Program, information processing device, and method

Abstract

We provide technology that enables accurate calculation of GHG emissions and provides information on the farming status of agricultural land. [Solution] The server 20 of the farming management system 1 has the following functions: a step (S101) of acquiring scan data obtained by scanning farmland; a step (S103) of analyzing the scan data to estimate the farming status of the farmland; a step (S104) of estimating and calculating the amount of GHG (Green House Gas) emissions in the farmland based on the estimated results of the farming status of the farmland; a step (S105) of acquiring specified information for providing information about the farmland based on the estimated results of the farming status of the farmland; and a step (S106) of outputting the estimated results of the farming status of the farmland, the calculated GHG emission information, and the acquired specified information.

Description

[Technical Field]

[0001] The present disclosure relates to a program, an information processing device, and a method. [Background technology]

[0002] In recent years, growing awareness of environmental protection has led to efforts to reduce greenhouse gas (GHG) emissions. GHGs include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and chlorofluorocarbons.

[0003] The same efforts to reduce GHG emissions are being made in the agricultural sector, and as a result, farming methods that produce less GHG emissions and have a lower environmental impact are being recommended.

[0004] Patent Document 1 discloses a system for estimating methane gas emissions from agricultural fields, a type of GHG. The methane emission estimation system described in Patent Document 1 acquires observation data of paddy fields from a satellite and estimates the waterlogging status of the paddy fields using a trained model to estimate the amount of methane gas emissions from the paddy fields. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2023-174067 Summary of the Invention [Problem to be solved by the invention]

[0006] As mentioned above, due to social demands, legal obligations, and the like, various industries are being required to calculate and manage GHG emissions in order to reduce their GHG emissions, and GHG emissions are being calculated using a system such as that described in Patent Document 1. The Ministry of the Environment is also considering calculating GHG emissions from agricultural soil. Furthermore, food companies and other businesses that purchase harvested agricultural products are also being required to calculate and manage GHG emissions.

[0007] However, in actual agricultural fields, GHG emissions vary depending on the farming conditions of the farmland, such as the amount of pesticides used, making it difficult to accurately calculate GHG emissions. Furthermore, even if GHG emissions could be calculated, it was not easy to obtain information on methods for improving them.

[0008] Therefore, this disclosure describes a technology that enables accurate calculation of GHG emissions and provides information on the farming status of farmland. [Means for solving the problem]

[0009] According to one embodiment of the present disclosure, there is provided a program for managing GHG (Green House Gas) emissions in agricultural land when executed by a computer having a processor and a memory, the program causing the processor to execute the following steps: acquiring scan data obtained by scanning the agricultural land; analyzing the scan data to estimate the farming status of the agricultural land; estimating and calculating GHG emissions in the agricultural land based on the estimated results of the farming status of the agricultural land; acquiring specified information for providing information about the agricultural land based on the estimated results of the farming status of the agricultural land; and outputting the estimated results of the farming status of the agricultural land, information on the calculated GHG emissions, and the acquired specified information. [Effects of the Invention]

[0010] According to the present disclosure, the system analyzes the scan data obtained by scanning farmland, estimates the farming status of the farmland, and estimates and calculates GHG emissions from the farmland. Furthermore, based on the estimated results of the farming status of the farmland, it acquires predetermined information for providing information about the farmland, and outputs the estimated results of the farming status of the farmland, the calculated GHG emissions information, and the acquired predetermined information. This makes it possible to accurately calculate GHG emissions and provide information about the farming status of the farmland. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram showing the overall configuration of a farm management system 1. FIG. [Figure 2] 2 is a block diagram showing a functional configuration of the terminal device 10 of FIG. 1. FIG. [Figure 3] FIG. 2 is a diagram showing a functional configuration of a server 20 in FIG. [Figure 4] FIG. 4 is a schematic diagram illustrating the function of the server 20 of FIG. 3. [Figure 5] FIG. 4 is a schematic diagram illustrating the function of the server 20 of FIG. 3. [Figure 6] FIG. 4 is a diagram illustrating an example of the data structure of a scan database 2021 in FIG. 3. [Figure 7] FIG. 4 is a diagram showing an example of the data structure of a farm field database 2022 in FIG. 3. [Figure 8] 10 is a flowchart showing an example of the flow of farming information output processing by the farming management system 1. [Figure 9] FIG. 10 is a diagram showing an example of a screen of farming improvement information displayed on the terminal device 10. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated description will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

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

[0014] Furthermore, the at least one processor may be a processor in the broad sense, such as a hardware circuit (for example, a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) that performs part or all of the processing.

[0015] In the following description, we may refer to information that produces an output in response to an input, but this information may be data of any structure, or may be a learning model such as a neural network that generates an output in response to an input.

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

[0017] In addition, in the following explanation, processing may be described using the "program" as the subject, but since a program is executed by a processor to perform specified processing while appropriately using a memory unit and / or an interface unit, etc., the subject of the processing may also be the processor (or a device such as a controller that has that processor).

[0018] The program may be installed in a device such as a computer, or may be stored in, for example, a program distribution server or a computer-readable (e.g., non-transitory) recording medium. Also, in the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

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

[0020] In addition, in the following description, when describing elements of the same type without distinguishing between them, reference symbols (or common symbols among the reference symbols) may be used, and when describing elements of the same type with distinction between them, the identification numbers (or reference symbols) of the elements may be used.

[0021] In the following description, the control lines and information lines are those that are considered necessary for the description, and do not necessarily represent all the control lines and information lines in the product. All components may be interconnected.

[0022] <Summary> The following describes a farm management system according to the present disclosure. The farm management system according to the present disclosure is, for example, 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, photographic data of the farmland taken from a satellite, to estimate the farming status of the farmland and estimate and calculate GHG emissions from the farmland. Based on the estimated results of the farming status of the farmland, the system acquires specified information for providing information about the farmland, and outputs the estimated results of the farming status of the farmland, the calculated GHG emissions, and the acquired specified information. The farm management system according to the present disclosure is a system provided as a web service, for example, via a cloud server or the like, via so-called SaaS (Software as a Service), and is configured to be accessible by users with specified authentication.

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

[0024] As mentioned above, due to social demands and legal obligations, various industries are being required to calculate and manage 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 crops are also being required to calculate and manage GHG emissions. However, in actual agricultural fields, GHG emissions vary depending on the farming conditions of the farmland, for example, the amount of pesticides used, making it difficult to accurately calculate GHG emissions. Furthermore, even if GHG emissions could be calculated, it was not easy to obtain information on methods for improving them.

[0025] Therefore, the farming management system according to the present disclosure is configured to analyze scan data obtained by scanning farmland, for example, photographic data of the farmland taken from a satellite, estimate the farming status of the farmland, and estimate and calculate GHG emissions from the farmland. Furthermore, the farming management system according to the present disclosure is configured to acquire predetermined information for providing information about the farmland based on the estimated farming status of the farmland, and output the estimated farming status of the farmland, information on the calculated GHG emissions, and the acquired predetermined information.

[0026] With this configuration, by using the farm management system according to the present disclosure, it is possible to accurately calculate GHG emissions and provide information on the farm management status of farmland, which will contribute to calculating and managing GHG emissions in accordance with social demands, legal obligations, etc.

[0027] First Embodiment The following describes the farm management system 1. In the following description, for example, when the terminal device 10 accesses the server 20, the server 20 responds with information for generating a screen on the terminal device 10. 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> FIG. 1 is a diagram showing the overall configuration of a farm management system 1. As shown in FIG. 1, the farm management system 1 includes multiple terminal devices (terminal device 10A and terminal device 10B are shown in FIG. 1; 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 via a network 80 so that they can communicate with each other. The network 80 may be a wired or wireless network. Examples of the network 80 include 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, the network 80 may use communication protocols such as Z-Wave (registered trademark), ZigBee (registered trademark), and Bluetooth (registered trademark). In addition, in the case of a wired connection, the network may also include a network that is directly connected using a USB (Universal Serial Bus) cable or the like.

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

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

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

[0032] Server 20 acquires and analyzes scan data obtained by scanning farmland, for example, photographic data of the farmland taken from a satellite, estimates the farming status of the farmland, and estimates and calculates the GHG emissions from the farmland. Based on the estimated farming status of the farmland, server 20 acquires specified information for providing information about the farmland, and outputs the estimated farming status of the farmland, information on the calculated GHG emissions, and the acquired specified information.

[0033] The server 20 is a computer connected to a network 80. The server 20 can be realized virtually by distributing all or part of its hardware components across multiple computers and interconnecting them via a network. In this way, the concept of the server 20 includes not only a computer housed in a single housing or case, but also a virtualized computer system. The server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29.

[0034] The communication IF 22 is an interface for inputting and outputting signals so that the server 20 can communicate with external devices. The input / output IF 23 functions as an interface with an input device for receiving input operations from a user and an output device for presenting information to the user. The memory 25 is for temporarily storing programs and data processed by the programs, etc., and is a volatile memory such as a DRAM (Dynamic Random Access Memory). The storage 26 is a storage device for saving data, such as a flash memory or an HDD (Hard Disc Drive). The processor 29 is hardware for executing an instruction set written in a 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 related to farmland, and includes a device that acquires information about the soil, weather, and topography of the farmland field from ground sensors and acquires field information for each field from artificial satellites in Earth's orbit. The external server 30 may also include a device that provides weather data for various locations. Note that this function may be configured to be included in the server 20, and the external server 30 may not be included. However, the farm management system 1 according to the present disclosure will be described as being configured to include the external server 30.

[0036] <1.1 Configuration of the terminal device 10> 2 is a block diagram showing the functional configuration of the terminal device 10 of FIG. 1. As shown in FIG. 2, the terminal device 10 includes a plurality of antennas (antenna 111, antenna 112), wireless communication units (first wireless communication unit 121, second wireless communication unit 122) corresponding to the respective antennas, 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 (e.g., a battery for storing power, a power supply circuit for controlling the supply of power from the battery to each circuit, etc.) that are not specifically shown in FIG. 2. As shown in FIG. 2, the blocks included in the terminal device 10 are electrically connected by a bus or the like.

[0037] The antenna 111 emits a signal emitted by the terminal device 10 as a radio wave. The antenna 111 also receives a radio wave from space and provides the received signal to the first radio communication unit 121.

[0038] The antenna 112 emits a signal emitted by the terminal device 10 as a radio wave. The antenna 112 also receives a radio wave from space and provides the received signal to the second radio communication unit 122.

[0039] The first wireless communication unit 121 performs modulation / demodulation processing and the like for transmitting and receiving signals via the antenna 111 so that the terminal device 10 can communicate with other wireless devices. The second wireless communication unit 122 performs modulation / demodulation processing and the like for transmitting and receiving signals via the antenna 112 so that the terminal device 10 can communicate with other wireless devices. The first wireless communication unit 121 and the second wireless communication unit 122 are communication modules including 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 wireless signals transmitted and received by the terminal device 10, and provide the received signals to the control unit 170.

[0040] The operation reception unit 130 has a mechanism for receiving input operations from the user. 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 position of the user's contact on the touch panel, for example, by using a capacitive touch panel.

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

[0042] The mouse 132 accepts input operations by the user of the terminal device 10. The mouse 132 is a pointing device for selecting an object displayed on the display 150, and outputs position information of the selected object on the screen and information indicating that a button has been pressed as input signals to the control unit 170.

[0043] The audio processing unit 140 modulates and demodulates audio signals. The audio processing unit 140 modulates a signal provided from the microphone 141 and provides the modulated signal to the control unit 170. The audio processing unit 140 also provides the audio signal to the speaker 142. The audio processing unit 140 is realized, for example, by a processor for audio processing. The microphone 141 accepts audio input and provides an audio signal corresponding to the audio input to the audio processing unit 140. The speaker 142 converts the audio signal provided from the audio processing unit 140 into audio and outputs the audio to the outside of the terminal device 10.

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

[0045] Storage unit 160 is configured with memory 15, such as a flash memory, and storage unit 16, and stores data and programs used by terminal device 10. In one aspect, storage unit 160 stores user information 161.

[0046] The user information 161 is information about a user who uses the terminal device 10 to manage the farming status of farmland and manage GHG emissions, which are functions of the farm management system 1. The user information includes information to identify the user (business ID), the user's name or title (company name, etc.), organizational information of the company, etc.

[0047] The control unit 170 is configured by, for example, the processor 19, and controls the operation of the terminal device 10 by reading a program stored in the storage unit 160 and executing instructions included in the program. The control unit 170 is, for example, an application that is pre-installed in the terminal device 10. The control unit 170 operates in accordance with the program to fulfill the functions of 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 receiving unit 171 performs processing to receive input operations by the user via an input device such as the keyboard 131 .

[0049] The transmitting / receiving unit 172 performs processing for the terminal device 10 to transmit and receive data to and from external devices such as the server 20 in accordance with a communication protocol.

[0050] The data processing unit 173 performs a process of performing calculations on data that the terminal device 10 has received as input in accordance with a program, and outputs the calculation results to a memory or the like.

[0051] The notification control unit 174 performs processing to present information to the user. The notification control unit 174 performs processing to display a display image on the display 150, processing to output sound from the speaker 142, and the like.

[0052] <1.2 Functional configuration of server 20> Fig. 3 is a diagram showing the functional configuration of the server 20 of 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 processing for the server 20 to communicate with external devices.

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

[0055] The scan database 2021 is a database for storing scan data obtained by scanning farmland for managing farming conditions and GHG emissions using the farm management system 1. Details will be described later.

[0056] The farm field database 2022 is a database for storing data relating to farm fields for which the farm management status is managed using the farm management system 1 and GHG emissions are managed, etc. Details will be described later.

[0057] The control unit 203 performs the functions shown in various modules, such as a receiving control module 2031, a transmitting control module 2032, a scan data acquisition module 2033, a field division module 2034, a farming status estimation module 2035, a GHG emission calculation module 2036, a specified information acquisition module 2037, and an output module 2038, by the processor 29 of the server 20 performing processing according to the program.

[0058] The reception control module 2031 controls the process by which the server 20 receives signals from external devices in accordance with a communication protocol.

[0059] The transmission control module 2032 controls the process in which the server 20 transmits signals to external devices in accordance with 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 the external server 30 via, for example, the communication unit 201, and acquires scan data stored in the external server 30. The scan data acquired by the scan data acquisition module 2033 is, for example, photography data acquired by an artificial satellite photographing farmland from Earth's orbit, but is not limited to photography data using visible light, and may also be image data obtained by the satellite observing the reflected waves of infrared rays or microwaves irradiated onto 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 of the scan, and data indicating the size (scale) 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 may be address data.

[0062] Furthermore, the scan data acquisition module 2033 stores the acquired scan data in, for example, 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 showing farmland into fields separated by predetermined areas. For example, the field division module 2034 analyzes the scan data and divides the farmland into separate areas (polygons) if the farmland has different colors, or if there are areas such as farm roads that are not considered to be farmland between areas that are considered to be farmland. In this case, the field division module 2034 may use a color analysis method for existing images to determine that an area is a different area (field boundary) if the color difference is equal to or exceeds a predetermined threshold, and divide the farmland into fields.

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

[0065] The field division module 2034 may divide the field into sections not only by analyzing the scan data but also by using field division information provided by a predetermined organization (for example, a national organization).

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

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

[0068] The farming status estimation module 2035 analyzes the scan data and controls the process of estimating the farming status of the farmland. The object of analysis by the farming status estimation module 2035 may be image data divided into fields by the field division module 2034, or may be scan data acquired by the scan data acquisition module 2033 if the field division module 2034 has not divided the field. The farming status estimation module 2035 estimates the farming status of the farmland, for example, by a method such as image analysis. The farming status estimation module 2035 estimates, for example, the crops in the farmland (field), the condition of the soil (such as the amount of nitrogen and carbon in the ground), fallow land, etc.

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

[0070] The learning model used by the farming status estimation module 2035 may also 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 soil of a field. Humus is a black substance (polymer compound) produced by the decay and decomposition of animal and plant remains accumulated in the soil. Humus has a significant impact on the properties and productivity of soil. This service can instantly obtain and display the humus content of each field over a wide area. Nitrogen content refers to the amount of nitrogen contained in the soil of a field and is calculated based on a vegetation index. Vegetation refers to the group of plants growing in a specific area. A vegetation index is an index used to understand the state of vegetation (the amount and vitality of plants). The vegetation index is calculated using the light reflection characteristics of plants. For example, the Normalized Difference Vegetation Index (NDVI) can be used as a vegetation index.

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

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

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

[0074] In one aspect, the GHG emission calculation module 2036 may estimate and calculate GHG emissions from the farmland based on the farming status estimation result of the farmland by the farming status estimation module 2035 and supplementary information about the farmland and field received and acquired by the specified information acquisition module 2037, which will be described later. The specified information acquisition module 2037 receives input of supplementary information about the farmland and field, such as meteorological data on temperature, precipitation, solar radiation, and ground surface temperature, and farming data on sowing / transplanting, farming techniques, fertilizer application, pesticide spraying, and harvesting, from, for example, producers or farmland managers. 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, for example, the estimated results of the farming status of the farmland, the received supplementary information, and GHG emissions, and outputs GHG emissions. This training model may be any training model that has undergone appropriate training, and may include, for example, a training model based on supervised machine learning using predetermined training data, a training model based on unsupervised machine learning, or a training model using a deep neural network (DNN), which is a multi-layer neural network that is the subject of deep learning. Furthermore, this training model does not need to be a single training model, and may be implemented by switching between multiple independent training models for each training data.

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

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

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

[0079] In one aspect, the predetermined information acquisition module 2037 may acquire information indicating 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 when 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 farming status of the farmland, so that the GHG emissions can be acquired by linking them to the date and time information of the scan data of the farmland (field). Therefore, the predetermined information acquisition module 2037 acquires the GHG emissions for each scan date and time as information indicating their changes.

[0080] In one aspect, the predetermined information acquisition module 2037 may acquire information indicating improvement proposals for farmland management. For example, the trained model that outputs the above-mentioned GHG emissions may be configured to also output improvement proposals for farmland management, and the predetermined information acquisition module 2037 may acquire the improvement proposals for farmland management output by the trained model. The improvement proposals for farmland management are, for example, improvement proposals for farming data such as sowing / transplanting, farming techniques, fertilization, pesticide application, and harvesting, and include improvement proposals for reducing GHG emissions. In this case, the predetermined information acquisition module 2037 may perform a simulation of the improvement proposals for farmland management based on the information indicating the time-series changes in the above-mentioned GHG emissions, and acquire the calculation results of the GHG emissions from the simulation. The simulation may also be calculated using the trained model.

[0081] In a certain aspect, the predetermined information acquisition module 2037 may acquire information indicating improvement proposals for farmland suitable for procuring a predetermined crop. For example, when a food company or the like procures agricultural crops as raw materials for food, it is conceivable that the source of the agricultural crops to be procured may be selected or changed depending on the GHG emissions of the farmland on which the procured agricultural crops are grown. The predetermined information acquisition module 2037 may acquire improvement proposals for farmland, for example, to guide a user to select farmland with lower GHG emissions as the source of the agricultural crops. The predetermined information acquisition module 2037 may, for example, store data indicating GHG emissions of farmland in various regions for each agricultural crop, and select farmland with lower GHG emissions as an improvement proposal for farmland suitable for procuring the crop. The predetermined information acquisition module 2037 may also acquire information indicating improvement proposals for farmland by referring to factors other than GHG emissions, such as procurement costs. In this case, the predetermined information acquisition module 2037 may use, for example, various trained models. The farmland improvement plan is, for example, an improvement plan that guides the procurement of a specified crop from a specified area (a specific village, municipality, country, etc.).

[0082] In addition, in a certain aspect, the predetermined information acquisition module 2037 may acquire supplementary information about the farmland or field. For example, the predetermined information acquisition module 2037 may accept input of supplementary information about the farmland or field, such as meteorological data about the farmland or field, such as temperature, precipitation, solar radiation, and ground surface temperature, and farming data about sowing / transplanting, farming techniques, fertilization, pesticide spraying, and harvesting, through operation input from the terminal device 10 by a producer, a farmland manager, or the like. Furthermore, the predetermined information acquisition module 2037 may acquire meteorological data about the farmland or field, such as temperature, precipitation, solar radiation, and ground surface temperature, from the external server 30, for example.

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

[0084] In a certain aspect, the output module 2038 may output information indicating the time-series change in GHG emissions acquired by the predetermined information acquisition module 2037. The output module 2038 may, for example, graph the time-series change in GHG emissions and display it on the terminal device 10. In this case, the output module 2038 may output the information indicating the time-series change in GHG emissions for each farmland, for each farm field, for each predetermined region (such as a specific village, municipality, or country), or for each producer or manager.

[0085] In a certain aspect, the output module 2038 may output information indicating a farming improvement plan for the farmland acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may link the information indicating the farming improvement plan for the farmland to a map of the farmland (field) such as those 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 the GHG emissions by simulation.

[0086] In a certain aspect, the output module 2038 may output information indicating an improvement plan for farmland acquired by the predetermined information acquisition module 2037. The output module 2038 may, for example, cause the terminal device 10 to display information indicating an improvement plan for farmland suitable for procuring predetermined crops (for example, information guiding the procurement of farmland with lower GHG emissions as a source of agricultural crops).

[0087] In addition, in a certain aspect, the output module 2038 may output supplemental information about the farmland or field acquired by the predetermined information acquisition module 2037. For example, the output module 2038 may link the supplemental information about the farmland or field to a map of the farmland (field) as shown in FIGS.

[0088] <2 Data Structure> Fig. 6 is a diagram showing an example of the data structure of the scan database 2021 in Fig. 3. Fig. 7 is a diagram showing an example of the data structure of the farm field database 2022 in Fig. 3.

[0089] As shown in FIG. 6, each record in the scan database 2021 includes an item "scan data ID," an item "scan location (latitude and longitude)," an item "scan date and time," an item "data size," an item "scan data storage location," etc.

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

[0091] The item "Scan location (latitude and longitude)" is information indicating the location where farmland was scanned for the scan data stored in the farm management system 1. In the example shown in Fig. 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 (a river, a steel tower, or other structure), or data that can identify other locations.

[0092] The item "scan date and time" is data indicating the date and time when the farmland was scanned for the scan data stored in the farm management system 1.

[0093] The item "data size" is 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 destination" is link data indicating the storage destination of the scan data stored in the farm management system 1. The item "scan data storage destination" may store a link indicating the storage destination of the scan data as shown in Figure 6, or the scan data itself may be stored.

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

[0096] As shown in FIG. 7, each record in the farm field database 2022 includes an item "scan data ID" and an item "farm field information," etc.

[0097] The item "Scan Data ID" is information that identifies each piece of scan data obtained by scanning farmland and 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 divided into specified areas by analyzing scan data showing farmland stored in the farm management system 1, and specifically includes the items "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 divided into predetermined areas by analyzing scan data showing the farmland stored in the farm management system 1.

[0100] The item "Field location" is information indicating the location of the field divided into predetermined areas by analyzing the scan data showing the farmland stored in the farm management system 1. As shown in Figure 7, the item "Field location" stores data showing which field is shown in the image data of the farmland as image data (using gray color), but any data may be stored as long as it allows the location of the field to be identified in the scan data of the farmland.

[0101] The item "farming status" is data indicating the farming status of a field stored in the farming management system 1. The item "farming status" stores data indicating, for example, the crops in the field, the condition of the soil (such as the amount of nitrogen and carbon in the ground), fallow land, etc.

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

[0103] The item “GHG emission amount” is data stored in the farm management system 1 that indicates the amount of GHG emissions in the farm field.

[0104] The item "field improvement data" is data that indicates farming improvement plans for fields stored in the farming management system 1.

[0105] The field division module 2034 of the server 20 adds a record to the "field information" item in the field database 2022 as the scan data is divided into fields. The farming status estimation module 2035 estimates the farming status of a field and stores data indicating the estimation results in the "farming status" item in the field database 2022. The GHG emission calculation module 2036 calculates GHG emissions for a field and stores data indicating the calculation results in the "GHG emissions" item in the field database 2022. In addition, the specified information acquisition module 2037 acquires information indicating improvement plans for farming operations for the field and stores data in the "field improvement data" item in the field database 2022.

[0106] <3 operations> Hereinafter, the farming information output process performed by the farming management system 1 in the first embodiment will be described with reference to FIG.

[0107] FIG. 8 is a flowchart showing an example of the flow of the farming information output process performed by the farming management system 1.

[0108] In step S101, the scan data acquisition module 2033 of the server 20 acquires scan data obtained by scanning farmland. In step S101, for example, the external server 30 is accessed via the communication unit 201, and the scan data held by the external server 30 is acquired. In addition, 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 showing the farmland into fields separated by predetermined areas. In step S102, the scan data is analyzed using, for example, a color analysis method for existing images, and the farmland is divided into separate areas (polygons). In addition, in step S102, the image data obtained by dividing the scan data into fields is stored, for example, in the field database 2022.

[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 for each field in step S102 to estimate the farming status of the farmland. In step S103, for example, using a method such as image analysis, the farming status is estimated, such as the crops in the farmland (field), the soil condition (such as the amount of nitrogen and carbon in the ground), and fallow land. Also, in step S103, data indicating the estimated farming status is stored, for example, in the farm 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) emissions in the farmland based on the results of the farming status estimation in step S103. In step S104, the amount of GHG emissions including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), chlorofluorocarbons, etc. is calculated. Also, in step S104, data indicating the amount of GHG emissions is stored in, for example, the farmland database 2022.

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

[0113] In step S106, the output module 2038 of the server 20 outputs the estimated results of the farming status of the farmland estimated in step S103, the information on the GHG emissions calculated in step S104, and the predetermined information acquired in step S105. In step S106, for example, the above various information is 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, photographic data of the farmland taken from a satellite, divides the farmland into fields separated by predetermined areas, estimates the farming status of each field, and estimates and calculates the GHG emissions from the farmland. Furthermore, based on the estimated results of the farming status of the farmland, it acquires predetermined information for providing information about the farmland, and transmits the estimated results of the farming status of the farmland, the calculated GHG emissions information, and the acquired predetermined information to the terminal device 10 for output.

[0115] <4 Screen example> An example of a screen display of farm management improvement information by the farm management system 1 will be described below with reference to FIG.

[0116] Fig. 9 is a diagram showing an example of a screen of farming improvement information displayed on the terminal device 10. The screen example of Fig. 9 shows an example of a screen in which a farming improvement plan for the farmland is output as an example of information provision related to the farmland. This corresponds to step S106 in Fig. 8.

[0117] As shown in Fig. 9, a display screen 1031a for farming improvement information for a farmland (field) is displayed on the display 150 of the terminal device 10. This farming improvement information display screen 1031a displays farmland image data 1031b generated from scan data showing the farmland. The farmland image data 1031b is divided into fields, and fields 1031c, 1031d, and 1031e are displayed. In addition, the farming operation estimation results obtained by the farming status estimation module 2035 and farming improvement proposals obtained by the specified information acquisition module 2037 are displayed for the fields 1031c, 1031d, and 1031e. A user, for example, a producer or manager of the farm, can understand the farming improvement proposals by referring to the farming improvement information display screen 1031a.

[0118] <Summary> As described above, according to this embodiment, scan data obtained by scanning farmland, such as photographic data of the farmland taken from a satellite, is analyzed, the farmland is divided into fields separated by predetermined areas, the farming status of each field is estimated, and the GHG emissions from the farmland are estimated and calculated. Furthermore, based on the estimated results of the farming status of the farmland, predetermined information for providing information about the farmland is acquired, and the estimated results of the farming status of the farmland, calculated GHG emissions, and the acquired predetermined information are output. Therefore, by using the farming management system according to the present disclosure, it is possible to accurately calculate GHG emissions and provide information about the farming status of the farmland. This contributes to calculating and managing GHG emissions in accordance with social demands, legal obligations, etc.

[0119] In addition, data showing time-series changes in GHG emissions is output to provide information on the farming status of farmland. This makes it possible to understand time-series changes in GHG emissions, specifically increases and decreases in GHG emissions, by comparing them with the farming status.

[0120] In addition, data showing improvement plans for farming operations on the farmland is output as information on the farming status of the farmland, allowing users, such as producers and managers of the farmland, to understand the improvement plans for farming operations.

[0121] Although several embodiments of the present disclosure have been described above, these embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and modifications are intended to be included in the scope of the inventions and their equivalents as defined in the claims, as well as in the scope and spirit of the inventions.

[0122] <Additional Notes> The matters described in the above embodiments will be supplemented below.

[0123] (Appendix 1) A program for managing GHG (Green House Gas) emissions in agricultural land when executed by a computer having a processor 29 and a memory 25, the program causing the processor 29 to execute the following steps: a step of acquiring scan data obtained by scanning the agricultural land (S101); a step of analyzing the scan data and estimating the farming status of the agricultural land (S103); a step of estimating and calculating GHG emissions in the agricultural land based on the estimated results of the farming status of the agricultural land (S104); a step of acquiring specified information for providing information about the agricultural land based on the estimated results of the farming status of the agricultural land (S105); and a step of outputting the estimated results of the farming status of the agricultural land, information on the calculated GHG emissions, and the acquired specified information (S106).

[0124] (Appendix 2) The program described in (Appendix 1) further executes a step (S102) of analyzing the scan data and dividing the scan data showing agricultural land into fields separated by predetermined areas.

[0125] (Appendix 3) A program described in (Appendix 2), in which, in the step of outputting the estimated results of the farming status of the farmland, the calculated GHG emission amount information, and the acquired specified information, the program outputs, for each field, the estimated results of the farming status of the farmland, the calculated GHG emission amount, and the acquired specified information.

[0126] (Appendix 4) A program described in (Appendix 3), which, in the step of acquiring specified information for providing information regarding agricultural land, acquires information showing the time series changes in the calculated GHG emissions, and, in the step of outputting the acquired specified information, outputs information showing the time series changes in the calculated GHG emissions.

[0127] (Appendix 5) The program according to (Appendix 4), wherein in the step of outputting the acquired predetermined information, information indicating the time-series change in the calculated GHG emissions for each field is output.

[0128] (Appendix 6) A program described in (Appendix 3), in which, in the step of acquiring specified information for providing information about agricultural land, information indicating improvement proposals for agricultural land farming is acquired, and in the step of outputting the acquired specified information, information indicating improvement proposals for agricultural land farming is output.

[0129] (Appendix 7) A program according to (Appendix 6), wherein in the step of outputting the acquired predetermined information, information indicating improvement proposals for farming operations of the farmland is output for each farm field.

[0130] (Appendix 8) A program described in (Appendix 3), which, in a step of acquiring specified information for providing information about agricultural land, accepts and acquires input of supplementary information about agricultural land, and, in a step of outputting the acquired specified information, outputs the received supplementary information about agricultural land.

[0131] (Appendix 9) A program described in (Appendix 8), in which, in the step of estimating and calculating GHG emissions from agricultural land, the GHG emissions from agricultural land are estimated and calculated based on the estimated farming status of the agricultural land and the supplementary information about the agricultural land that has been received.

[0132] (Appendix 10) A program described in (Appendix 9), wherein in the step of estimating and calculating GHG emissions in agricultural land, the GHG emissions in agricultural land are estimated and calculated for each field, and in the step of outputting information on GHG emissions in agricultural land, the calculated information on GHG emissions is output for each field.

[0133] (Appendix 11) An information processing device that is equipped with a control unit 203 and a memory 25 (storage unit 202) and manages GHG (Green House Gas) emissions in agricultural land, wherein the control unit 203 executes the steps of: acquiring scan data obtained by scanning the agricultural land (S101); analyzing the scan data to estimate the agricultural operation status of the agricultural land (S103); estimating and calculating GHG emissions in the agricultural land based on the estimated results of the agricultural operation status of the agricultural land (S104); acquiring specified information for providing information about the agricultural land based on the estimated results of the agricultural operation status of the agricultural land (S105); and outputting the estimated results of the agricultural operation status of the agricultural land, information on the calculated GHG emissions, and the acquired specified information (S106).

[0134] (Appendix 12) A method for managing GHG (Green House Gas) emissions in agricultural land, executed by a computer having a processor 29 and a memory 25, the method comprising the steps of: a step (S101) of the processor 29 scanning the agricultural land to obtain scan data; a step (S103) of analyzing the scan data to estimate the agricultural operation status of the agricultural land; a step (S104) of estimating and calculating GHG emissions in the agricultural land based on the estimated results of the agricultural operation status of the agricultural land; a step (S105) of acquiring specified information for providing information about the agricultural land based on the estimated results of the agricultural operation status of the agricultural land; and a step (S106) of outputting the estimated results of the agricultural operation status of the agricultural land, the calculated GHG emission information, and the acquired specified information. [Explanation of symbols]

[0135] 1: Farm management system 10: Terminal device 10A: Terminal equipment 10B: Terminal device 13: Input device 14: Output device 15: Memory 16: Storage section 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: Audio processing unit 141:Mike 142: Speaker 150: Display 160: Storage section 161: User information 170: Control unit 171: Input operation reception unit 172: Transmitter / receiver 173: Data processing section 174: Notification control section 201: Communications Department 202: Storage section 203: Control unit 2021: Scan Database 2022: Field Database 2031: Receiving control module 2032: Transmission control module 2033: Scan data acquisition module 2034: Field division module 2035: Farming status estimation module 2036:GHG Emissions Calculation Module 2037: Prescribed information acquisition module 2038: Output module

Claims

1. A program for managing GHG (Green House Gas) emissions in agricultural land when executed by a computer having a processor and a memory, The program causes the processor to: acquiring scan data obtained by scanning the farmland; analyzing the scan data to estimate the farming status of the farmland; a step of estimating and calculating the amount of GHG emissions from the farmland based on the estimated farming status of the farmland; a step of acquiring predetermined information for providing information about the farmland based on the estimated farming status of the farmland; outputting the estimated farming status of the farmland, the calculated GHG emission amount information, and the acquired predetermined information; A program that, in the step of estimating the farming status of the farmland, estimates the farming status of the farmland, including the crops on the farmland and whether the farmland is fallow.

2. The program further comprises: executes a step of receiving and acquiring supplementary information about the farmland, including weather data for the farmland and farming data for the farmland; In the step of estimating and calculating the amount of GHG emissions from the agricultural land, the amount of GHG emissions from the agricultural land is estimated and calculated based on the estimated farming status of the agricultural land and the received supplementary information about the agricultural land; The program according to claim 1 , wherein the output step outputs supplemental information about the accepted farmland.

3. The program further comprises:

3. The program according to claim 1, further comprising: a step of analyzing the scan data and dividing the scan data representing the farmland into fields separated by predetermined areas.

4. A program as described in claim 3, wherein in the step of outputting the estimated results of the farming status of the farmland, the calculated GHG emission amount information, and the acquired specified information, the estimated results of the farming status of the farmland, the calculated GHG emission amount, and the acquired specified information are output for each field.

5. In the step of acquiring predetermined information for providing information about the farmland, information indicating a time-series change in the calculated GHG emissions is acquired; 5. The program according to claim 4, wherein in the step of outputting the acquired predetermined information, information indicating a time-series change in the calculated GHG emission amount is output.

6. The program according to claim 5 , wherein in the step of outputting the acquired predetermined information, information indicating a time-series change in the calculated GHG emission amount for each of the fields is output.

7. In the step of acquiring predetermined information for providing information about the farmland, information indicating an improvement plan for farming of the farmland is acquired, The program according to claim 3 , wherein in the step of outputting the acquired predetermined information, information indicating a plan for improving farming operations of the farmland is output.

8. The program according to claim 7 , wherein in the step of outputting the acquired predetermined information, information indicating an improvement plan for farming of the farmland is output for each of the fields.

9. In the step of acquiring predetermined information for providing information about the farmland, information indicating an improvement plan for the farmland suitable for procuring predetermined crops is acquired; The program according to claim 1 , wherein the step of outputting the acquired predetermined information outputs information indicating an improvement plan for the farmland.

10. In the step of estimating and calculating the amount of GHG emissions in the agricultural land, the amount of GHG emissions in the agricultural land is estimated and calculated for each of the farm fields; 4. The program according to claim 3, wherein in the step of outputting information on GHG emissions in the farmland, the calculated information on GHG emissions is output for each of the farm fields.

11. An information processing device that includes a control unit and a memory and manages GHG (Green House Gas) emissions in agricultural land, The control unit acquiring scan data obtained by scanning the farmland; analyzing the scan data to estimate the farming status of the farmland; a step of estimating and calculating the amount of GHG emissions from the farmland based on the estimated farming status of the farmland; a step of acquiring predetermined information for providing information about the farmland based on the estimated farming status of the farmland; outputting the estimated farming status of the farmland, the calculated GHG emission amount information, and the acquired predetermined information; An information processing device that, in the step of estimating the farming status of the farmland, estimates the farming status of the farmland, including crops on the farmland and whether the farmland is fallow.

12. A method for managing GHG (Green House Gas) emissions in agricultural land, the method being executed by a computer having a processor and a memory, the method comprising: The method further comprises the processor: acquiring scan data obtained by scanning the farmland; analyzing the scan data to estimate the farming status of the farmland; a step of estimating and calculating the amount of GHG emissions from the farmland based on the estimated farming status of the farmland; a step of acquiring predetermined information for providing information about the farmland based on the estimated farming status of the farmland; outputting the estimated farming status of the farmland, the calculated GHG emission amount information, and the acquired predetermined information; A method for estimating the farming status of the farmland, wherein the step of estimating the farming status of the farmland includes estimating the crops on the farmland and whether the farmland is fallow.

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