, food companies that purchase harvested agricultural products are also required to calculate and manage GHG emissions.

[0007] However, in the actual agricultural field, the GHG emissions vary depending on the agricultural management status of the farmland, such as the amount of pesticides used, etc., and it has not been easy to accurately calculate the GHG emissions. Furthermore, even if the GHG emissions can be calculated, it has not been easy to obtain information on methods for improving them.

[0008] Therefore, this disclosure describes a technology that enables the accurate calculation of GHG emissions and the provision of information on the farming conditions of agricultural land.

[0009] According to one embodiment of the present disclosure, an information processing device for managing GHG (Green House Gas) emissions in farmland comprises a control unit and a memory, wherein the control unit performs the steps of: acquiring scan data obtained by scanning farmland; analyzing the scan data to estimate the farming conditions of the farmland; estimating and calculating GHG emissions in the farmland based on the estimation results of the farming conditions of the farmland; acquiring predetermined information for providing information about farmland based on the estimation results of the farming conditions of the farmland; and outputting the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information.

[0010] According to this disclosure, the system analyzes scan data obtained by scanning farmland to estimate the farming conditions of the land, and then estimates and calculates the amount of GHG emissions from the farmland. Furthermore, based on the estimation results of the farming conditions of the land, predetermined information for providing information about farmland is acquired, and the estimated results of the farming conditions of the land, the calculated GHG emission information, and the acquired predetermined information are output. This makes it possible to accurately calculate GHG emissions and to provide information about the farming conditions of farmland.

[0011] This is a diagram showing the overall configuration of the farm management system 1. This is a block diagram showing the functional configuration of the terminal device 10 in Figure 1. This is a diagram showing the functional configuration of the server 20 in Figure 1. This is a schematic diagram explaining the functions of the server 20 in Figure 3. This is a schematic diagram explaining the functions of the server 20 in Figure 3. This is a diagram showing an example of the data structure of the scan database 2021 in Figure 3. This is a diagram showing an example of the data structure of the field database 2022 in Figure 3. This is a flowchart showing an example of the flow of farm information output processing by the farm management system 1. This is a diagram showing an example of a screen displaying farm improvement information on the terminal device 10.

[0012] The embodiments of this disclosure will be described below with reference to the drawings. In all the drawings illustrating the embodiments, common components are denoted by the same reference numerals, and repeated explanations are omitted. The following embodiments are not intended to unduly limit the content of this disclosure as described in the claims. Not all components shown in the embodiments are necessarily essential components of this disclosure. Also, each drawing is a schematic diagram and is not necessarily a strict illustration.

[0013] Furthermore, in the following description, "processor" refers to one or more processors. At least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may be another type of processor such as a GPU (Graphics Processing Unit). At least one processor may be single-core or multi-core.

[0014] Furthermore, at least one processor may be a broad-sense processor, such as a hardware circuit that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)).

[0015] Furthermore, in the following explanation, we may describe information that yields an output for a given input. This information can be data of any structure, or it can be a learning model such as a neural network that generates an output for a given input.

[0016] Furthermore, in the following explanation, the tables that make up each database are just examples; one table may be divided into two or more tables, or all or part of two or more tables may be a single table.

[0017] Furthermore, in the following explanation, the subject of the process may sometimes be "program," but since a program is executed by a processor and performs defined processes using the memory and / or interface as appropriate, the subject of the process may also be the processor (or a device such as a controller that has that processor).

[0018] The program may be installed on a device such as a computer, or it may reside on a program distribution server or a computer-readable (e.g., non-temporary) recording medium. Furthermore, in the following description, two or more programs may be implemented as a single program, or one program may be implemented as two or more programs.

[0019] Furthermore, in the following explanation, identification numbers are used as identification information for various objects, but other types of identification information (for example, identifiers including letters or symbols) may also be used.

[0020] Furthermore, in the following explanations, when describing similar elements without distinction, a reference code (or a common code among reference codes) may be used, and when describing similar elements with distinction, the element's identification number (or reference code) may be used.

[0021] Furthermore, in the following explanation, only control lines and information lines deemed necessary for the explanation are shown, and not all control lines and information lines in the product are necessarily shown. All components may be interconnected.

[0022] <Overview> The following describes the farm management system related to this disclosure. The farm management system related to this disclosure is a system for estimating and calculating GHG (Green House Gas) emissions from farmland by analyzing scan data of farmland. This farm management system analyzes scan data obtained by scanning farmland, for example, image data of farmland taken from satellites, to estimate the farming conditions of the farmland and estimate and calculate the GHG emissions from the farmland. Furthermore, based on the estimation results of the farming conditions of the farmland, it acquires predetermined information for providing information about the farmland and outputs the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information. The farm management system related to this disclosure is a system provided as a web service, for example via a cloud server, using so-called SaaS (Software as a Service), and is configured to allow users to access it through predetermined authentication.

[0023] Here, GHG refers to greenhouse gases, including, for example, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and chlorofluorocarbons (CFCs). In agricultural land, CO2 is generated by the activity of microorganisms present in the soil. Also, while crops absorb CO2 through photosynthesis, they also release CO2 through respiration.

[0024] As mentioned above, due to social demands and legal obligations, various industries are required to calculate and manage their GHG emissions in order to reduce them. The Ministry of the Environment is also considering calculating GHG emissions from agricultural soil, and food companies that purchase harvested agricultural products are also being asked to calculate and manage their GHG emissions. However, in actual agricultural practice, GHG emissions vary depending on the farming conditions of the land, such as the amount of pesticides used, making it difficult to accurately calculate GHG emissions. Furthermore, even if it was possible to calculate GHG emissions, it was not easy to obtain information on methods to improve them.

[0025] Therefore, the farm management system described in this disclosure is configured to estimate the farming conditions of farmland by analyzing scan data obtained by scanning farmland, for example, image data of farmland taken from satellites, and to estimate and calculate the amount of GHG emissions from farmland. Furthermore, the farm management system described in this disclosure is configured to obtain predetermined information for providing information on farmland based on the estimation results of the farming conditions of farmland, and to output the estimation results of the farming conditions of farmland, the calculated GHG emission information, and the obtained predetermined information.

[0026] This configuration makes it possible to accurately calculate GHG emissions and provide information on the farming status of agricultural land by using the farm management system described in this disclosure. This will contribute to calculating and managing GHG emissions in response to social demands, legal obligations, etc.

[0027] <First Embodiment> The farm management system 1 will be described below. In the following description, for example, when the terminal device 10 accesses the server 20, the server 20 responds with information for the terminal device 10 to generate a screen. The terminal device 10 generates and displays a screen based on the information received from the server 20.

[0028] <1. Overall Configuration of Farm Management System 1> Figure 1 shows the overall configuration of the farm management system 1. As shown in Figure 1, the farm management system 1 includes a plurality of terminal devices (in Figure 1, terminal devices 10A and 10B are shown; hereinafter, they may be collectively referred to as "terminal devices 10"), a server 20, and an external server 30. The terminal devices 10, the server 20, and the external server 30 are connected to each other so as to be able to communicate with each other via a network 80. The network 80 is composed of a wired or wireless network. The network 80 includes, for example, 3G, 4G, 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks that can connect to the internet via a predetermined access point (e.g., Wi-Fi®). When the network 80 is connected wirelessly, communication protocols include, for example, Z-Wave®, ZigBee®, Bluetooth®, etc. Furthermore, when connecting via a wired connection, the network includes connections made directly using USB (Universal Serial Bus) cables, etc.

[0029] Terminal device 10 is a device operated by each user. Here, a user is a person who uses terminal device 10 to manage the farming status of farmland and manage GHG emissions, etc., which are functions of the farm management system 1, and refers to producers, various businesses, etc. related to the farmland. Terminal device 10 is implemented as a stationary PC (Personal Computer), laptop PC (notebook PC), etc. In addition, terminal device 10 may be a mobile terminal such as a tablet compatible with a mobile communication system or a smartphone.

[0030] The terminal device 10 is connected to the server 20 via the network 80 so as to be able to communicate with it. The terminal device 10 is connected to the network 80 by communicating with communication equipment such as a wireless base station 81 that supports communication standards such as 4G, 5G, and LTE, and a wireless LAN router 82 that supports wireless LAN (Local Area Network) standards such as IEEE (Institute of Electrical and Electronics Engineers) 802.11. As shown as terminal device 10B in Figure 1, the terminal device 10 includes a communication interface 12, an input device 13, an output device 14, a memory 15, a storage unit 16, and a processor 19.

[0031] The communication interface 12 is an interface for inputting and outputting signals so that the terminal device 10 can communicate with an external device. The input device 13 is an input device (for example, a keyboard, touch panel, touchpad, mouse, or other pointing device) for receiving input operations from the user. The output device 14 is an output device (display, speaker, etc.) for presenting information to the user. The memory 15 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as DRAM (Dynamic Random Access Memory). The storage unit 16 is a storage device for saving data, such as flash memory or HDD (Hard Disk Drive). The processor 19 is hardware for executing the instruction set written in the program, and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0032] Server 20 acquires and analyzes scan data obtained by scanning farmland, such as image data of farmland taken from satellites, to estimate the farming conditions of the farmland and to estimate and calculate the amount of GHG emissions from the farmland. Based on the estimation results of the farming conditions of the farmland, Server 20 acquires predetermined information for providing information about farmland and outputs the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information.

[0033] Server 20 is a computer connected to network 80. Server 20 can be virtually realized by distributing all or part of its hardware configuration across multiple computers and connecting them to each other via a network. Thus, Server 20 is a concept that includes not only a computer housed in a single enclosure or case, but also a virtualized computer system. Server 20 includes a communication interface 22, an input / output interface 23, memory 25, storage 26, and a processor 29.

[0034] The communication interface 22 is an interface for inputting and outputting signals so that the server 20 can communicate with external devices. The input / output interface 23 functions as an interface to an input device for receiving input operations from the user and an output device for presenting information to the user. The memory 25 is for temporarily storing programs and data processed by programs, etc., and is a volatile memory such as DRAM (Dynamic Random Access Memory). The storage 26 is a storage device for saving data, such as flash memory or an HDD (Hard Disk Drive). The processor 29 is hardware for executing the instruction set written in the program, and is composed of an arithmetic unit, registers, peripheral circuits, etc.

[0035] The external server 30 is a server device for acquiring various information about farmland, and includes a device for acquiring information on soil, weather, and topography of farmland fields from ground sensors, and a device for acquiring field information for each field from artificial satellites in Earth orbit. The external server 30 may also include a device for providing weather data from various locations. It is also possible to configure the system to have this function in server 20 and not to have an external server 30, but in this disclosure, the farm management system 1 will be described as having a configuration that includes an external server 30.

[0036] <1.1 Configuration of Terminal Device 10> Figure 2 is a block diagram showing the functional configuration of the terminal device 10 in Figure 1. As shown in Figure 2, the terminal device 10 includes a plurality of antennas (antenna 111, antenna 112), wireless communication units corresponding to each antenna (first wireless communication unit 121, second wireless communication unit 122), an operation reception unit 130 (including a keyboard 131 and a mouse 132), an audio processing unit 140, a microphone 141, a speaker 142, a display 150, a storage unit 160, and a control unit 170. The terminal device 10 also has functions and configurations not specifically shown in Figure 2 (for example, a battery for maintaining power, a power supply circuit for controlling the supply of power from the battery to each circuit, etc.). As shown in Figure 2, each block included in the terminal device 10 is electrically connected by a bus or the like.

[0037] Antenna 111 radiates signals emitted by terminal device 10 as radio waves. Antenna 111 also receives radio waves from space and provides the received signals to first wireless communication unit 121.

[0038] Antenna 112 radiates signals emitted by terminal device 10 as radio waves. Antenna 112 also receives radio waves from space and provides the received signals to second wireless communication unit 122.

[0039] The first wireless communication unit 121 performs modulation and demodulation processing, etc., for the terminal device 10 to transmit and receive signals via the antenna 111 in order to communicate with other wireless devices. The second wireless communication unit 122 performs modulation and demodulation processing, etc., for the terminal device 10 to transmit and receive signals via the antenna 112 in order to communicate with other wireless devices. The first wireless communication unit 121 and the second wireless communication unit 122 are a communication module that includes a tuner, an RSSI (Received Signal Strength Indicator) calculation circuit, a CRC (Cyclic Redundancy Check) calculation circuit, a high-frequency circuit, etc. The first wireless communication unit 121 and the second wireless communication unit 122 perform modulation, demodulation, and frequency conversion of the wireless signals transmitted and received by the terminal device 10, and provide the received signal to the control unit 170.

[0040] The operation reception unit 130 has a mechanism for receiving a user's input operation. Specifically, the operation reception unit 130 includes a keyboard 131 and a mouse 132. Note that the operation reception unit 130 may be configured as a touch screen that detects the contact position of the user on the touch panel by using, for example, a capacitive touch panel.

[0041] The keyboard 131 receives a user's input operation of the terminal device 10. The keyboard 131 is a device for inputting characters, and outputs the input character information to the control unit 170 as an input signal.

[0042] The mouse 132 receives a user's input operation of the terminal device 10. The mouse 132 is a pointing device for selecting, etc., a display object displayed on the display 150, and outputs the position information selected on the screen and the information indicating that a button is pressed to the control unit 170 as input signals.

[0043] The voice processing unit 140 performs modulation and demodulation of voice signals. The voice processing unit 140 modulates the signal given from the microphone 141 and gives the modulated signal to the control unit 170. Also, the voice processing unit 140 gives a voice signal to the speaker 142. The voice processing unit 140 is realized by, for example, a processor for voice processing. The microphone 141 receives voice input and gives a voice signal corresponding to the voice input to the voice processing unit 140. The speaker 142 converts the voice signal given from the voice processing unit 140 into voice and outputs the voice to the outside of the terminal device 10.

[0044] The display 150 displays data such as images, videos, texts, etc. according to the control of the control unit 170. The display 150 is realized by, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0045] The storage unit 160 is composed of, for example, a memory 15 such as a flash memory and a storage unit 16, and stores data and programs used by the terminal device 10. In a certain aspect, the storage unit 160 stores user information 161.

[0046] The user information 161 is information of a user who manages the farming situation of farmland and manages the GHG emission amount, etc., which are functions of the farming management system 1 using the terminal device 10. The user information includes information for identifying the user (business operator ID), the name of the user (corporate name, etc.), organizational information of an enterprise, etc.

[0047] The control unit 170 is constituted by, for example, a processor 19, reads a program stored in the storage unit 160, and controls the operation of the terminal device 10 by executing instructions included in the program. The control unit 170 is, for example, an application pre-installed in the terminal device 10. By operating according to the program, the control unit 170 functions as an input operation reception unit 171, a transmission / reception unit 172, a data processing unit 173, and a notification control unit 174.

[0048] The input operation reception unit 171 performs a process of receiving a user's input operation on an input device such as a keyboard 131.

[0049] The transmission / reception unit 172 performs a process for the terminal device 10 to transmit and receive data to and from an external device such as a server 20 according to a communication protocol.

[0050] The data processing unit 173 performs a process of performing an operation on the data input by the terminal device 10 according to a program and outputting the operation result to a memory or the like.

[0051] The notification control unit 174 performs a process of presenting information to the user. The notification control unit 174 performs a process of displaying a display image on the display 150, a process of outputting sound to the speaker 142, etc.

[0052] <1.2 Functional Configuration of Server 20> FIG. 3 is a diagram showing the functional configuration of the server 20 in FIG. 1. As shown in FIG. 3, the server 20 functions as a communication unit 201, a storage unit 202, and a control unit 203.

[0053] The communication unit 201 performs a process for the server 20 to communicate with an external device.

[0054] The storage unit 202 stores data and programs used by the server 20. The storage unit 202 stores the scan database 2021, the field database 2022, and the like.

[0055] Scan Database 2021 is a database for storing scan data obtained by scanning farmland, which is managed using the farm management system 1 to track farming conditions and manage GHG emissions. Further details will be described later.

[0056] The Field Database 2022 is a database for holding data on farmland fields that are managed using the Farm Management System 1 to track farming conditions and manage GHG emissions. Further details will be provided later.

[0057] The control unit 203 performs the functions shown in the reception control module 2031, transmission control module 2032, scan data acquisition module 2033, field division module 2034, farming status estimation module 2035, GHG emission calculation module 2036, predetermined information acquisition module 2037, and output module 2038 as various modules, as the server 20's processor 29 processes according to the program.

[0058] The reception control module 2031 controls the process by which the server 20 receives signals from an external device according to a communication protocol.

[0059] The transmission control module 2032 controls the process by which the server 20 transmits signals to an external device according to a communication protocol.

[0060] The scan data acquisition module 2033 controls the process of acquiring scan data obtained by scanning farmland. The scan data acquisition module 2033 accesses an external server 30, for example, via the communication unit 201, and acquires scan data held by the external server 30. The scan data acquired by the scan data acquisition module 2033 is, for example, photographic data obtained by an artificial satellite photographing farmland from Earth's orbit, but is not limited to photographic data in visible light, and may also be image data obtained by observing infrared or microwave reflected waves irradiated onto the farmland by the satellite.

[0061] The scan data acquisition module 2033 may acquire, along with the scan data, data indicating the scanned location on the ground, i.e., the location of the farmland, information on the date and time the scan was performed, and data indicating the size (scale ratio) of the farmland from the external server 30. The data indicating the location of the farmland may be data indicating the latitude and longitude on the ground, or address data.

[0062] Furthermore, the scan data acquisition module 2033 stores the acquired scan data, for example, in the scan database 2021.

[0063] The field division module 2034 analyzes the scan data acquired by the scan data acquisition module 2033 and controls the process of dividing the scan data representing farmland into fields that are separated into predetermined regions. For example, the field division module 2034 analyzes the scan data and divides the farmland into separate regions (polygons) if the farmland has different colors, or if there are areas that are not farmland, such as farm roads, between areas that are considered farmland. At this time, the field division module 2034 may use an existing color analysis method for images to determine that areas with different colors exceeding a predetermined threshold are different regions (field boundaries) and divide the farmland into fields.

[0064] The field division module 2034 may, when dividing scan data into fields, use a pre-trained model (polygon generation model) that learns on both the scan data and the data after the division into fields, and outputs the divided scan data as fields. This training model can be any training model that has undergone appropriate training, such as a supervised machine learning model using predetermined training data, an unsupervised machine learning model, a training model using a deep neural network (DNN), which is a multilayer neural network targeted by deep learning, or an artificial intelligence model. Furthermore, this training model does not need to be a single training model; it may be implemented by switching between multiple independent training models for each training data set.

[0065] Furthermore, the field division module 2034 may use not only the analysis of scan data but also field division information provided by a designated institution (e.g., a national institution) as a method for dividing the field.

[0066] Figure 4 is a schematic diagram illustrating the functions of the server 20 in Figure 3. The scan data acquisition module 2033 acquires scan data IM1 as shown in Figure 4. Then, the field division module 2034 divides the scan data IM1 into fields FD1, FD2, and FD3 by dividing it according to the boundaries of the colors, etc., and generates image data IM2, which is a map of the divided fields.

[0067] Furthermore, the field division module 2034 stores the image data obtained by dividing the scanned data into fields, for example, in the field database 2022.

[0068] The farming status estimation module 2035 controls the process of analyzing scan data to estimate the farming status of the farmland. The target of analysis by the farming status estimation module 2035 may be image data divided into fields by the field division module 2034, or, if the field division module 2034 has not divided the fields, it may be scan data acquired by the scan data acquisition module 2033. The farming status estimation module 2035 estimates the farming status of the farmland using methods such as image analysis. For example, the farming status estimation module 2035 estimates crops, soil conditions (such as nitrogen and carbon content in the soil), fallow land, etc., on the farmland (field).

[0069] In this case, when estimating the farming status of farmland, the farming status estimation module 2035 may use a pre-trained model (crop estimation model) that outputs the farming status of farmland by learning from, for example, scan data (image data divided into fields) and data on crops, soil conditions (such as nitrogen and carbon content in the soil), fallow land, etc. in the farmland (field). This training model can be any training model that has undergone appropriate training, for example, it may be a training model using supervised machine learning with predetermined training data, or an unsupervised machine learning training model, or it may include a training model using a deep neural network (DNN), which is a multilayer neural network that is the target of deep learning. Furthermore, this training model does not have to be a single training model, and may be implemented by switching between multiple independent training models for each training data.

[0070] Furthermore, the learning model used by the farming condition estimation module 2035 may be one that has learned information such as humus content and nitrogen content obtained from field information. Humus content refers to the amount of humus contained in the field soil. Humus is a black substance (high molecular weight compound) produced by the decomposition of the remains of plants and animals accumulated in the soil. Humus has an important influence on soil properties and productivity. This service allows for the instantaneous acquisition and display of humus content for each field over a wide area. Nitrogen content refers to the amount of nitrogen contained in the field soil and is calculated based on vegetation indicators. Vegetation refers to a group of plants growing together in a given area. Vegetation indicators are indicators used to understand the state of vegetation (quantity and vitality of plants). Vegetation indicators are calculated using the characteristics of light reflection possessed by plants. As a vegetation index, for example, the NDVI (Normalized Difference Vegetation Index) can be used.

[0071] The farming status estimation module 2035 estimates the farming status of fields FD1, FD2, and FD3 in the image data IM2 of farmland, as shown in Figure 4, using methods such as image analysis. For example, as shown in Figure 4, the farming status estimation module 2035 estimates that the crop in field FD1 is wheat, field FD2 is fallow land, and the crop in field FD3 is maize. Then, for image data IM3, which is similar to image data IM2, the farming status estimation module 2035 associates the estimated crop with fields FD1, FD2, and FD3.

[0072] Furthermore, the farming status estimation module 2035 stores data showing the estimation results of farming status in, for example, the field database 2022.

[0073] The GHG emission calculation module 2036 controls the process of estimating and calculating GHG (Green House Gas) emissions from farmland based on the estimation results of farming conditions for farmland by the farming conditions estimation module 2035. The GHG emissions calculated by the GHG emission calculation module 2036 include, but are not limited to, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and chlorofluorocarbons, as described above.

[0074] In certain situations, the GHG emission calculation module 2036 may estimate and calculate GHG emissions from farmland based on the estimation results of farming conditions of farmland by the farming conditions estimation module 2035 and supplementary information about farmland and fields received and acquired by the predetermined information acquisition module 2037, which will be described later. The predetermined information acquisition module 2037 receives input of supplementary information about farmland and fields, such as meteorological data such as temperature, precipitation, solar radiation, and ground surface temperature, and farming data such as sowing / transplanting, farming methods, fertilizer application, pesticide application, and harvesting, from producers, farmland managers, etc. The GHG emission calculation module 2036 may estimate and calculate GHG emissions based on this information.

[0075] In this case, the GHG emission calculation module 2036 may use a trained model (field parameter estimation model) that learns from, for example, the estimated results of farming conditions on farmland, the received supplementary information, and GHG emissions, and outputs GHG emissions. This trained model can be any trained model that has undergone appropriate training, for example, a trained model using supervised machine learning with predetermined training data, a trained model using unsupervised machine learning, or a trained model using a deep neural network (DNN), which is a multilayer neural network targeted by deep learning. Furthermore, this trained model does not need to be a single trained model, and may be implemented by switching between multiple independent trained models for each training data.

[0076] Figure 5 is a schematic diagram illustrating the functions of the server 20 in Figure 3. The predetermined information acquisition module 2037 accepts input of supplementary information regarding farming data such as sowing, fertilizer, and pesticides for field FD1, FD2, and FD3 of the farmland image data IM4 as shown in Figure 5. The GHG emission calculation module 2036 estimates and calculates the GHG emission amount for each field FD1, FD2, and FD3. Then, the GHG emission calculation module 2036 associates the estimated GHG emission results for image data IM5, which is similar to the image data IM4, with field FD1, FD2, and FD3.

[0077] Furthermore, the GHG emission calculation module 2036 stores data indicating GHG emissions, for example, in the field database 2022.

[0078] The predetermined information acquisition module 2037 controls the process of acquiring predetermined information for providing information about farmland, based on the estimation results of the farming status of farmland by the farming status estimation module 2035. The predetermined information acquisition module 2037 acquires various predetermined information about farmland, for example, via the communication unit 201, from user input from the terminal device 10, various information stored in the storage unit 202, or from an external server 30.

[0079] In a given scenario, the predetermined information acquisition module 2037 may acquire information showing the time-series changes in GHG emissions calculated by the GHG emission calculation module 2036. The scan data acquisition module 2033, for example, acquires information on the date and time the scan was performed along with the scan data and stores it in the scan database 2021. In subsequent processing, the GHG emission calculation module 2036 calculates GHG emissions based on the estimated results of the farming conditions of the farmland, and can acquire GHG emissions linked to the date and time information of the scan data for the farmland (field). Therefore, the predetermined information acquisition module 2037 acquires the GHG emissions for each date and time the scan was performed as information showing its changes.

[0080] In certain situations, the predetermined information acquisition module 2037 may acquire information indicating suggestions for improving farming practices on agricultural land. For example, a trained model that outputs the above-mentioned GHG emissions may be configured to also output suggestions for improving farming practices on agricultural land, and the predetermined information acquisition module 2037 may acquire the suggestions for improving farming practices on agricultural land output by the trained model. Suggestions for improving farming practices on agricultural land include suggestions for improving farming data such as sowing / transplanting, farming methods, fertilizer application, pesticide application, and harvesting, and include suggestions for reducing GHG emissions. In this case, the predetermined information acquisition module 2037 may perform a simulation based on the suggestions for improving farming practices on agricultural land, using information indicating the time-series changes in GHG emissions, and acquire the calculation results of GHG emissions from the simulation. The simulation may also be calculated using the trained model.

[0081] In certain situations, the predetermined information acquisition module 2037 may acquire information indicating suggestions for improving farmland suitable for procuring a predetermined crop. For example, when a food company procures agricultural products to be used as raw materials for food, it is conceivable that the supplier of agricultural products may be selected or changed depending on the amount of GHG emissions from the farmland where the crops are cultivated. The predetermined information acquisition module 2037 may, for example, acquire suggestions for improving farmland in order to guide the user to choose farmland with lower GHG emissions as the supplier of agricultural products. The predetermined information acquisition module 2037 may, for example, maintain data showing GHG emissions from farmland in various regions for each agricultural product, and select farmland with lower GHG emissions as a suggestion for improving farmland suitable for procuring the crop, or it may acquire information indicating suggestions for improving farmland by referring to procurement costs other than GHG emissions. In this case, the predetermined information acquisition module 2037 may, for example, use various pre-trained models. A farmland improvement plan is, for example, a plan that guides farmers to source specific crops from designated areas (specific settlements, municipalities, the national government, etc.).

[0082] Furthermore, in certain situations, the predetermined information acquisition module 2037 may acquire supplementary information about farmland and fields. The predetermined information acquisition module 2037 may, for example, accept input from a terminal device 10 by producers or farmland managers as supplementary information about farmland and fields, including meteorological data such as temperature, precipitation, solar radiation, and ground surface temperature, as well as farming data such as sowing / transplanting, farming methods, fertilizer application, pesticide application, and harvesting. The predetermined information acquisition module 2037 may also acquire meteorological data such as temperature, precipitation, solar radiation, and ground surface temperature as supplementary information about farmland and fields from an external server 30, for example.

[0083] The output module 2038 controls the process of outputting the estimated farming status of the farmland by the farming status estimation module 2035, the GHG emission information calculated by the GHG emission calculation module 2036, and predetermined information acquired by the predetermined information acquisition module 2037. The output module 2038 transmits, for example, the above various information to the user's terminal device 10 via the communication unit 201 for display. This information may be output on a farmland basis or on a field basis.

[0084] In certain situations, the output module 2038 may output information showing the time-series changes in GHG emissions acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may graph the time-series changes in GHG emissions and display it on the terminal device 10. In this case, the output module 2038 may output the information showing the time-series changes in GHG emissions on a farmland basis, on a field basis, on a predetermined region (a specific settlement, municipality, country, etc.), or on a producer or manager basis.

[0085] In certain situations, the output module 2038 may output information indicating proposed improvements to farming practices for agricultural land acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may link the information indicating proposed improvements to farming practices for agricultural land to a map of agricultural land (fields) as shown in Figures 4 and 5 and display it on the terminal device 10. At this time, the output module 2038 may output the calculation results of GHG emissions based on simulations.

[0086] In certain situations, the output module 2038 may output information indicating proposed improvements to farmland acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may display information indicating proposed improvements to farmland suitable for procuring a predetermined crop (for example, information guiding the procurement of crops from farmland with lower GHG emissions) on the terminal device 10.

[0087] Furthermore, in certain situations, the output module 2038 may output supplementary information about farmland and fields acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may link the supplementary information about farmland and fields to a map of farmland (fields) as shown in Figures 4 and 5 and display it on the terminal device 10.

[0088] <2. Data Structure> Figure 6 shows an example of the data structure of the scan database 2021 in Figure 3. Figure 7 shows an example of the data structure of the field database 2022 in Figure 3.

[0089] As shown in Figure 6, each record in the scan database 2021 includes the fields "Scan Data ID", "Scan Location (Latitude and Longitude)", "Scan Date and Time", "Data Size", "Scan Data Storage Location", etc.

[0090] The item "Scan Data ID" is information that identifies each scan data obtained by scanning farmland, and is stored in the farm management system 1.

[0091] The item "Scan Location (Latitude and Longitude)" is information indicating the location where the farmland was scanned, based on the scan data stored in the farm management system 1. In the example shown in Figure 6, it is shown as data indicating the latitude and longitude of the scan location, but it may also be address data, data indicating a specific landmark (such as a river or a transmission tower), or any other data that can identify a location.

[0092] The item "Scan Date and Time" is data indicating the date and time when the farmland was scanned, based on the scan data stored in the farm management system 1.

[0093] The item "Data Size" refers to data indicating the actual size of the farmland, the scale ratio, etc., for the scan data stored in the farm management system 1.

[0094] The item "Scan Data Storage Location" is data for a link that indicates the storage location of the scan data stored in the farm management system 1. The item "Scan Data Storage Location" may store a link that indicates the storage location of the scan data, as shown in Figure 6, or it may store the scan data itself.

[0095] The scan data acquisition module 2033 of server 20 adds a record to the scan database 2021 as it acquires scan data.

[0096] As shown in Figure 7, each record in the field database 2022 includes the item "Scan Data ID" and the item "Field Information," etc.

[0097] The item "Scan Data ID" is information that identifies each scan data obtained by scanning farmland and is stored in the farm management system 1, and corresponds to the item "Scan Data ID" in the scan database 2021.

[0098] The item "Field Information" is information about fields that have been divided into predetermined areas by analyzing scan data indicating farmland stored in the farm management system 1. Specifically, it includes items such as "Field ID," "Field Location," "Farming Status," "Scan Date and Time," "GHG Emissions," and "Field Improvement Data."

[0099] The item "Field ID" is information that identifies each field, which is divided into predetermined areas, by analyzing scan data indicating farmland stored in the farm management system 1.

[0100] The item "Field Location" is information indicating the location of fields divided into predetermined areas by analyzing scan data representing farmland stored in the farm management system 1. As shown in Figure 7, the item "Field Location" stores data that indicates which field in the farmland image data it represents, as image data (in the colors of the clay), but any data can be stored as long as it is in a manner that allows the location of the field in the farmland scan data to be identified.

[0101] The item "Farming Status" is data indicating the farming status of a field, stored in the farming management system 1. The "Farming Status" item includes data such as the crops in the field, soil conditions (e.g., nitrogen and carbon content in the soil), and fallow land.

[0102] The "Scan Date and Time" field is data indicating the date and time the farmland was scanned, for the scan data that forms the basis of the field data stored in the farm management system 1, and corresponds to the "Scan Date and Time" field in the scan database 2021.

[0103] The item "GHG emissions" is data showing the amount of GHG emissions from the field, stored in the farm management system 1.

[0104] The item "Field Improvement Data" is data that shows suggestions for improving farming practices for fields stored in the farm management system 1.

[0105] The field division module 2034 of server 20 divides the scanned data into fields and adds a record to the "Field Information" item in the field database 2022. The farming status estimation module 2035 estimates the farming status for each field and stores the estimation result in the "Farming Status" item of the field database 2022. The GHG emission calculation module 2036 calculates the GHG emissions for each field and stores the calculation result in the "GHG Emissions" item of the field database 2022. In addition, the predetermined information acquisition module 2037 acquires information indicating proposed improvements to farming practices for each field and stores the data in the "Field Improvement Data" item of the field database 2022.

[0106] <3 Operation> The farm information output process by the farm management system 1 in the first embodiment will be described below with reference to Figure 8.

[0107] Figure 8 is a flowchart showing an example of the flow of agricultural information output processing by the agricultural management system 1.

[0108] In step S101, the scan data acquisition module 2033 of the server 20 acquires scan data obtained by scanning the farmland. In step S101, for example, the server accesses the external server 30 via the communication unit 201 and acquires the scan data held by the external server 30. In step S101, the acquired scan data is stored in, for example, the scan database 2021.

[0109] In step S102, the field division module 2034 of the server 20 analyzes the scan data acquired in step S101 and divides the scan data representing farmland into fields that are separated into predetermined regions. In step S102, for example, the scan data is analyzed using a color analysis method for existing images and the farmland is divided into separate regions (polygons). Also in step S102, the image data of the scanned data divided into fields is stored in the field database 2022, for example.

[0110] In step S103, the farming status estimation module 2035 of the server 20 analyzes the scan data acquired in step S101 or the image data divided by field in step S102 to estimate the farming status of the farmland. In step S103, for example, using methods such as image analysis, the farming status is estimated to include crops, soil conditions (such as nitrogen and carbon content in the soil), fallow land, etc. Also in step S103, the data showing the estimation results of the farming status is stored in, for example, the field database 2022.

[0111] In step S104, the GHG emission calculation module 2036 of the server 20 estimates and calculates the amount of GHG (Green House Gas) emitted from farmland based on the estimated results of the farming conditions of the farmland estimated in step S103. In step S104, the amount of GHG emissions, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorocarbon gases, is calculated. In step S104, the data showing the GHG emissions is stored, for example, in the field database 2022.

[0112] In step S105, the predetermined information acquisition module 2037 of the server 20 acquires predetermined information for providing information about farmland based on the estimated results of the farming conditions of the farmland estimated in step S103. In step S105, predetermined various information about farmland is acquired, for example, via the communication unit 201, from user input from the terminal device 10, various information stored in the storage unit 202, or from an external server 30.

[0113] In step S106, the output module 2038 of the server 20 outputs the estimated results of the farming conditions of the farmland estimated in step S103, the GHG emission information calculated in step S104, and predetermined information acquired in step S105. In step S106, for example, the above various pieces of information are transmitted to the user's terminal device 10 via the communication unit 201 for presentation.

[0114] As described above, the farm management system 1 analyzes scan data obtained by scanning farmland, for example, image data of farmland taken from a satellite, divides the farmland into fields that are separated into predetermined areas, estimates the farming conditions for each field, and estimates and calculates the amount of GHG emissions from the farmland. Furthermore, based on the estimation results of the farming conditions of the farmland, it acquires predetermined information for providing information about the farmland, and transmits the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information to the terminal device 10 for output.

[0115] <4. Screen Examples> Below, we will explain examples of screens for farm improvement information provided by the farm management system 1, referring to Figure 9.

[0116] Figure 9 shows an example of a screen displaying farming improvement information on the terminal device 10. The example screen in Figure 9 shows a screen displaying farming improvement proposals for farmland, as an example of providing information about farmland. This corresponds to step S106 in Figure 8.

[0117] As shown in Figure 9, the display screen 150 of the terminal device 10 shows a display screen 1031a of farming improvement information for farmland (fields). This display screen 1031a shows image data 1031b of farmland generated from scan data indicating farmland. The image data 1031b of farmland is divided by field, and fields 1031c, 1031d, and 1031e are displayed. Fields 1031c, 1031d, and 1031e also show the estimated farming results by the farming status estimation module 2035 and the proposed farming improvements by the predetermined information acquisition module 2037. Users, such as producers or managers of the farmland, can understand the proposed farming improvements by referring to the display screen 1031a of farming improvement information.

[0118] <Summary> As described above, according to this embodiment, scan data obtained by scanning farmland, for example, image data of farmland taken from a satellite, is analyzed to divide the farmland into fields that are separated into predetermined areas, the farming conditions are estimated for each field, and the amount of GHG emissions from the farmland is estimated and calculated. Furthermore, based on the estimation results of the farming conditions of the farmland, predetermined information for providing information about the farmland is acquired, and the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information are output. Therefore, by using the farming management system according to this disclosure, it is possible to accurately calculate GHG emissions and provide information about the farming conditions of farmland. This makes it possible to calculate and manage GHG emissions in response to social demands, legal obligations, etc.

[0119] Furthermore, as information regarding the farming conditions of agricultural land, data showing the time-series changes in GHG emissions will be output. This will make it possible to understand the time-series changes in GHG emissions, specifically increases and decreases in GHG emissions, in comparison with the farming conditions.

[0120] Furthermore, the system provides information on the current state of farming practices, outputting data that suggests improvements to farming practices. This allows users, such as producers or managers of the farmland in question, to understand potential improvements to their farming practices.

[0121] While several embodiments of this disclosure have been described above, these embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications are permitted without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0122] <Note> The matters described in each of the above embodiments are noted below.

[0123] (Note 1) An information processing device comprising a control unit 203 and a memory 25 (storage unit 202), for managing GHG (Green House Gas) emissions from farmland, wherein the control unit 203 performs the following steps: acquiring scan data obtained by scanning farmland (S101); analyzing the scan data to estimate the farming conditions of the farmland (S103); estimating and calculating GHG emissions from farmland based on the estimation results of the farming conditions of the farmland (S104); acquiring predetermined information for providing information about farmland based on the estimation results of the farming conditions of the farmland (S105); and outputting the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information (S106).

[0124] (Note 2) The information processing device described in (Note 1), wherein the control unit further analyzes the scan data and performs the step (S102) of dividing the scan data indicating farmland into fields that are separated into predetermined areas.

[0125] (Note 3) An information processing device as described in (Note 2), which, in the step of outputting the estimated results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information, outputs the estimated results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information for each field.

[0126] (Note 4) The information processing device described in (Note 3), wherein in the step of acquiring predetermined information for providing information on farmland, it acquires information showing the time-series changes in the calculated GHG emissions, and in the step of outputting the acquired predetermined information, it outputs information showing the time-series changes in the calculated GHG emissions.

[0127] (Note 5) The information processing device described in (Note 4), which, in the step of outputting the acquired predetermined information, outputs information showing the time-series change in the calculated GHG emissions for each field.

[0128] (Appendix 6) An information processing device according to any one of (Appendix 3) to (Appendix 5), wherein in the step of acquiring predetermined information for providing information on farmland, it acquires information indicating a plan for improving farming practices on farmland, and in the step of outputting the acquired predetermined information, it outputs information indicating a plan for improving farming practices on farmland.

[0129] (Appendix 7) The information processing device described in (Appendix 6), which, in the step of outputting the acquired predetermined information, outputs information indicating a plan for improving farming practices for each field.

[0130] (Note 8) An information processing device according to any one of (Note 1) to (Note 7), wherein in the step of acquiring predetermined information for providing information on farmland, it acquires information indicating a plan for improving farmland suitable for procuring a predetermined crop, and in the step of outputting the acquired predetermined information, it outputs information indicating a plan for improving farmland.

[0131] (Note 9) An information processing device according to any one of (Note 1) to (Note 8), wherein in the step of acquiring predetermined information for providing information on farmland, it accepts and acquires input of supplementary information on farmland, and in the step of outputting the acquired predetermined information, it outputs the accepted supplementary information on farmland.

[0132] (Note 10) An information processing device as described in (Note 9), which, in the step of estimating and calculating the amount of GHG emissions from agricultural land, estimates and calculates the amount of GHG emissions from agricultural land based on the estimated results of the farming conditions of the agricultural land and supplementary information about the agricultural land received.

[0133] (Note 11) An information processing device according to any one of (Note 2) to (Note 10), wherein in the step of estimating and calculating the amount of GHG emissions in agricultural land, the device estimates and calculates the amount of GHG emissions in agricultural land for each field, and in the step of outputting information on the amount of GHG emissions in agricultural land, the device outputs the calculated information on the amount of GHG emissions for each field.

[0134] (Note 12) A method for managing GHG (Green House Gas) emissions on farmland, which is executed by a computer comprising a processor 29 and a memory 25, wherein the method is a method in which the processor 29 performs the steps of: acquiring scan data obtained by scanning farmland (S101); analyzing the scan data to estimate the farming conditions of the farmland (S103); estimating and calculating GHG emissions on farmland based on the estimation results of the farming conditions of the farmland (S104); acquiring predetermined information for providing information on farmland based on the estimation results of the farming conditions of the farmland (S105); and outputting the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information (S106).

[0135] 1: Farm management system 10: Terminal device 10A: Terminal device 10B: Terminal device 13: Input device 14: Output device 15: Memory 16: Storage unit 19: Processor 20: Server 25: Memory 26: Storage 29: Processor 30: External server 80: Network 81: Wireless base station 82: Wireless LAN router 111: Antenna 112: Antenna 121: First wireless communication unit 122: Second wireless communication unit 130: Operation reception unit 131: Keyboard 132: Mouse 140: Voice processing unit 141: Microphone 142: Speaker 150: Display 160: Storage unit 161: User information 170: Control unit 171: Input operation reception unit 172: Transmit / receive unit 173: Data Processing Unit 174: Notification Control Unit 201: Communication Unit 202: Storage Unit 203: Control Unit 2021: Scan Database 2022: Field Database 2031: Reception Control Module 2032: Transmission Control Module 2033: Scan Data Acquisition Module 2034: Field Division Module 2035: Farming Status Estimation Module 2036: GHG Emission Calculation Module 2037: Predetermined Information Acquisition Module 2038: Output Module

Claims

1. An information processing device comprising a control unit and a memory, for managing GHG (Green House Gas) emissions from farmland, wherein the control unit performs the steps of: acquiring scan data obtained by scanning the farmland; analyzing the scan data to estimate the farming conditions of the farmland; estimating and calculating GHG emissions from the farmland based on the estimation results of the farming conditions of the farmland; acquiring predetermined information for providing information about the farmland based on the estimation results of the farming conditions of the farmland; and outputting the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information.

2. The information processing apparatus according to claim 1, wherein the control unit further performs the step of analyzing the scan data and dividing the scan data representing the farmland into fields separated by predetermined regions.

3. The information processing device according to claim 2, wherein in the step of outputting the estimated results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information, the estimated results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information are output for each field.

4. The information processing apparatus according to claim 3, wherein in the step of acquiring predetermined information for providing information on the farmland, it acquires information showing the time-series changes in the calculated GHG emissions, and in the step of outputting the acquired predetermined information, it outputs information showing the time-series changes in the calculated GHG emissions.

5. The information processing apparatus according to claim 4, wherein, in the step of outputting the predetermined information obtained, information showing the time-series change of the calculated GHG emissions for each field is output.

6. An information processing device according to any one of claims 3 to 5, wherein in the step of acquiring predetermined information for providing information about the farmland, information indicating a plan for improving farming practices on the farmland is acquired, and in the step of outputting the acquired predetermined information, information indicating a plan for improving farming practices on the farmland is output.

7. The information processing device according to claim 6, wherein, in the step of outputting the predetermined information obtained, information indicating a plan for improving farming practices for each field is output.

8. An information processing device according to any one of claims 1 to 7, wherein in the step of acquiring predetermined information for providing information on the farmland, information indicating a plan for improving the farmland suitable for procuring a predetermined crop is acquired, and in the step of outputting the acquired predetermined information, information indicating a plan for improving the farmland is output.

9. An information processing device according to any one of claims 1 to 8, wherein in the step of acquiring predetermined information for providing information about the farmland, the device accepts and acquires input of supplementary information about the farmland, and in the step of outputting the acquired predetermined information, the device outputs the received supplementary information about the farmland.

10. The information processing device according to claim 9, wherein, in the step of estimating and calculating the amount of GHG emissions from the farmland, the device estimates and calculates the amount of GHG emissions from the farmland based on the estimated results of the farming conditions of the farmland and the supplementary information about the farmland received.

11. An information processing device according to any one of claims 2 to 10, wherein in the step of estimating and calculating the amount of GHG emissions in the farmland, the device estimates and calculates the amount of GHG emissions in the farmland for each field, and in the step of outputting information on the amount of GHG emissions in the farmland, the device outputs the calculated information on the amount of GHG emissions for each field.

12. A method for managing GHG (Green House Gas) emissions on farmland, which is executed by a computer comprising a processor and memory, wherein the method is performed by the processor: acquiring scan data obtained by scanning the farmland; analyzing the scan data to estimate the farming conditions of the farmland; estimating and calculating GHG emissions on the farmland based on the estimation results of the farming conditions of the farmland; acquiring predetermined information for providing information on the farmland based on the estimation results of the farming conditions of the farmland; and outputting the estimation results of the farming conditions of the farmland, the calculated GHG emission information, and the acquired predetermined information.

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