Evaluation system, evaluation method, and evaluation program

Abstract

To stably evaluate crop water damage over a wide area.SOLUTION: An evaluation system includes an estimation unit (111) for calculating estimated leaf areas of crops planted in the field on the basis of aerial images of the field to be evaluated for water damage, a calculation unit (112) for calculating the ideal leaf area corresponding to the aerial image at a time of capture on the basis of information related to crop growth conditions in the field, and an evaluation unit (113) for evaluating the state of moisture damage in the field on the basis of the estimated value and the ideal value.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a technique for evaluating the state of moisture damage in a farm field. [Background technology]

[0002] There are some crops for which it is important to avoid moisture damage. For example, soybeans are often sown in May or June, and are prone to moisture damage when the rainy season arrives in the early stages of growth. One measure to avoid moisture damage is drainage measures in fields. However, the construction of drainage measures requires a great deal of effort. For example, there is a demand for technology to evaluate the moisture damage status in fields in order to select fields where drainage measures should be given priority.

[0003] Non-Patent Document 1 describes a technique for evaluating the condition of moisture damage in a field from the leaf color of soybeans, while Non-Patent Document 2 describes a technique for estimating soil moisture from the color of the soil in order to predict conditions that may cause moisture damage to soybeans. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Shuhei Yamamoto et. al., "Evaluation of soybean moisture damage in farm fields based on UAV remote sensing," Jpn. J. Crop Sci. 88(1):p48-49, 2019 [Non-Patent Document 2] Naoyuki Hashimoto et. al., "Estimation of soil moisture content using UAV remote sensing for predicting water damage in soybean fields," Jpn. J. Crop Sci. 89(1):52-53, 2020 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Non-Patent Document 1 requires high-resolution images suitable for sensing leaf color, making it difficult to evaluate moisture damage over a wide area. Also, when there is a lot of cloud cover, it is difficult to capture images suitable for sensing leaf color, making it difficult to stably monitor leaf color.

[0006] In addition, the technology described in Non-Patent Document 2 requires high-resolution images suitable for sensing soil color, making it difficult to evaluate moisture damage over a wide area. In addition, when the coverage rate of the soil by leaves increases with growth, it becomes difficult to sense the soil color from the images, making it difficult to stably monitor the soil color.

[0007] As described above, these conventional techniques have the problem that it is difficult to stably evaluate the state of moisture damage in farm fields over a wide area.

[0008] One aspect of the present invention has been made to solve the above-mentioned problems, and aims to realize a technology for stably evaluating moisture damage to crops over a wide area. [Means for solving the problem]

[0009] In order to solve the above problems, an evaluation system according to one embodiment of the present invention includes an estimation unit that calculates an estimate of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field, a calculation unit that calculates an ideal value of the leaf area corresponding to the time when the aerial image was taken based on information related to the growing conditions of the crop in the field, and an evaluation unit that evaluates the moisture damage situation in the field based on the estimated value and the ideal value.

[0010] In order to solve the above problems, an evaluation method according to one embodiment of the present invention is an evaluation method executed by a computer, and includes an estimation step of determining an estimated value of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field, a calculation step of calculating an ideal value of the leaf area corresponding to the time when the aerial image was taken based on information related to the growing conditions of the crop in the field, and an evaluation step of evaluating the moisture damage situation in the field based on the estimated value and the ideal value.

[0011] In order to solve the above problems, an evaluation program according to one aspect of the present invention is an evaluation program for causing a computer to function as the above-mentioned evaluation system, and causes the computer to function as the estimation unit, the calculation unit, and the evaluation unit. Note that a computer-readable recording medium having the evaluation program recorded thereon also falls within the scope of the present invention. Effect of the Invention

[0012] According to one aspect of the present invention, moisture damage to crops can be stably evaluated over a wide area. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram illustrating an overview of an evaluation system according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram showing a functional configuration of an evaluation system according to an embodiment of the present invention. [Diagram 3] FIG. 1 is a flow diagram showing the flow of an evaluation method according to one embodiment of the present invention. [Figure 4] FIG. 13 is a schematic diagram showing an example of an evaluation result according to an embodiment of the present invention. [Diagram 5] FIG. 11 is a flow diagram showing the flow of another evaluation method according to an embodiment of the present invention. [Figure 6] FIG. 11 is a schematic diagram showing an example of another evaluation result according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

[0015] The inventors of the present application focused on the fact that leaf area growth is suppressed in crops that have suffered damage from moisture damage, and invented a technology that estimates the leaf area of ​​crops from aerial images taken of the field being evaluated, calculates an ideal value for leaf area from a growth prediction model, and evaluates moisture damage from the estimated and ideal values ​​for leaf area.

[0016] Here, it is known that there is a close relationship between the leaf area and the coverage rate when moisture damage occurs, and the coverage rate can be estimated even with low-resolution images. It is also known that the leaf area can be estimated using a synthetic aperture radar that can capture images through clouds. For this reason, the leaf area can be monitored more stably than the leaf color and the soil color. In this way, in the present invention, the leaf area can be used to stably evaluate moisture damage over a wide area. An embodiment of the present invention will be described below.

[0017] [Overview of Evaluation System 1] FIG. 1 is a schematic diagram for explaining an overview of an evaluation system 1 according to this embodiment. As shown in FIG. 1, the evaluation system 1 is a system that evaluates the moisture damage status in evaluation target fields D1 and D2 using aerial images of the fields D1 and D2. The evaluation system 1 includes a server 10 that provides a moisture damage evaluation service, and a terminal 20 of a user who uses the moisture damage evaluation service. The user U can grasp the moisture damage status in the fields D1 and D2 using the terminal 20. The user U may be, for example, a producer who manages the fields D1 and D2.

[0018] The aerial images are captured by, for example, the photographing devices C1 and C2. The aerial images need only include at least a portion of at least one farm field as a subject, but may include multiple farm fields as a subject. The photographing devices C1 and C2 are devices capable of capturing aerial images. For example, the photographing device C1 is a UAV (Unmanned Aerial Vehicle) and can capture UAV images including a portion or the entirety of one farm field D1 as a subject. Also, for example, the photographing device C2 is a satellite and can capture satellite images including multiple farm fields D1 and D2 as subjects.

[0019] The evaluation system 1 is also connected to an image collecting device 30 via a network NW. The image collecting device 30 is a device that collects and stores aerial images taken by the photographing devices C1 and C2. For example, the image collecting device 30 may be configured to collect and store aerial images taken periodically by the photographing devices C1 and C2 in real time. For example, in the image collecting device 30, an aerial image is associated with the photographing date and time, area identification information that identifies the area where the image was taken, field identification information that identifies the field included as a subject, and the like. Note that if one aerial image includes multiple fields as subjects, the aerial image is associated with multiple field identification information. Note that if the area where one aerial image is taken includes multiple areas, the aerial image is associated with multiple area identification information.

[0020] 1 shows two farm fields D1, D2 and two image capture devices C1, C2, the number of farm fields to be evaluated by the evaluation system 1 and the number of image capture devices to be used are not limited to two, and may be one, or three or more. Hereinafter, the farm fields D1 and D2 will not be particularly distinguished from each other, and will be simply referred to as farm fields. Also, while FIG. 1 shows one terminal 20, the evaluation system 1 may include multiple terminals 20.

[0021] [Configuration of evaluation system 1] Fig. 2 is a block diagram showing an example of a functional configuration of the evaluation system 1. As shown in Fig. 2, the evaluation system 1 includes a server 10 and a terminal 20. The server 10 is communicably connected to each of the terminal 20 and the image collection device 30 via a network NW. The network NW is configured to include, for example, a wired LAN (Local Area Network), a wireless LAN, a WAN, a mobile data communication network, or a combination of some or all of these.

[0022] (Server 10 configuration) The server 10 includes a control unit 110, a storage unit 120, and a communication unit 130. When the server 10 is configured by a computer equipped with a processor, a memory, and a communication interface, the control unit 110 is realized by the processor executing an evaluation program P1 stored in the memory. The storage unit 120 is realized by the memory. The communication unit 130 is realized by the processor controlling the communication interface.

[0023] The control unit 110 controls each unit of the server 10. The control unit 110 includes an estimation unit 111, a calculation unit 112, an evaluation unit 113, an acquisition unit 114, and a display control unit 115. The storage unit 120 stores various information used by the control unit 110. For example, the storage unit 120 stores an evaluation program P1 and a growth prediction model M. The evaluation program P1 is a program that cooperates with an evaluation program P2 described later to cause the server 10 to function as at least a part of the evaluation system 1. The growth prediction model M is a model that calculates an ideal value of a leaf area based on information related to the growth conditions of a crop, and a specific example will be described later. The communication unit 130 communicates with other devices under the control of the control unit 110.

[0024] The estimation unit 111 obtains an estimated value of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage, based on an aerial image of the field. The crop may be, for example, a crop that experiences a rainy season in the early stages of growth. Examples of such crops include soybeans and buckwheat. These crops are sown from May to July in areas of Japan that experience a rainy season, and are therefore susceptible to moisture damage in the early stages of growth due to the influence of the rainy season. However, the crops in this embodiment are not limited to the above examples.

[0025] For example, the estimation unit 111 may obtain an estimated value of the leaf area for each of a plurality of sections obtained by dividing the field. In addition, when a plurality of fields are included as subjects in the aerial photographed image, the estimation unit 111 may obtain an estimated value of the leaf area for each field.

[0026] Here, a known technique can be adopted for the process of obtaining an estimated value of the leaf area from the aerial photographed image. The estimation unit 111 may obtain an estimated value of the leaf area per unit area as the leaf area. The estimation unit 111 may also obtain an estimated value of the leaf area based on a coverage rate, which is the rate at which leaves cover the soil. As a specific example, the estimation unit 111 extracts a unit area (e.g., a farm field or a plot) from the aerial photographed image, and identifies whether each pixel included in the area is a leaf or not. Thereby, the estimation unit 111 may obtain the rate (coverage rate) that the pixels identified as leaves occupy in the unit area, and obtain an estimated value of the leaf area based on the coverage rate. However, the method of obtaining an estimated value of the leaf area from an image is not limited to the above-mentioned method.

[0027] The calculation unit 112 calculates an ideal value of the leaf area corresponding to the time of capturing the aerial image based on information related to the growing conditions of the crop in the field included as a subject in the aerial image. In addition, when the aerial image includes multiple fields as subjects, the calculation unit 112 may calculate the ideal value of the leaf area for each field.

[0028] Here, the process of calculating the ideal value of the leaf area from the growth conditions uses a growth prediction model M that predicts the leaf area based on information related to the growth conditions of the crop. Here, examples of the growth conditions include temperature conditions, planting density conditions, and the like. For example, the information related to the growth conditions may include information indicating the temperature in the field after the crop emergence date. Furthermore, the information related to the growth conditions may include information indicating the planting density of the crop in the field. Furthermore, the information related to the growth conditions may include information indicating the sowing date of the crop in the field. When the crop is soybean, an example of the growth prediction model M is expressed by the following formulas (1) and (2).

[0029]

number

number

[0030] The growth prediction model M shown in equations (1) and (2) is a model that the inventors of the present application have improved on the soybean leaf area growth prediction model M described in Reference 1 below to be particularly suitable for the early stages of growth.

[0031] [References 1] Sinclair, "Leaf Area Development in Field-Grown Soybeans", Agronomy Journal, 76, 141-146 Using equations (1) and (2), the calculation unit 112 calculates the leaf area index L as an ideal value of the leaf area. Note that the growth prediction model M used to calculate the ideal value of the leaf area is not limited to the above-mentioned model. Also, the information related to the growth conditions input to the growth prediction model M is not limited to the above-mentioned example. However, it is desirable that the information related to the growth conditions input to the growth prediction model M does not include information related to growth conditions related to moisture damage (e.g., soil moisture, etc.).

[0032] The evaluation unit 113 evaluates the moisture damage status in the field based on the estimated value and ideal value of the leaf area. For example, when the leaf area estimate is obtained for each of a plurality of sections into which the field is divided, the evaluation unit 113 may evaluate the moisture damage for each section. Also, when a plurality of fields are included as subjects in the aerial photograph image, the evaluation unit 113 may evaluate the moisture damage for each field.

[0033] For example, the evaluation unit 113 may evaluate the moisture damage by calculating an evaluation value indicating the moisture damage. The evaluation value indicating the moisture damage is calculated, for example, by the following formula (3).

[0034] Evaluation value = estimated leaf area / ideal leaf area … (3) In formula (3), " / " indicates division. The higher the evaluation value calculated by formula (3), the smaller the moisture damage, and the lower the evaluation value, the greater the moisture damage. However, the method by which the evaluation unit 113 evaluates the moisture damage is not limited to the example shown in formula (3).

[0035] The acquisition unit 114 acquires evaluation target information indicating a field to be evaluated for moisture damage. The evaluation target information may include, for example, field identification information, or may include area identification information indicating an area including the field. The evaluation target information may also include information related to the growing conditions of the crop in the field. The evaluation target information may also include information indicating the evaluation date and time of the evaluation target. For example, the acquisition unit 114 may acquire a part or all of the evaluation target information from the terminal 20. For example, the field identification information, germination date, planting density, etc. of the evaluation target field may be acquired from the terminal 20. The acquisition unit 114 may also estimate a part or all of the evaluation target information using an aerial image or another server. For example, the acquisition unit 114 may estimate the germination date of the crop based on an aerial image, or may estimate it based on the change in temperature from the sowing date. In the latter case, the sowing date may be acquired from the terminal 20. The information indicating the temperature after the germination date can be acquired by inquiring to a weather information providing server (not shown). The acquiring unit 114 may acquire predetermined information as a part or all of the evaluation target information. For example, predetermined farm field identification information may be acquired as the farm field to be evaluated.

[0036] The display control unit 115 generates an image showing the evaluation result by the evaluation unit 113 for the farm field indicated by the evaluation target information, and displays the image on a display device. For example, the display control unit 115 may transmit the image showing the evaluation result to the terminal 20, and control the image to be displayed on a display device 250 (described later) included in the terminal 20.

[0037] (Configuration of terminal 20) The terminal 20 includes a control unit 210, a storage unit 220, a communication unit 230, an input device 240, and a display device 250. When the terminal 20 is configured by a computer having a processor, a memory, and a communication interface, the control unit 210 is realized by the processor executing the evaluation program P2 stored in the memory. The storage unit 220 is realized by the memory. The communication unit 230 is realized by the processor controlling the communication interface. Examples of the input device 240 include, but are not limited to, a touch panel, a touch pad, a mouse, a keyboard, or a combination of these. Examples of the display device 250 include, but are not limited to, a liquid crystal display, an organic EL (electro luminescence) display, etc. In addition, the input device 240 and the display device 250 may be connected to the outside of the terminal 20 instead of being built into the terminal 20.

[0038] The control unit 210 controls each unit of the terminal 20. The control unit 210 also includes an acquisition unit 211 and a display control unit 212. The storage unit 120 stores various information used by the control unit 210. For example, the storage unit 220 stores an evaluation program P2. The evaluation program P2 is a program that cooperates with the above-mentioned evaluation program P1 to cause the terminal 20 to function as at least a part of the evaluation system 1. The communication unit 230 communicates with other devices under the control of the control unit 210.

[0039] The acquisition unit 211 acquires information related to the growth conditions via the input device 240, and transmits the acquired information to the server 10. The display control unit 212 displays, on the display device 250, an image showing the evaluation result received from the server 10.

[0040] (Evaluation method S1 flow) The evaluation system 1 configured as above executes the evaluation method S1. The evaluation method S1 is a method for evaluating the state of moisture damage for each section in a specific farm field. The flow of the evaluation method S1 will be described with reference to Fig. 3. Fig. 3 is a flow diagram showing the flow of the evaluation method S1. As shown in Fig. 3, the evaluation method S1 includes steps S101 to S109.

[0041] In step S101, the acquisition unit 211 of the terminal 20 acquires, via the input device 240, evaluation target information indicating a field to be evaluated for moisture damage, and transmits the acquired evaluation target information to the server 10. Details of the evaluation target information are as described above. Here, it is assumed that the user U inputs the evaluation target information including field identification information of a specific field to be evaluated, the germination date of the crop in the field, and the planting density into the terminal 20 using the input device 240. Note that the sowing density may be input instead of the planting density.

[0042] In step S102, the acquisition unit 114 of the server 10 receives and acquires the evaluation target information from the terminal 20.

[0043] In step S103, the estimation unit 111 acquires UAV images including the field to be evaluated as a subject from the image collection device 30 based on the evaluation target information. For example, the estimation unit 111 acquires UAV images associated with the field identification information included in the evaluation target information (i.e., UAV images including the relevant field as a main subject) from the image collection device 30. Note that, when the evaluation target information includes an evaluation date and time, the estimation unit 111 may acquire UAV images associated with the field identification information, the evaluation date and time (or the date and time closest to the evaluation date and time), from the image collection device 30. Also, when the evaluation target information does not include an evaluation date and time, the estimation unit 111 may acquire the latest UAV image from the image collection device 30, the UAV images associated with the field identification information.

[0044] In step S104, the estimation unit 111 extracts a field area from the UAV image. The estimation unit 111 also divides the extracted field area into a plurality of sections and extracts the area of ​​each section. A known technique can be used for the process of extracting the field area from the UAV image.

[0045] Step S105 is an example of an estimation step. In step S105, the estimation unit 111 refers to the area of ​​each extracted partition and obtains an estimated value of the leaf area for each partition. A specific example of the method for obtaining the estimated value of the leaf area is as described above.

[0046] Step S106 is an example of a calculation step. In step S106, the calculation unit 112 inputs information related to the growth conditions based on the evaluation target information into the growth prediction model M to calculate the ideal value of the leaf area in the field. For example, the calculation unit 112 acquires the daily average temperature of each day from the germination date included in the evaluation target information to the date and time of shooting the UAV image by inquiring of a weather information providing server or the like. In addition, the calculation unit 112 applies the acquired average temperature of each day and the planting density included in the evaluation target information to formulas (1) and (2) to calculate the ideal value of the leaf area. Note that, if the planting density is obtained for each section, the calculation unit 112 may obtain the ideal value of the leaf area for each section.

[0047] Step S107 is an example of an evaluation process. In step S107, the evaluation unit 113 evaluates the state of moisture damage for each section. For example, the evaluation unit 113 applies the ideal value of the leaf area in the corresponding field and the estimated value of the leaf area of ​​the section to formula (3) for each section to calculate an evaluation value of the section.

[0048] In step S108, the display control unit 115 generates an image showing the evaluation result by the evaluation unit 113 and transmits it to the terminal 20. For example, the display control unit 115 may generate an image in which a display element showing the evaluation value for each section is superimposed on the UAV image. For example, the display control unit 115 may generate an image in which a figure corresponding to the evaluation value of each section is superimposed on the area of ​​each section of the UAV image. The figure corresponding to the evaluation value may be, for example, a figure having the same shape as the area of ​​the section and filled with a color corresponding to the evaluation value. The color corresponding to the evaluation value may be a color associated with each stage obtained by dividing the range of the evaluation value into multiple stages. For example, when the evaluation value is 0 or more and 1 or less, red indicating large moisture damage may be associated with the first stage (e.g., 0 or more and less than 0.33) of the three stages, yellow indicating medium moisture damage may be associated with the second stage (e.g., 0.33 or more and less than 0.66), and green indicating small moisture damage may be associated with the third stage (e.g., 0.66 or more and less than 1). However, the number of stages, the range of each stage, and the color associated with each stage are not limited to the above examples. Also, a color according to the evaluation value may be calculated by associating a predetermined gradation (e.g., 256 gradations) between two specific colors (e.g., red and blue) with a range of evaluation values ​​(e.g., 0 to 1).

[0049] In step S109, the display control unit 212 of the terminal 20 displays on the display device 250 an image showing the received evaluation result.

[0050] 4 is a diagram showing an example of an image showing the evaluation result displayed in step S109. In FIG. 4, the image G1 showing the evaluation result is an image in which figures painted in different colors according to the evaluation value are superimposed on the area of ​​each section of the UAV image. By visually checking the image, the user U can grasp the moisture damage status of each section of the corresponding field.

[0051] (Evaluation method S2 flow) Furthermore, the evaluation system 1 executes an evaluation method S2. The evaluation method S2 is a method for evaluating the state of moisture damage in each of a plurality of farm fields. The flow of the evaluation method S2 will be described with reference to Fig. 5. Fig. 5 is a flow diagram showing the flow of the evaluation method S2. As shown in Fig. 5, the evaluation method S2 includes steps S201 to S209.

[0052] In step S201, the acquisition unit 211 of the terminal 20 acquires evaluation target information via the input device 240, and transmits the acquired evaluation target information to the server 10. Here, it is assumed that the user U inputs area identification information including a plurality of fields to be evaluated, and the germination date and planting density of each of the plurality of fields. Note that the sowing density may be input instead of the planting density.

[0053] In step S202, the acquisition unit 114 of the server 10 receives and acquires the evaluation target information from the terminal 20.

[0054] In step S203, the estimation unit 111 acquires a satellite image including the multiple fields to be evaluated as its subject from the image collecting device 30 based on the evaluation target information. For example, the estimation unit 111 acquires a satellite image associated with the area identification information (i.e., a satellite image including the multiple fields as its main subject) from the image collecting device 30. Note that, when the evaluation target information includes an evaluation date and time, the estimation unit 111 may acquire, from the image collecting device 30, a satellite image associated with the evaluation date and time (or a date and time closest to the evaluation date and time) from among the satellite images associated with the area identification information. Also, when the evaluation target information does not include an evaluation date and time, the estimation unit 111 may acquire, from the image collecting device 30, the latest satellite image from among the satellite images associated with the area identification information.

[0055] In step S204, the estimation unit 111 extracts the area of ​​each of the multiple farm fields from the satellite image. Note that a known technique can be adopted for the process of extracting the area of ​​each farm field from the satellite image.

[0056] Step S205 is an example of an estimation step. In step S205, the estimation unit 111 refers to the area of ​​each extracted field and obtains an estimated value of the leaf area for each field. A specific example of the method for obtaining the estimated value of the leaf area is as described above.

[0057] Step S206 is an example of a calculation step. In step S206, the calculation unit 112 calculates an ideal value of the leaf area for each field by inputting information related to the growth conditions in each of the multiple fields included in the evaluation target information into the growth prediction model M. A specific example of the process of calculating the ideal value for each field will be described in the same manner as in step S106, and therefore detailed description will not be repeated.

[0058] Step S207 is an example of an evaluation process. In step S207, the evaluation unit 113 evaluates the state of moisture damage for each field. For example, the evaluation unit 113 applies the ideal value of the leaf area in the corresponding field and the estimated value of the leaf area of ​​the field to formula (3) to calculate an evaluation value of the field.

[0059] In step S208, the display control unit 115 generates an image showing the evaluation result by the evaluation unit 113 and transmits it to the terminal 20. For example, the display control unit 115 may generate an image in which a display element showing an evaluation value for each field is superimposed on a satellite image. For example, the display control unit 115 may generate an image in which a figure corresponding to the evaluation value of each field is superimposed on the area of ​​the field in the satellite image. Specific examples of the figure corresponding to the evaluation value are described in the same manner as in step S108, and therefore detailed description will not be repeated.

[0060] In step S209, the display control unit 212 of the terminal 20 displays on the display device 250 an image showing the received evaluation result.

[0061] Fig. 6 is a diagram showing an example of an image showing the evaluation results displayed in step S209. In Fig. 6, image G2 showing the evaluation results is an image in which a figure corresponding to the evaluation value is superimposed on the area of ​​each field in the satellite image. By visually checking the image, user U can grasp the condition of moisture damage for each field in the corresponding area.

[0062] In the above-described embodiment, some or all of the functional blocks included in the server 10 may be included in the terminal 20.

[0063] [Software implementation example] The functions of the servers 10 (hereinafter referred to as "devices") constituting the evaluation system can be realized by a program for causing a computer to function as the device, and by a program for causing a computer to function as each control block of the device (particularly each part included in control units 110, 210).

[0064] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program to realize each function described in each of the above embodiments.

[0065] The program may be non-transitory and may be recorded in one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be provided to the device via any wired or wireless transmission medium.

[0066] In addition, some or all of the functions of each of the control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0067] 〔summary〕 The evaluation system according to the first aspect of the present embodiment includes an estimation unit that obtains an estimated value of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field, a calculation unit that calculates an ideal value of the leaf area corresponding to the time when the aerial image was taken based on information related to the growing conditions of the crop in the field, and an evaluation unit that evaluates the moisture damage situation in the field based on the estimated value and the ideal value. According to the above configuration, moisture damage of crops can be stably evaluated over a wide area. Furthermore, when the configuration is applied to each of a plurality of fields, the influence of moisture damage on crop growth can be compared between the plurality of fields.

[0068] The evaluation system according to Aspect 2 of the present embodiment is the same as Aspect 1, in which the crop is a crop that experiences a rainy season in the early stage of growth. With the above configuration, it is possible to stably evaluate moisture damage in soybeans over a wide area.

[0069] In the evaluation system according to Aspect 3 of the present embodiment, in Aspect 1 or Aspect 2, the information on the growth conditions includes information indicating an air temperature in the field after the crop emergence date. With the above configuration, it is possible to evaluate the extent to which the crop is affected by moisture damage in comparison with an ideal value of the leaf area predicted from the air temperature after the crop emergence date.

[0070] The evaluation system according to Aspect 4 of the present embodiment is any one of Aspects 1 to 3, wherein the information on the growth conditions includes information indicating the planting density of the crop in the field. With the above configuration, it is possible to evaluate the extent to which the crop is affected by water damage in comparison with an ideal value of the leaf area predicted from the planting density.

[0071] The evaluation system according to Aspect 5 of the present embodiment is any one of Aspects 1 to 4, wherein the information on the growth conditions includes information indicating a sowing date of the crop in the field. With the above configuration, it is possible to evaluate the extent to which the crop is affected by water damage in comparison with an ideal value of the leaf area predicted from the sowing date.

[0072] The evaluation system according to aspect 6 of the present embodiment is any one of aspects 1 to 5, wherein the aerial photographed image includes a plurality of fields as subjects, the estimation unit obtains the estimated value for each of the fields, the calculation unit calculates the ideal value for each of the fields, and the evaluation unit evaluates the moisture damage for each of the fields. With the above configuration, the degree of moisture damage to crops can be compared between a plurality of fields.

[0073] The evaluation system according to aspect 7 of the present embodiment is any one of aspects 1 to 6, wherein the estimation unit obtains the estimated value for each of a plurality of plots into which the field is divided, and the evaluation unit evaluates the moisture damage for each of the plots. With the above configuration, the degree of moisture damage affecting the crops can be evaluated in detail for each plot.

[0074] The evaluation system according to aspect 8 of the present embodiment is any one of aspects 1 to 7, further including an acquisition unit that acquires evaluation target information indicating a field to be evaluated for moisture damage, and a display control unit that generates an image indicating an evaluation result by the evaluation unit for the field indicated by the evaluation target information and displays the image on a display device. With the above configuration, a user viewing the display device can easily grasp the degree of moisture damage.

[0075] The evaluation method according to aspect 9 of the present embodiment is an evaluation method executed by a computer, and includes an estimation step of estimating a leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field, a calculation step of calculating an ideal value of the leaf area corresponding to the time when the aerial image was taken based on information related to the growing conditions of the crop in the field, and an evaluation step of evaluating the moisture damage condition in the field based on the estimated value and the ideal value. The above configuration provides the same effects as aspect 1.

[0076] An evaluation program according to aspect 10 of the present embodiment is an evaluation program for causing a computer to function as the evaluation system according to any one of aspects 1 to 7, and causes a computer to function as the estimation unit, the calculation unit, and the evaluation unit. According to the above configuration, the same effect as any one of aspects 1 to 8 is achieved.

[0077] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0078] 1. Rating System 10 Server 20 Terminals 30 Image acquisition device 110, 210 Control unit 120, 220 storage section 130, 230 Communications Department 111 Estimation Department 112 Calculation section 113 Evaluation Department 114, 211 Acquisition Department 115, 212 Display control unit 240 Input Device 250 Display device

Claims

1. an estimation unit that calculates an estimated value of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field; a calculation unit that calculates an ideal value of the leaf area corresponding to the time when the aerial image was captured based on information related to the growing conditions of the crop in the field; an evaluation unit that evaluates a state of moisture damage in the field based on the estimated value and the ideal value; Including, a rating system.

2. The crop is a crop that experiences a rainy season in the early stages of growth. The evaluation system according to claim 1 .

3. The evaluation system according to claim 1 or 2, wherein the information on the growing conditions includes information indicating an air temperature in the field after the date of germination of the crop.

4. The evaluation system according to claim 1 , wherein the information on the growing conditions includes information indicating a planting density of the crop in the field.

5. The evaluation system according to claim 1 , wherein the information on the growing conditions includes information indicating a sowing date of the crop in the field.

6. The aerial image includes a plurality of farm fields as subjects, The estimation unit obtains the estimated value for each of the farm fields, The calculation unit calculates the ideal value for each of the fields, The evaluation unit evaluates the moisture damage for each of the fields. The evaluation system according to claim 1 or 2.

7. The estimation unit obtains the estimated value for each of a plurality of sections obtained by dividing the farm field, The evaluation unit evaluates the moisture damage for each of the sections. The evaluation system according to claim 1 or 2.

8. An acquisition unit that acquires evaluation target information indicating a farm field that is to be evaluated for moisture damage; a display control unit that generates an image indicating an evaluation result by the evaluation unit regarding the farm field indicated by the evaluation target information and displays the image on a display device; Also includes The evaluation system according to claim 1 or 2.

9. 1. A computer implemented evaluation method comprising: An estimation step of obtaining an estimated value of the leaf area of ​​a crop planted in a field to be evaluated for moisture damage based on an aerial image of the field; A calculation step of calculating an ideal value of the leaf area corresponding to the time when the aerial image was taken based on information related to the growing conditions of the crop in the field; an evaluation step of evaluating a state of moisture damage in the field based on the estimated value and the ideal value; (c) evaluation methods;

10. 2. An evaluation program for causing a computer to function as the evaluation system according to claim 1, the evaluation program causing a computer to function as the estimation unit, the calculation unit, and the evaluation unit.

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