Information processing device, method, and system

Abstract

The present invention is an information processing device comprising a control unit. The control unit executes: a process for acquiring address information indicating addresses of paddy fields and identifying a target paddy field to be estimated; a process for acquiring a satellite image of the target paddy field and water level information indicating the water level of the target paddy field each time a prescribed timing arrives during a prescribed period; a process for calculating, for each of a plurality of image cells constituting each of the plurality of satellite images on the basis of said image cell and the water level information, a first index indicating the flooding state of a section corresponding to the image cell; a process for taking a statistical value of the first index in units of the target paddy field for each of the plurality of satellite images, and calculating the statistical value as a second index indicating the flooding state of the target paddy field; and a process for estimating a non-flooding period of the target paddy field during the prescribed period on the basis of a time-series change of the second index calculated for each of the plurality of satellite images.

Description

Information Processing Apparatus, Method, and System

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

[0002] In recent years, for the purpose of creating carbon credits, research and development of technologies for estimating greenhouse gas emissions have been actively carried out.

[0003] For example, Patent Document 1 discloses a technique for calculating a daily water index for each of a plurality of water management sections included in paddy fields based on satellite data of the paddy fields, and classifying the status of each of the plurality of water management sections on a daily basis based on the satellite data and the calculated water index. Further, the technique disclosed in Patent Document 1 estimates a non-flooding period using the classified status. Furthermore, the technique disclosed in Patent Document 1 estimates the daily water level based on satellite data learned with the measurement result of the water level as teacher data, and estimates the daily greenhouse gas emissions based on the daily water level.

[0004] Japanese Patent No. 7460228

[0005] The technique disclosed in Patent Document 1 estimates the overall greenhouse gas emissions based on the estimation result of the non-flooding period and the estimation result of the daily greenhouse gas emissions. However, there is room for improvement in the estimation accuracy of the non-flooding period, which is a premise for estimating the overall greenhouse gas emissions, in the technique disclosed in Patent Document 1.

[0006] An object of the present disclosure is to improve the estimation accuracy of the non-flooding period.

[0007] To solve the aforementioned problems, a program according to one aspect of this disclosure is a program to be executed by a computer having a processor and memory. The program causes the processor to perform the following steps: to obtain address information indicating the address of a paddy field and identify a target paddy field to be estimated; to obtain satellite images of the target paddy field and water level information indicating the water level of the target paddy field each time a predetermined timing occurs within a predetermined period; for each of the acquired multiple satellite images, to calculate a first index indicating the flooding state of the section corresponding to the image cell in the target paddy field based on the image cell and water level information for each of the multiple image cells constituting the satellite image; to calculate a second index indicating the flooding state of the target paddy field by taking a statistical value of the calculated first index on a unit of the target paddy field for each of the acquired multiple satellite images; and to estimate the non-flooding period of the target paddy field within a predetermined period based on the time-series change of the second index calculated for each of the acquired multiple satellite images.

[0008] According to this disclosure, the accuracy of estimating the non-flooded period can be improved.

[0009] This is a block diagram showing an example of the overall configuration of System 1. This is a block diagram showing an example of the configuration of the terminal device 10 shown in Figure 1. This is a block diagram showing an example of the configuration of the server 20 shown in Figure 1. This is a diagram showing the data structure of the polygon table 2021 shown in Figure 3. This is a flowchart showing an example of the operation of the server 20 when estimating the mid-season drying period. This is a schematic diagram showing an example of the screen of the display 141 when application documents are presented to the user. This is a schematic diagram showing another example of the screen of the display 141 when application documents are presented to the user. This is a block diagram showing the basic hardware configuration of the computer 90.

[0010] 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.

[0011] [1. Overview] The system according to this embodiment acquires address information of a paddy field to identify the target paddy field. The system according to this embodiment acquires SAR images and water level information of the target paddy field at predetermined times during a predetermined period. For each of the acquired multiple SAR images, the system according to this embodiment inputs the image cell and water level information for each of the multiple image cells constituting the SAR image into a trained machine learning model, and outputs a first index from the machine learning model. For each of the acquired multiple SAR images, the system according to this embodiment calculates a second index by taking a statistical value of the calculated first index on a unit basis for the target paddy field. Based on the time-series changes of the second index calculated for each of the acquired multiple SAR images, the system according to this embodiment estimates the non-flooded period of the target paddy field during a predetermined period.

[0012] [2. Overall System Configuration] Figure 1 is a block diagram showing an example of the overall configuration of System 1. System 1 is a system for providing a service (hereinafter referred to as the estimation service) for estimating the mid-season drainage period of a target paddy field. The target paddy field is the paddy field that System 1 will estimate.

[0013] In this embodiment, an example is described in which System 1 estimates the mid-season drainage period, but System 1 is not limited to estimating only the mid-season drainage period. For example, System 1 may estimate the non-flooded period after planting, the non-flooded period before harvest, the non-flooded period after fertilization, the non-flooded period after pest and disease control, etc. In other words, System 1 only needs to estimate the non-flooded period of the target paddy field.

[0014] The system 1 shown in Figure 1 includes, for example, a terminal device 10, a server 20, an AI system 30, and a satellite 40. The terminal device 10, the server 20, and the AI ​​system 30 are connected to each other via, for example, a network 80. The satellite 40 transmits, for example, various satellite data to a ground station (not shown). The ground station is connected to the network 80 and, for example, receives a transmission request from the server 20 and transmits the satellite data to the server 20 via the network 80.

[0015] In Figure 1, an example is shown where System 1 includes one terminal device 10, but for example, System 1 may include two or more terminal devices 10. In Figure 1, an example is shown where System 1 includes one server 20, but for example, a collection of multiple devices may be considered as one server 20. The method of distributing the multiple functions required to realize Server 20 to one or more hardware can be appropriately determined according to the processing capacity of each hardware and / or the specifications required for Server 20.

[0016] Figure 1 shows an example where System 1 includes one AI system 30, but System 1 may include two or more AI systems 30. Also, Figure 1 shows an example where the AI ​​system 30 is independent of the server 20, but the server 20 may include the functions of the AI ​​system 30. In other words, the server 20 may store the machine learning models (details described later) included in the AI ​​system 30.

[0017] Terminal device 10 is, for example, an information processing device operated by a user. The user is, for example, a user of the estimated service. An example of a user of the estimated service is the person in charge of the mid-season drying project.

[0018] A mid-season drainage project is a project that generates carbon credits by, for example, extending the mid-season drainage period of a target rice paddy by a predetermined number of days compared to the conventional period during the rice cultivation season, thereby reducing methane emissions from the soil. There are various types of mid-season drainage projects, including those under the "J-Credit Scheme" in which the government certifies the credits, and any type of mid-season drainage project can be subject to System 1.

[0019] The person in charge of the mid-season drainage project will, for example, be responsible for the management and operation of the project, as well as managing the farmers participating in the project. Furthermore, individuals with diverse backgrounds can become the person in charge of the mid-season drainage project, including managers of agricultural companies, engineers or researchers with specialized agricultural knowledge, farmers, water management or environmental protection specialists, and so on.

[0020] Furthermore, users of the estimation service are not limited to those in charge of the mid-season drainage project. Anyone who is involved in estimating the non-flooded period of the target paddy field can become a user of the estimation service.

[0021] The terminal device 10 is implemented by, for example, a mobile device such as a smartphone or tablet. In this embodiment, the terminal device 10 is assumed to be a smartphone. The terminal device 10 may also be implemented by, for example, a stationary PC (Personal Computer), a laptop PC, or the like.

[0022] The terminal device 10 includes a communication interface (IF) 12, an input device 13, an output device 14, a memory 15, storage 16, and a processor 19. The input device 13 is a device for receiving input operations from the user (e.g., a touch panel, touchpad, pointing device such as a mouse, keyboard, etc.). The output device 14 is a device for presenting information to the user (display, speaker, etc.). In this embodiment, the terminal device 10 is assumed to have a touch panel that integrates the input device 13 and the output device 14.

[0023] Server 20 is, for example, an information processing device for managing and operating the estimation service, and is implemented by a computer connected to the network 80. As shown in Figure 1, Server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29. The input / output IF 23 functions as an interface for an input device to receive input operations from the administrator of the estimation service, and an output device to output information to the administrator.

[0024] The AI ​​system 30 is a system that includes a trained machine learning model (hereinafter abbreviated as "machine learning model"). The machine learning model is, for example, a decision tree algorithm, or a neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN). Alternatively, the machine learning model may be a multimodal generative AI model.

[0025] The training data for the machine learning model included in the AI ​​system 30 consists of, for example, actual measured values ​​of the water level in the target area, photographs of the target area, or the results of human judgment of the water level in the target area by a user, and SAR images. Details of the target area and SAR images will be described later.

[0026] The AI ​​system 30 may contain one or more machine learning models. Furthermore, if the AI ​​system 30 contains multiple machine learning models, they may be of the same type, or they may be of different types, such as one being a neural network and the other a multimodal generative AI model.

[0027] The AI ​​system 30 receives image cells and water level information transmitted from the server 20, inputs them into a machine learning model, and causes the machine learning model to output a first indicator. An image cell is a unit that constitutes a satellite image of the target paddy field (hereinafter abbreviated as "satellite image"). In this embodiment, an image cell is assumed to be one pixel, but for example, an image cell may be composed of multiple pixels. The AI ​​system 30 transmits the first indicator output from the machine learning model to the server 20. Details of the water level information and the first indicator will be described later.

[0028] The artificial satellite 40 acquires satellite images as satellite data and transmits them to a ground station. Figure 1 shows an example in which system 1 includes one artificial satellite 40, but for example, system 1 may include two or more artificial satellites 40. If it includes two or more, the types of artificial satellites 40 may be the same or they may be different.

[0029] In this embodiment, the artificial satellite 40 acquires SAR images as satellite images and transmits them to the server 20 via a ground station. SAR images are satellite images acquired by SAR (Synthetic Aperture Radar). In other words, in this embodiment, the artificial satellite 40 is equipped with SAR. The artificial satellite 40 acquires SAR images by irradiating an object with microwaves (electromagnetic waves) from the SAR and receiving the reflected (backscattered) signals.

[0030] Because SAR images are generated using microwaves (electromagnetic waves), they are perfectly usable even if generated in bad weather or at night. Furthermore, because SAR images are generated based on synthetic aperture technology, they are high-resolution and useful for obtaining detailed information about the Earth's surface and water surface.

[0031] The vibration characteristics of the microwaves (electromagnetic waves) irradiated onto the target object from the SAR may be single-polarization, dual-polarization, or quadruple-polarization. Furthermore, the combination of transmitting and receiving polarizations can be arbitrarily set.

[0032] Server 20 acquires SAR images from a ground station, for example, in the GRD (Ground Range Detected) or SLC (Single Look Complex) file format. However, Server 20 may also acquire SAR images from a ground station, for example, in the Polarimetric SAR (PolSAR) or HDF5 (Hierarchical Data Format version 5) file format.

[0033] Furthermore, the satellite images transmitted by the satellite 40 to the server 20 via the ground station are not limited to SAR images. The satellite 40 may transmit, for example, optical satellite images (visible light images, near-infrared images, etc.), spectral satellite images, or topographic satellite images (DEM: Digital Elevation Model).

[0034] Each information processing device, such as the terminal device 10, the server 20, and the AI ​​system 30, is composed of a computer 90 (see Figure 8) equipped with an arithmetic unit and a memory device. The basic hardware configuration of the computer 90 and the basic functional configuration of the computer 90 realized by this basic hardware configuration will be described later. Note that explanations of the terminal device 10, the server 20, and the AI ​​system 30 that overlap with the basic hardware configuration of the computer 90 and the basic functional configuration of the computer will be omitted.

[0035] <2.1 Configuration of Terminal Device> Figure 2 is a block diagram showing an example configuration of the terminal device 10 shown in Figure 1. As shown in Figure 2, the terminal device 10 includes a communication unit 120, an input device 13, an output device 14, an audio processing unit 170, a microphone 171, a speaker 172, a camera 160, a position information sensor 150, an acceleration sensor 155, a storage unit 180, and a control unit 190. Each block included in the terminal device 10 is electrically connected, for example, by a bus.

[0036] The communication unit 120 performs modulation and demodulation processing for the terminal device 10 to communicate with an external device (for example, a server 20). The communication unit 120 performs transmission processing on the signal generated by the control unit 190 and transmits it to the external device. The communication unit 120 performs reception processing on the signal received from the external device and outputs it to the control unit 190.

[0037] The input device 13 is a device for the user to input instructions or information. The input device 13 can be implemented, for example, by a touch-sensitive device 131 on which instructions are input by touching the operating surface. If the terminal device 10 is a PC or the like, the input device 13 may be implemented by a reader, keyboard, mouse, etc. The input device 13 converts the instructions input by the user into electrical signals and outputs them to the control unit 190. The input device 13 may also include, for example, a receiving port that accepts electrical signals input from an external input device.

[0038] The output device 14 is a device for presenting information to the user. The output device 14 is implemented, for example, by a display 141. The display 141 displays various information according to the control of the control unit 190. The display 141 is implemented, for example, by an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0039] The audio processing unit 170 performs, for example, digital-to-analog conversion processing of the audio signal. The audio processing unit 170 converts the signal provided from the microphone 171 into a digital signal and provides the converted signal to the control unit 190. The audio processing unit 170 also provides the audio signal to the speaker 172. The audio processing unit 170 is implemented, for example, by an audio processing processor. The microphone 171 receives an audio input and provides the audio signal corresponding to the audio input to the audio processing unit 170. The speaker 172 converts the audio signal provided from the audio processing unit 170 into audio and outputs the audio to the outside of the terminal device 10.

[0040] Camera 160 is an imaging device that captures images using visible light. In other words, camera 160 is a device that receives visible light using a photodetector and outputs image data as a shooting signal. Camera 160 captures subjects in a certain direction and within a certain shooting range relative to the terminal device 10 and outputs image data as a result of the capture. If camera 160 has a function that allows adjustment of the shooting range, or more precisely, the angle of view, camera 160 also outputs information regarding this angle of view. Such a function is called a zoom function.

[0041] The location information sensor 150 is a sensor that detects the position of the terminal device 10, and is generally a GNSS device, such as a GPS module. A GPS module is a receiving device used in a satellite positioning system. In a satellite positioning system, signals are received from at least three or four satellites, and based on the received signals, the current position of the terminal device 10, which is equipped with a GPS module, is detected as coordinate values. The location information sensor 150 may also detect the current position of the terminal device 10 from the position of a wireless base station to which the terminal device 10 is connected via the communication unit 120.

[0042] The acceleration sensor 155 is a sensor that detects the acceleration applied to the terminal device 10. Preferably, the acceleration sensor 155 has a function of detecting the inclination around each axis (X-axis, Y-axis, Z-axis) of the three-dimensional coordinates with the position of the terminal device 10 as the origin. The acceleration sensor 155 having such a function can detect the posture of the terminal device 10, that is, the directions with respect to the X-axis, Y-axis, and Z-axis, by detecting the gravitational acceleration of the universal gravitation with respect to the Earth.

[0043] The storage unit 180 is realized by the memory 15 and the storage 16 shown in FIG. 1, and stores the data and programs used by the terminal device 10. The programs include application programs such as web browser applications.

[0044] The control unit 190 is realized, for example, by the processor 19 reading the programs stored in the storage unit 180 and executing the instructions included in the programs. The control unit 190 controls the operation of the terminal device 10. By operating according to the read programs, the control unit 190 exhibits the functions as the operation reception unit 191, the transmission / reception unit 192, and the presentation control unit 193.

[0045] The operation reception unit 191 performs processing for receiving instructions or information input from the input device 13. Specifically, the operation reception unit 191 receives instructions or information input from the touch-sensitive device 131. The transmission / reception unit 192 performs processing for the terminal device 10 to transmit and receive data to and from an external device according to a communication protocol. Specifically, the transmission / reception unit 192 transmits instructions or information input by the user to the server 20. The transmission / reception unit 192 receives information transmitted from the server 20. The presentation control unit 193 controls the output device 14 to present various information to the user.

[0046] <2.2 Server Configuration> FIG. 3 is a block diagram showing a configuration example of the server 20 shown in FIG. 1. As shown in FIG. 3, the server 20 exhibits the functions as the communication unit 201, the storage unit 202, and the control unit 203.

[0047] The communication unit 201 performs processes for the server 20 to communicate with an external device (e.g., the terminal device 10). The storage unit 202 is realized by the memory 25 and the storage 26, and stores data and programs used by the server 20. The programs include application programs such as web browser applications. The storage unit 202 stores, for example, a polygon table 2021 and an app 2022.

[0048] The polygon table 2021 is a table that stores polygon information. The polygon information is information regarding the target paddy field and includes at least address information. Details of the polygon information will be described later.

[0049] The address information is information indicating the address of the target paddy field. The address of the target paddy field indicated by the address information may be, for example, the address reported by the user and may not match the registered address registered in the register. Also, the address of the target paddy field indicated by the address information and the registered address of the target paddy field registered in the register may both have multiple addresses for the target paddy field, or the target paddy field and one or more paddy fields adjacent to the target paddy field may have the same address.

[0050] The app 2022 is an application for managing the use of the estimation service by the user. The app 2022 is executed, for example, in the background of other applications installed in the server 20 and monitors the processes executed by the user. The user can access the app 2022 in the server 20 by using the web browser application installed in the terminal device 10.

[0051] Note that the server 20 may, for example, grasp / manage the usage status of the app 2022 and execute predetermined analysis processes. Also, for example, the app 2022 may be installed in the terminal device 10 and stored in the storage unit 180.

[0052] The control unit 203 is realized when the processor 29 reads a program stored in the memory unit 202 and executes the instructions contained in the program. The control unit 203 controls the operation of the server 20. By operating according to the read program, the control unit 203 performs the functions of a receive control module 2031, a transmit control module 2032, a presentation control module 2033, and an estimation processing module 2034.

[0053] The receiving control module 2031 controls the process by which the server 20 receives signals from external devices according to a communication protocol. For example, the receiving control module 2031 receives address information transmitted from the terminal device 10. Also, for example, the receiving control module 2031 receives SAR images and water level information transmitted from the satellite 40 via a ground station at predetermined timings within a predetermined period.

[0054] Water level information refers to information indicating the water level of a target paddy field, and is a concept that includes drainage (water level at zero). In water level information, the reference point for high and low water levels (water level 0) is, for example, the ground surface. Water level information is measured continuously for a predetermined period of time by, for example, a water level gauge, water tube, or water level sensor (not shown) installed in the target paddy field.

[0055] The water level information may be temporarily stored in the terminal device 10 as a result of a display confirmation by a user, for example, who has checked the display on a water level gauge or water tube. Then, for example, the terminal device 10, upon receiving a transmission request from the server 20, may transmit the water level information to the server 20. Alternatively, for example, the water level information may be transmitted to the server 20 via the terminal device 10 by a water level sensor upon receiving a transmission request from the server 20, or it may be transmitted directly to the server 20.

[0056] The predetermined period is a period set to estimate the non-flooded period of the target paddy field, and can be any period as long as it is long enough to estimate the non-flooded period. In this embodiment, as an example, the predetermined period is set to estimate the mid-season drainage period of the target paddy field. For example, if the user is seeking certification for credit application through the use of the "J-Credit System," the predetermined period may be a period that is seven days or more longer than the average number of mid-season drainage days in the most recent two years or more prior to the implementation of the mid-season drainage project in the target paddy field (the mid-season drainage period required to receive certification). Hereinafter, the predetermined period will be referred to as the "estimated implementation period."

[0057] The predetermined timing can be any timing at which the server 20 can acquire the necessary number of SAR images and water level information to estimate the non-flooded period of the target paddy field during the estimated implementation period. In this embodiment, as an example, the predetermined timing is the timing at which the server 20 can acquire the necessary number of SAR images and water level information to estimate the mid-season drainage period of the target paddy field during the estimated implementation period. The predetermined timing may be, for example, a fixed period. In addition, the predetermined timing at which SAR images are acquired and the predetermined timing at which water level information is acquired are, in principle, synchronized, but it is not necessarily required. Hereinafter, the predetermined timing will be referred to as the "acquisition timing".

[0058] The estimated implementation period and acquisition timing may be hardcoded in the application 2022, for example. Alternatively, the estimated implementation period and acquisition timing may be set by editing the program code of the application 2022 when the server 20 accepts a setting operation for the estimated implementation period and acquisition timing.

[0059] The transmission control module 2032 controls the process by which the server 20 transmits signals to external devices according to a communication protocol. The presentation control module 2033 controls the process of presenting various information to the user.

[0060] The estimation processing module 2034 calculates a first index for each of the multiple SAR images received by the receiving control module 2031, based on the image cell and water level information for each of the multiple image cells that make up the SAR image. The first index is an index that indicates the flooding state of the section corresponding to the image cell in the target paddy field. Hereinafter, the section corresponding to the image cell will be referred to as the "target section".

[0061] In this embodiment, the first indicator is the probability of flooding in the target area. The probability of flooding is a ratio indicating how much water is contained in the target area. For example, if the target area is drained, the probability of flooding is "0". Note that an indicator other than the probability of flooding may be used as the first indicator. For example, a discrete indicator may be used as the first indicator, where "0" represents a flooded state (regardless of the degree of flooding) and "1" represents an unflooded state. The same applies to the second indicator, which will be described later.

[0062] The estimation processing module 2034 may, for example, calculate the first index each time the receiving control module 2031 receives SAR images and water level information. Alternatively, for example, the estimation processing module 2034 may calculate the first index all at once after receiving all of the SAR images and water level information that the receiving control module 2031 can receive during the estimation period.

[0063] In this embodiment, the estimation processing module 2034 inputs image cell and water level information to a machine learning model included in the AI ​​system 30, and causes the machine learning model to output a first indicator. If the machine learning model is a multimodal generation AI model, a prompt instructing the output of the first indicator is also input to the machine learning model. The prompt may be hardcoded, for example, in a program stored in the AI ​​system 30. Alternatively, for example, an input device (not shown) provided in the server 20 may receive an input operation for the prompt, and the server 20 may send the prompt to the AI ​​system 30. Or, the prompt may be pre-stored in the storage unit 202 or the AI ​​system 30.

[0064] The estimation processing module 2034 may calculate the first indicator using rule-based processing instead of a machine learning model, for example. Specifically, the estimation processing module 2034 may calculate the amount of water in the target paddy field by analyzing the SAR image and water level information using a known analysis method, taking into account the rice variety present in the target paddy field, the density of rice in the target paddy field, etc. The estimation processing module 2034 may, for example, read the maximum moisture content (saturation moisture content) of the target paddy field, which is stored in the storage unit 202 in advance, and use the calculated water amount mentioned above as the first indicator (accuracy of flooding of the target paddy field) as a percentage (%) obtained by dividing it by the maximum moisture content. The maximum moisture content of the target paddy field may be obtained by the server 20 when the terminal device 10 transmits measured values, for example, measured in advance by the user, to the server 20.

[0065] The estimation processing module 2034 calculates a second index by taking a statistical value of the first index calculated for each of the multiple SAR images received by the receiving control module 2031, on a unit basis for the target paddy field. The second index is an index indicating the flooding state of the target paddy field and is of the same type as the first index. In this embodiment, the second index is a statistical value obtained by taking statistics on the flooding accuracy of the target plot on a unit basis for the target paddy field. There are no particular limitations on the type of statistical value, and it may be the mean, median, or mode.

[0066] The estimation processing module 2034 estimates the non-flooded period of the target paddy field during the estimation period based on the time-series changes of the second indicator calculated for each of the multiple SAR images received by the receiving control module 2031. In this embodiment, as an example, the estimation processing module 2034 estimates the mid-season drainage period of the target paddy field during the estimation period.

[0067] There are no particular limitations on the method used by the estimation processing module 2034 to estimate the mid-season drainage period of the target paddy field. In this embodiment, for example, the estimation processing module 2034 sequentially compares all of the calculated second indicators with a reference value in the order of acquisition timing, and estimates the period during which these second indicators exist as the mid-season drainage period if a predetermined number of second indicators are below the reference value for a predetermined number of consecutive periods. In this estimation process, the reference value and the predetermined number can be arbitrarily set according to the characteristics of the target paddy field, the required estimation accuracy, etc.

[0068] In this embodiment, the estimation processing module 2034 performs preprocessing on the SAR image for calculating the first index. For example, as preprocessing for calculating the first index, the estimation processing module 2034 corrects for the influence of the topography of the target paddy field for each of the multiple SAR images received by the receiving control module 2031 (hereinafter referred to as the correction process). Alternatively, as preprocessing for calculating the first index, the estimation processing module 2034 performs polarization decomposition on each of the multiple SAR images received by the receiving control module 2031 and reflects the estimated results of the scattering characteristics of the target paddy field in the SAR image (hereinafter referred to as the polarization decomposition process).

[0069] Specifically, for example, the estimation processing module 2034 performs correction processing on the SAR image, such as geometry correction, line-of-sight correction or altitude correction using a Digital Elevation Model (DEM) of the target paddy field, ambient correction, radiometric correction, or at least two or more combinations thereof.

[0070] For example, the estimation processing module 2034 applies polarization decomposition to the SAR image as polarization decomposition processing, such as the basis decomposition method, Cloude-Pottier decomposition, Freeman-Durden decomposition, or integral transform. This allows the estimation processing module 2034 to estimate the scattering characteristics of the target paddy field. Examples of scattering characteristics include surface scattering, double reflection, and volume scattering. Surface scattering is scattering by wide, flat terrain such as water surfaces, ground, and snowfields. Double reflection is scattering by structures such as buildings and bridges. Volume scattering is scattering by objects where scattering occurs multiple times internally, such as forests and farmland. The estimation processing module 2034 then reflects the estimated scattering characteristics in the SAR image to generate a polarization-decomposed image. The polarization-decomposed image is a SAR image displaying the estimated scattering characteristics of the target paddy field obtained through polarization decomposition. There are no particular limitations on the display method of the estimation results in the polarization-decomposed image. For example, a polarization-resolved SAR image may be obtained by displaying each object in the target rice paddy with a different color assigned to each object with the same scattering characteristics.

[0071] In this embodiment, the estimation processing module 2034 applies both correction processing and polarization resolution processing to the SAR image. Specifically, the estimation processing module 2034 first applies correction processing to each of the multiple SAR images received by the receiving control module 2031 to reduce the influence of the topography of the target area in the SAR image. Next, the estimation processing module 2034 applies polarization resolution processing to the corrected SAR image to generate a polarization-resolved image. Then, the estimation processing module 2034 calculates a first index based on the generated polarization-resolved image and water level information.

[0072] As described above, by applying correction processing and polarization resolution processing to the SAR image as preprocessing for calculating the first indicator, the estimation accuracy of the flooding state of the target area indicated by the first indicator is improved. Consequently, the estimation accuracy of the flooding state of the target paddy field indicated by the second indicator is improved, and the estimation accuracy of the non-flooding period is further improved.

[0073] It is not mandatory for the estimation processing module 2034 to perform the two preprocessing steps described above on the SAR image. For example, the estimation processing module 2034 may perform only one of either correction processing or polarization resolution processing on the SAR image. Alternatively, for example, the estimation processing module 2034 may directly input the SAR image received by the reception control module 2031 into the machine learning model without performing any preprocessing on the SAR image.

[0074] [3. Data Structure] Figure 4 shows the data structure of a table stored by the server 20. Note that Figure 4 is merely an example and does not exclude data that is not shown. Also, even if data is listed in the same table, it may be stored in separate memory areas in the storage unit 202.

[0075] Figure 4 shows the data structure of polygon table 2021. Polygon table 2021 shown in Figure 4 is a table that uses polygon ID as the key and has columns for registered address information, project information, image information, water level information, and estimation results.

[0076] The item "Polygon ID" stores an identifier for uniquely identifying the target paddy field. The item "Registered Address Information" stores registered address information. Details of the registered address information will be described later. The item "Project Information" stores various information related to the mid-season drainage project. Examples of various information related to the mid-season drainage project include the project implementing body, participating bodies, objectives, outline of implementation details, and implementation period. The item "Image Information" stores various information related to the SAR image. There are no particular limitations on the content of the various information related to the SAR image. In this embodiment, the numerical values ​​of each scattering characteristic displayed in the polarization-resolved image are stored in the item "Image Information". Examples of numerical values ​​of scattering characteristics (hereinafter, "numerical values ​​indicating scattering characteristics") include the specular scattering coefficient, volume scattering coefficient, and threshold scattering coefficient. The item "Water Level Information" stores the water level information of the target paddy field. The item "Estimated Result" stores the estimated result of the mid-season drying period (the estimated mid-season drying period described later) by the estimation processing module 2034.

[0077] In this embodiment, project information is stored in the "Project Information" field when the terminal device 10, which receives a user's transmission request, sends the project information to the server 20, and the server 20 receives it. In this case, the project information may be stored in advance in the storage unit 180, or it may be entered into the input device 13 by the user's input operation. Alternatively, the project information may be stored in the "Project Information" field when, for example, the facsimile machine on the administrator's side receives scanned data of a document (containing the project information) sent from the user's facsimile machine, and the server 20 accepts the administrator's input operation for the project information.

[0078] Regarding various information related to the SAR image, in this embodiment, the estimation processing module 2034 analyzes the polarization-resolved image at each acquisition timing to calculate numerical values ​​indicating scattering characteristics and stores them in the item "Image Information".

[0079] In this embodiment, regarding water level information, the terminal device 10, upon receiving a transmission request from the server 20 at each acquisition timing, transmits the water level information to the server 20, and upon reception by the server 20, it is stored in the item "Water Level Information". In this case, all water level information for the estimated implementation period may be stored in advance in the storage unit 180, or it may be input to the input device 13 by user input operation. For example, the water level sensor, upon receiving a transmission request from the server 20 at each acquisition timing, transmits the water level information to the server 20, and upon reception by the server 20, the water level information is stored in the item "Water Level Information".

[0080] [4 Operation] An example of the operation of the server 20 when estimating the mid-season drainage period will be described. Figure 5 is a flowchart showing an example of the operation of the server 20 when estimating the mid-season drainage period. Note that the example of the operation of the server 20 shown in Figure 5 applies not only to the estimation of the mid-season drainage period but also to the estimation of the non-flooded period in general.

[0081] In step S11 shown in Figure 5, the server 20 obtains address information and identifies the target rice paddy (identification step).

[0082] Specifically, for example, the operation reception unit 191 receives a request from the user to send address information. The transmission / reception unit 192 reads the address information from the storage unit 180 and sends it to the server 20. The reception control module 2031 receives the address information sent from the terminal device 10. As a result, the server 20 acquires the address information.

[0083] The receiving control module 2031, for example, compares the address information received from the terminal device 10 with the registered address information stored in a public database such as EMAFF (Ministry of Agriculture, Forestry and Fisheries Common Application Service). The registered address information is information indicating the registered address of the paddy field as registered in the land register. It should be noted that using a public database is not mandatory for this comparison process. Any database that stores registered address information can be used for this comparison process.

[0084] In this embodiment, before matching the address information with the registered address, the receiving control module 2031 inputs the address information, along with a prompt (an instruction to correct inconsistencies in the notation of the address of the target rice paddy), into an unillustrated generating AI model, and causes the generating AI model to output the address information with the inconsistencies corrected. The generating AI model is, for example, an LLM (Large Language Model) and is stored in the storage unit 202 or the AI ​​system 30 along with the prompt.

[0085] By correcting these inconsistencies in notation, server 20 can quickly and accurately identify the target rice paddy. Note that correcting inconsistencies using the generated AI model is not mandatory.

[0086] The receiving control module 2031 identifies, for example, registered address information whose content matches the address information whose spelling variations have been corrected. The receiving control module 2031 identifies, for example, the rice paddy corresponding to the identified registered address information as the target rice paddy. The receiving control module 2031 stores, for example, the identified registered address information in the "Registered Address Information" item of the polygon table 2021.

[0087] In the example shown in Figure 5, the server 20 executes the processes in step S11 and step S12 consecutively, but this is not the only case. For example, the server 20 may execute the processes in step S11 and the processes from step S12 onward separately. This is because, basically, the process in step S11 is executed before the estimated implementation period, and the processes from step S12 onward are executed after the start of the estimated implementation period.

[0088] In step S12, the server 20 acquires SAR images and water level information whenever the acquisition timing arrives during the estimated implementation period (step of acquiring satellite images and water level information).

[0089] Specifically, for example, the transmission control module 2032 sends transmission request information to the ground station indicating a request to transmit a SAR image whenever the acquisition timing arrives. The reception control module 2031 receives a SAR image from the ground station that received the transmission request whenever the acquisition timing arrives. As a result, the server 20 acquires a SAR image whenever the acquisition timing arrives.

[0090] The transmission control module 2032, for example, sends transmission request information indicating a request to transmit water level information to the terminal device 10 each time the acquisition timing arrives. The reception control module 2031, for example, receives water level information from the terminal device 10 that received the transmission request each time the acquisition timing arrives. As a result, the server 20 acquires water level information each time the acquisition timing arrives.

[0091] In the example shown in Figure 5, the server 20 executes the processes in step S12 and step S13 consecutively, but this is not the only case. For example, the server 20 may execute the processes in step S12 and the processes from step S13 onward separately. This is because, basically, the process in step S12 is executed during the estimated execution period, and the processes from step S13 onward are executed after the estimated execution period ends.

[0092] In step S13, the server 20 calculates a first index for each of the multiple image cells that make up the acquired SAR image, based on the image cell and water level information (step of calculating the first index).

[0093] Specifically, for example, before calculating the first index, the estimation processing module 2034 applies correction processing and polarization decomposition processing to each of the multiple SAR images received by the reception control module 2031 during the estimation period. That is, the estimation processing module 2034 first applies correction processing to the SAR image, and then applies polarization decomposition processing to the corrected SAR image to generate a polarization decomposed image.

[0094] The estimation processing module 2034 reads, for example, all water level information for the estimation period from the polygon table 2021. The estimation processing module 2034 then transmits, for example, the generated polarization-resolved images and the water level information acquired at the same time as each of the polarization-resolved images to the AI ​​system 30.

[0095] For example, the AI ​​system 30 inputs, for each of the multiple polarization-resolved images received from the server 20, the image cell and the water level information received from the server 20 into a machine learning model. For example, the AI ​​system 30 causes the machine learning model to output a first index corresponding to each of the multiple image cells for each of the multiple polarization-resolved images received from the server 20. For example, the AI ​​system 30 sends all of the first indexes output from the machine learning model to the server 20.

[0096] The receiving control module 2031 receives, for example, the first index transmitted from the AI ​​system 30. As a result, the server 20 calculates the first index for each of the multiple image cells that make up each of the multiple SAR images acquired.

[0097] In step S14, the server 20 calculates a second index by taking a statistical value of the first index calculated for each of the acquired SAR images in units of the target paddy field (step of calculating the second index).

[0098] Specifically, for example, the estimation processing module 2034 calculates a second index by taking statistical values ​​of the first index corresponding to each of the multiple image cells constituting the SAR image, based on the first index received by the reception control module 2031. The estimation processing module 2034 performs this calculation process for all SAR images acquired during the estimation period.

[0099] In step S15, the server 20 estimates the mid-season drainage period of the target paddy field during the estimated implementation period based on the time-series changes of the second indicator calculated for each of the acquired SAR images (estimation step).

[0100] Specifically, for example, the estimation processing module 2034 reads information indicating a reference value from the storage unit 202 and sequentially compares all of the calculated second indicators with the reference value according to the acquisition timing. The information indicating the reference value may, for example, be stored in advance in the storage unit 202, or it may be stored in the storage unit 202 after receiving an input operation from the administrator and inputting it to the server 20. Alternatively, for example, the reference value may be hardcoded in the application 2022. The estimation processing module 2034 estimates the period during which a predetermined number of second indicators that are below the reference value exist as the mid-season drying period. The predetermined number is, for example, hardcoded in the application 2022.

[0101] In step S16, the server 20 presents the estimated non-flooding period to at least one of the user and the administrator. Specifically, for example, the presentation control module 2033 presents the mid-season drainage period (hereinafter, "estimated mid-season drainage period") estimated by the estimation processing module 2034 to at least one of the user or the administrator.

[0102] When presenting the estimated mid-season drying period to the user, the transmission control module 2032 transmits, for example, information for displaying the estimated mid-season drying period to the terminal device 10. The transmission / reception unit 192 receives, for example, information for displaying the estimated mid-season drying period from the server 20. When the presentation control unit 193 receives, for example, a presentation request from the user, it displays the estimated mid-season drying period on the display 141.

[0103] When presenting the estimated mid-season drying period to the management operator, the presentation control module 2033, for example, upon receiving a presentation request from the management operator, displays the estimated mid-season drying period on the display (not shown) of the output device provided on the server 20.

[0104] Furthermore, if the output devices of output device 14 and server 20 are printers, the presentation control module 2033 may, for example, print out a paper medium on which the estimated mid-season drying period is printed from the output devices of output device 14 and server 20. Also, it is not mandatory for the presentation control module 2033 to present the estimated mid-season drying period to at least one of the user and the administrator.

[0105] [5 Summary] As described above, in this embodiment, the terminal device 10 receives a request from the user to send address information and sends the address information to the server 20. The receiving control module 2031 compares the address information received from the terminal device 10 with the registered address information stored in the public database and identifies the registered address information whose content matches the address information. The receiving control module 2031 identifies the paddy field corresponding to the identified registered address information as the target paddy field.

[0106] The receiving control module 2031 receives SAR images from the ground station that has received a transmission request from the transmitting control module 2032 whenever the acquisition timing arrives. The receiving control module 2031 also receives water level information from the terminal device 10 that has received a transmission request from the transmitting control module 2032 whenever the acquisition timing arrives. Before calculating the first index, the estimation processing module 2034 first applies a correction process to each of the multiple SAR images received by the receiving control module 2031 during the estimation period, and then applies a polarization resolution process to the corrected SAR images to generate a polarization-resolved image.

[0107] The estimation processing module 2034 transmits, for example, the generated polarization-resolved images and water level information acquired at the same time as each of the polarization-resolved images to the AI ​​system 30. For each of the polarization-resolved images received from the server 20, the AI ​​system 30 inputs the image cell and the water level information received from the server 20 into a machine learning model. The AI ​​system 30 outputs a first index corresponding to each of the image cells from the machine learning model and transmits it to the server 20.

[0108] The estimation processing module 2034 calculates a second indicator by taking statistical values ​​of the first indicator corresponding to each of the multiple image cells constituting the SAR image for the first indicator received by the reception control module 2031. The estimation processing module 2034 sequentially compares all of the calculated second indicators with a reference value according to the acquisition timing. If a predetermined number of second indicators that are below the reference value exist consecutively, the estimation processing module 2034 estimates the period during which these second indicators exist as the mid-season drying period.

[0109] As a result, the server 20 can use a first indicator, which estimates the flooding state of each target plot that makes up the target paddy field, to estimate the flooding state of the entire target paddy field as a second indicator. Therefore, the server 20 can take into account the finer characteristics of each area of ​​the target paddy field more accurately when estimating the flooding state of the entire target paddy field compared to when the first indicator is not used directly. Thus, the server 20 can improve the accuracy of estimating the non-flooding period.

[0110] [6. Modifications] In this embodiment, as an example of utilizing the estimated mid-season drying period, an example was given in which the server 20 presents the estimated mid-season drying period. However, the server 20 can employ various other utilization examples besides presenting the estimated mid-season drying period. In other words, the system 1 can provide various utilization services in the estimation service other than presenting the estimated mid-season drying period.

[0111] The following explanation will use an example of a service that System 1 can provide: creating documents for applying for the issuance of carbon credits (hereinafter referred to as "application documents"). Specifically, Server 20 may create application documents based, for example, on the estimated mid-season drying period.

[0112] First, the server 20 may, for example, obtain declared information regarding the mid-season drainage period declared by the user (step of obtaining declared information). The declared information may include, for example, information to prove the mid-season drainage period declared by the user. Examples of information to prove the mid-season drainage period declared by the user include scanned data of a logbook (diary) that records the dates on which flooding, drainage, etc., were carried out during the estimated implementation period. The logbook may, for example, be created by the farmer who owns the target paddy field. The user may, for example, receive a logbook from the farmer and store the declared information obtained by scanning the logbook in the terminal device 10 in advance.

[0113] Specifically, for example, the operation reception unit 191 may receive a request from the user to send declaration information. The transmission / reception unit 192 may, for example, read the declaration information from the storage unit 180 and send it to the server 20. The reception control module 2031 may, for example, receive the declaration information sent from the terminal device 10. As a result, the server 20 may acquire the declaration information.

[0114] The server 20 may, for example, obtain the declaration information by having the facsimile machine on the management side receive scanned data of the logbook sent from the user's or farmer's side, and then accepting the input operation of the declaration information by the management side.

[0115] The receiving control module 2031 may, for example, store the received declaration information in the polygon table 2021. In this case, for example, the polygon table 2021 may have an item "Declaration Information" for storing the declaration information, and the receiving control module 2031 may store the received declaration information in the item "Declaration Information". Alternatively, for example, the receiving control module 2031 may store the received declaration information in the item "Project Information".

[0116] Next, the server 20 may, for example, verify the degree of reliability of the declared information based on the time-series changes of the acquired declared information and the second indicator (verification step).

[0117] Specifically, for example, the estimation processing module 2034 may compare each date of flooding, drainage, etc., included in the declared information with the second indicator corresponding to each of those dates in a time-series manner. Alternatively, the estimation processing module 2034 may estimate the time-series changes in the flooding state of the target paddy field from each date of flooding, drainage, etc., included in the declared information, and compare this estimation result with the time-series changes in the second indicator. For example, the estimation processing module 2034 may verify the degree of reliability of the declared information based on the comparison result.

[0118] Specifically, for example, the estimation processing module 2035 may determine that the declared information is highly reliable if the rate of agreement between the time-series changes in the flooded state as determined from the declared information and the time-series changes in the second indicator is equal to or greater than a baseline rate. On the other hand, if the aforementioned rate is less than the baseline rate, the estimation processing module 2035 may determine that the declared information is unreliable. The baseline rate can be arbitrarily set depending on the characteristics of the target paddy field, the level of reliability required for the declared information, etc.

[0119] The estimation processing module 2034 may, after verifying the reliability of the declared information, decide to use the declared information in creating the declaration documents if it determines that the declared information is highly reliable. On the other hand, if it determines that the declared information is unreliable, the estimation processing module 2034 may, for example, display the verification result indicating that the declared information is unreliable on the display of the output device provided on the server 20. In this case, for example, the administrator may, by understanding the verification result displayed on the display, inquire with the user about the reliability of the declared information. The administrator may then, for example, modify the declared information as appropriate in response to the user's response to the inquiry and input it into the input device provided on the server 20. The estimation processing module 2034 may, for example, by receiving the modified declared information, decide to use the modified declared information in creating the declaration documents.

[0120] Next, the server 20 may, for example, create application documents using the declaration information (creation step).

[0121] Specifically, for example, the estimation processing module 2034 may refer to the project information stored in the polygon table 2021 and read the format of the application documents corresponding to the mid-season project from a format table (not shown). The format table may store, for example, the format for each type of mid-season project. The format table may be stored, for example, in the storage unit 202.

[0122] The estimation processing module 2034 may, for example, input the declaration information into the corresponding fields of the format read from the format table. Alternatively, the estimation processing module 2034 may, for example, input the corresponding information stored in the polygon table 2021 into the fields other than the corresponding fields in the format. In this way, the estimation processing module 2034 may create the application documents.

[0123] The aforementioned process for creating application documents is merely one example, and various other creation processes are conceivable. The estimation processing module 2034 may, for example, input the declaration information, along with the relevant information and prompts (instructions for creating application documents) stored in the polygon table 2021, into the generating AI model, and then output the application documents from the generating AI model. The generating AI model may be, for example, an LLM, and may be stored in the storage unit 202 or the AI ​​system 30 along with the prompts.

[0124] Next, the server 20 may present the created application documents to the user. That is, the presentation control module 2033 may present the application documents created by the estimation processing module 2034 to the user.

[0125] Specifically, for example, the transmission control module 2032 may transmit information for displaying the application documents to the terminal device 10. The transmission / reception unit 192 may receive information for displaying the application documents from the server 20. The presentation control unit 193 may, for example, display the application documents on the display 141 when it receives a presentation request from the user. As a result, the server 20 may present the application documents to the user.

[0126] The presentation control module 2033 may also control the presentation control unit 193 to display the application documents on the display 141 in an editable state. In this case, for example, the operation reception unit 191 may accept an editing operation from the user, and the presentation control unit 193 may edit the application documents displayed on the display 141 to match the content of the editing operation. Alternatively, for example, the presentation control module 2033 may present the application documents created by the estimation processing module 2034 to the administrator. Alternatively, for example, if the output device 14 is a printer, the presentation control module 2033 may print out the application documents from the output device 14. Furthermore, it is not mandatory for the presentation control module 2033 to present the application documents to the user. In this case, the administrator may, for example, mail the application documents printed out from the output device of the server 20 to the user.

[0127] Thus, according to this modified version, the server 20 can automatically create application documents simply by the user providing address information and declaration information to the server 20. This improves user convenience when using the estimation service.

[0128] The following describes examples of the display 141 screen when application documents are presented to a user, with reference to Figures 6 and 7. Figure 6 is a schematic diagram showing an example of the display 141 screen when application documents are presented to a user. Figure 7 is a schematic diagram showing another example of the display 141 screen when application documents are presented to a user.

[0129] In the example screen shown in Figure 6, the registration application form 1411 is displayed on the display 141 as an application document. The registration application form 1411 includes items such as application category, how the support funds will be used, project information, applicant information, participation in the carbon neutral action plan, and review information. The corresponding information is displayed in the input field for each item. The display control module 2033 reads the corresponding information from, for example, the polygon table 2021 and displays it in the input field. Here, the corresponding information for each item, such as how the support funds will be used, applicant information, participation in the carbon neutral action plan, and review information, may be stored in, for example, the "project information" item in the polygon table 2021.

[0130] For example, the display area directly below the registration application form 1411 on the display 141 shows an edit button 1412 and a confirm button 1413. For example, when the operation reception unit 191 receives a press of the edit button 1412, the corresponding information in the input field of each item becomes editable. For example, when the operation reception unit 191 receives a press of the confirm button 1413, the corresponding information in the input field of each item becomes confirmed. Even if the user presses the edit button 1412 after pressing the confirm button 1413, the corresponding information in the input field of each item remains uneditable.

[0131] In the example screen shown in Figure 7, the project plan 1414 is displayed on the display 141 as an application document. The project plan 1414 includes items such as project name, project implementer name, review information, project overview, reduction activity methodology, and data management. The corresponding information is displayed in the input field for each item. The presentation control module 2033 reads the corresponding information from, for example, the polygon table 2021 and displays it in the input field. Here, the corresponding information for each item, project name, project implementer name, review information, project overview, reduction activity methodology, and data management, may be stored, for example, in the item "Project Information" in the polygon table 2021.

[0132] For example, the display area directly below the project plan 1414 on display 141 shows the edit button 1415 and the confirm button 1416. For example, the edit button 1415 has the same function as the edit button 1412, and the confirm button 1416 has the same function as the confirm button 1413.

[0133] The screen examples in Figures 6 and 7 are merely examples, and various variations are conceivable regarding the content and presentation method of the application documents. For example, the display control module 2033 may display only one of the registration application form 1411 or the project plan 1414 on the display 141. Alternatively, the display control module 2033 may display an application document in a format that combines the contents of the registration application form 1411 and the project plan 1414 into a single document. Furthermore, the display control module 2033 may add, delete, or modify each item of the registration application form 1411 and the project plan 1414 and display them on the display 141. In addition, the display control module 2033 may display an application document in a format different from the registration application form 1411 and the project plan 1414 on the display 141.

[0134] Furthermore, the preparation of application documents discussed in this modified example can also be applied when System 1 provides estimation services for non-flooded periods other than the mid-season drainage period. In other words, Server 20 may prepare the application documents based on estimation results for the entire non-flooded period.

[0135] [7 Basic Hardware Configuration of the Computer] Figure 8 is a block diagram showing the basic hardware configuration of computer 90. Computer 90 includes at least a processor 901, main memory 902, auxiliary storage 903, and a communication IF 991 (interface). These are electrically connected to each other by a communication bus 921.

[0136] The processor 901 is hardware for executing the instruction set described in a program. The processor 901 consists of an arithmetic unit, registers, peripheral circuits, etc.

[0137] The main memory 902 is for temporarily storing programs and data processed by programs, etc. For example, it is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0138] The auxiliary storage device 903 is a storage device for storing data and programs. Examples include flash memory, HDD (Hard Disk Drive), magneto-optical disk, CD-ROM, DVD-ROM, semiconductor memory, etc.

[0139] The communication interface IF991 is an interface for inputting and outputting signals for communication with other computers via a network using wired or wireless communication standards.

[0140] A network consists of various mobile communication systems, such as the Internet, LANs, and wireless base stations. For example, a network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks that can connect to the Internet via designated access points (e.g., Wi-Fi®). When connecting wirelessly, communication protocols include, for example, Z-Wave®, ZigBee®, and Bluetooth®. When connecting via a wired connection, the network also includes connections made directly via USB (Universal Serial Bus) cables, etc.

[0141] Furthermore, by distributing all or part of each hardware configuration across multiple computers 90 and connecting them to each other via a network, a computer 90 can be virtually realized. Thus, the concept of computer 90 includes not only a computer 90 housed in a single enclosure or case, but also a virtualized computer system.

[0142] [8 Basic Functional Configuration of Computer 90] The functional configuration of the computer realized by the basic hardware configuration of computer 90 (Figure 8) will be described below. The computer comprises at least one functional unit: a control unit, a memory unit, and a communication unit.

[0143] Furthermore, the functional units of computer 90 can also be realized by distributing all or part of each functional unit across multiple computers 90 interconnected via a network. The term "computer 90" is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0144] The control unit is realized when the processor 901 reads various programs stored in the auxiliary storage device 903, loads them into the main memory device 902, and executes processing according to those programs. The control unit can realize various functional units that perform information processing depending on the type of program. In this way, the computer is realized as an information processing device that performs information processing.

[0145] The memory unit is implemented by a main memory 902 and an auxiliary memory 903. The memory unit stores data, various programs, and various databases. The processor 901 can also reserve a memory area corresponding to the memory unit in the main memory 902 or the auxiliary memory 903 according to a program. The control unit can also cause the processor 901 to perform addition, update, and deletion operations on data stored in the memory unit according to various programs.

[0146] A database, specifically a relational database, is used to manage and link together tabular data sets called masters, which are structurally defined by rows and columns. In a database, tables are called tables, masters are called masters, the columns of tables are called columns, and the rows of tables are called records. In a relational database, relationships can be established and linked between tables and masters.

[0147] Typically, each table and master has a primary key column to uniquely identify records, but setting a primary key column is not mandatory. The control unit can instruct the processor 901 to add, delete, or update records in specific tables and masters stored in the storage unit, according to various programs.

[0148] Furthermore, by storing data, various programs, and various databases in the memory unit, the information processing device and information processing system related to this disclosure can be considered to have been manufactured.

[0149] Furthermore, the databases and masters in this disclosure may include any data structures (lists, dictionaries, associative arrays, objects, etc.) in which information is structurally defined. Data structures also include data that can be considered as data structures by combining data with functions, classes, methods, etc., written in any programming language.

[0150] The communication unit is implemented by the communication IF 991. The communication unit implements the function of communicating with other computers 90 via the network. The communication unit can receive information transmitted from other computers 90 and input it to the control unit. The control unit can cause the processor 901 to perform information processing on the received information according to various programs. The communication unit can also transmit information output from the control unit to other computers 90.

[0151] Furthermore, each of the above-mentioned configurations, functions, processing units, processing means, etc., may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. The present invention can also be implemented by software program code that realizes the functions of the embodiments. In this case, a storage medium on which the program code is recorded is provided to a computer, and the processor of that computer reads the program code stored in the storage medium. In this case, the program code read from the storage medium itself realizes the functions of the embodiments described above, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media used to supply such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, and the like.

[0152] Furthermore, the program code that implements the functions described in this embodiment can be implemented in a wide range of programming or scripting languages, such as assembler, C / C++, perl, Shell, PHP, Java®, JavaScript, and TypeScript.

[0153] Furthermore, the program code of the software that realizes the functions of the embodiment may be distributed via a network and stored on a storage means such as a computer's hard disk or memory, or on a storage medium such as a CD-RW or CD-R, and the computer's processor may read and execute the program code stored on the storage means or storage medium.

[0154] The functions realized by the components described herein may be implemented in a circuit or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and / or combinations thereof, programmed to realize the functions described herein. A processor is considered to be a circuit or processing circuitry, including transistors and other circuits. A processor may be a programmed processor that executes a program stored in memory.

[0155] In this specification, circuitry, unit, and means are hardware programmed to perform or execute the functions described herein. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein.

[0156] If the hardware is a processor that is considered to be a type of circuitry, then the circuitry, means, or unit is a combination of hardware and software used to constitute the hardware and / or processor.

[0157] Although several embodiments of this disclosure have been described above, these embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made 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.

[0158] [9. Addendum] The matters described in each of the above embodiments are added below.

[0159] <Note 1> A program to be executed by a computer having a processor and memory, the program causing the processor to execute the following steps: to obtain address information indicating the address of a paddy field and identify a target paddy field to be estimated; to obtain satellite images of the target paddy field and water level information indicating the water level of the target paddy field each time a predetermined timing occurs within a predetermined period; for each of the acquired multiple satellite images, for each of the multiple image cells constituting the satellite image, to calculate a first index indicating the flooding state of the section corresponding to the image cell in the target paddy field based on the image cell and water level information; for each of the acquired multiple satellite images, to calculate a statistical value of the calculated first index on a unit of the target paddy field, and to calculate the statistical value as a second index indicating the flooding state of the target paddy field; and to estimate the non-flooding period of the target paddy field within a predetermined period based on the time-series change of the second index calculated for each of the acquired multiple satellite images.

[0160] <Note 2> The satellite image is a SAR image, as described in the program in (Note 1).

[0161] <Note 3> The program described in (Note 2) causes the processor to perform an additional step of correcting for the influence of the topography of the target paddy field for each of the acquired SAR images as a preprocessing step for calculating the first index.

[0162] <Note 4> The program described in (Note 2) or (Note 3) further causes the processor to perform the step of polarization decomposing each of the acquired SAR images and reflecting the estimated scattering characteristics of the target paddy field in the SAR image, as a preprocessing step for calculating the first index.

[0163] <Note 5> In the step of calculating the first indicator, the program described in any of (Note 1) to (Note 4) inputs the image cell and water level information into a trained machine learning model and outputs the first indicator from the machine learning model.

[0164] <Note 6> A program described in any of (Notes 1) to (Note 5) that causes the processor to perform the additional step of creating documents for applying for the issuance of carbon credits based on the estimated results of the non-flooded period.

[0165] <Note 7> The non-flooded period is the mid-season drainage period, and the steps to create the program include the steps of obtaining declared information regarding the mid-season drainage period declared by the user, and verifying the degree of reliability of the declared information based on the obtained declared information and the time-series changes of the second indicator (as described in Note 6).

[0166] <Note 8> An information processing device comprising a control unit and a storage unit, wherein the control unit executes all steps in any of the programs described in (Note 1) to (Note 7).

[0167] <Note 9> A method to be executed on a computer having a processor and memory, wherein the processor executes all steps in any of the programs described in (Note 1) to (Note 7).

[0168] <Note 10> A system comprising means for executing all steps in any of the programs described in (Note 1) to (Note 7).

[0169] 1...System 10...Terminal device 120...Communication unit 13...Input device 14...Output device 15...Memory 16...Storage 19...Processor 20...Server 22...Communication IF 23...Input / Output IF 25...Memory 26...Storage 29...Processor 30...AI system 40...Artificial satellite

Claims

1. An information processing device comprising a control unit and a storage unit, wherein the control unit performs the following processes: acquiring address information indicating the address of a paddy field to identify a target paddy field to be estimated; acquiring satellite images of the target paddy field and water level information indicating the water level of the target paddy field each time a predetermined timing occurs within a predetermined period; for each of the acquired plurality of satellite images, calculating a first index indicating the flooded state of the section corresponding to the image cell in the target paddy field based on the image cell and the water level information for each of the plurality of image cells constituting the satellite image; for each of the acquired plurality of satellite images, calculating a statistical value of the calculated first index on a unit of the target paddy field, and calculating the statistical value as a second index indicating the flooded state of the target paddy field; and estimating the non-flooded period of the target paddy field within the predetermined period based on the time-series change of the second index calculated for each of the acquired plurality of satellite images.

2. The information processing apparatus according to claim 1, wherein the satellite image is a SAR image.

3. The information processing apparatus according to claim 2, wherein the control unit further performs a process to correct for the influence of the topography of the target paddy field for each of the acquired SAR images as a preprocessing step for calculating the first index.

4. The information processing apparatus according to claim 2, wherein the control unit further performs a process to perform polarization decomposition on each of the acquired SAR images and reflect the estimated scattering characteristics of the target paddy field in the SAR image, as a preprocessing step for calculating the first index.

5. The information processing apparatus according to any one of claims 1 to 4, wherein the control unit inputs the image cell and the water level information to a trained machine learning model in the process of calculating the first indicator, and outputs the first indicator from the machine learning model.

6. The information processing apparatus according to any one of claims 1 to 5, wherein the control unit further performs a process to create documents for applying for the issuance of carbon credits based on the estimated results of the non-flooded period.

7. The non-flooded period is a mid-season drainage period, and the information processing apparatus according to claim 6 includes a process of acquiring declared information regarding the mid-season drainage period declared by the user, and a process of verifying the degree of reliability of the declared information based on the acquired declared information and the time-series changes of the second indicator.

8. A method performed by one or more computers, comprising: the steps of: obtaining address information indicating the address of a paddy field to identify a target paddy field to be estimated; obtaining satellite images of the target paddy field and water level information indicating the water level of the target paddy field each time a predetermined timing occurs within a predetermined period; for each of the multiple satellite images obtained, calculating a first index indicating the flooding state of the section corresponding to the image cell in the target paddy field based on the image cell and the water level information for each of the multiple image cells constituting the satellite image; for each of the multiple satellite images obtained, calculating a statistical value of the calculated first index on a unit of the target paddy field, and using the statistical value as a second index indicating the flooding state of the target paddy field; and estimating the non-flooding period of the target paddy field within the predetermined period based on the time-series change of the second index calculated for each of the multiple satellite images obtained.

9. A system for estimating the period of non-flooding of a paddy field, comprising: means for acquiring address information indicating the address of a paddy field to be identified; means for acquiring satellite images of the target paddy field and water level information indicating the water level of the target paddy field each time a predetermined timing occurs within a predetermined period; means for each of the acquired plurality of satellite images to calculate a first index indicating the flooding state of the section corresponding to the image cell in the target paddy field based on the image cell and the water level information for each of the plurality of image cells constituting the satellite image; means for each of the acquired plurality of satellite images to calculate a second index indicating the flooding state of the target paddy field by taking a statistical value of the calculated first index on a unit of the target paddy field; and means for estimating the period of non-flooding of the target paddy field within the predetermined period based on the time-series change of the second index calculated for each of the acquired plurality of satellite images.

Images