Specific device, identification method, and program

Abstract

This invention provides a specific device, a specific method, and a specific program that enable more accurate field management. [Solution] The information processing device includes an acquisition unit 211 that acquires image information relating to an image of a predetermined field, a determination unit 212 that determines an image region corresponding to the field within the image based on the acquired image information, and a identification unit 213 that identifies a first evaluation region from the image region that is suitable for evaluating management indicators for managing the field.

Description

Technical Field

[0004] , ,

[0008] , , , , , , , ,

[0006] , , , ,

[0005] , , , , , , ,

[0007] , , ,

[0001] The present invention relates to a specific device, a specific method, and a program.

Background Art

[0002] In agriculture, in order to grow plants such as high-quality and stable high-yield crops, appropriate field management such as topdressing timing, topdressing amount, and lodging reduction measures is required. Producers of plants, etc. manage the field while checking the growth degree of the plants (see, for example, Patent Document 1).

[0003] In recent years, the development of a technique for obtaining vegetation indices such as NDVI (Normalized Difference Vegetation Index) from field images by photographing the field using a camera has been advanced.

Prior Art Documents

[0009] (2) The identification device according to (1) above, wherein the acquisition unit further acquires field information relating to the field, and the determination unit determines the image region based on the image information and the field information.

[0010] (3) The identification unit is the identification device described in (2) above, which identifies the first evaluation area based on at least one of the image information and the field information.

[0011] (4) The identification device according to (2) above, wherein the field information includes at least one of the information relating to the location of the field and the information relating to the shape of the field.

[0012] (5) The identification device according to (1) above, further comprising a calculation unit that calculates the management index based on the acquired image information.

[0013] (6) The calculation unit is the identification device described in (5) above, which calculates the management index based on the image information in the identified first evaluation area.

[0014] (7) The calculation unit is the identification device described in (6) above, which calculates a representative value of the management indicator obtained for each of the unit areas obtained by dividing the first evaluation area into a plurality of unit areas.

[0015] (8) The identification device according to (5) above, wherein the calculation unit calculates the management index for each of the unit regions obtained by dividing the image region into a plurality of unit regions, and the identification unit identifies the first evaluation region based on the calculated management index for each unit region.

[0016] (9) The identification device according to claim (1), wherein the identification unit identifies a second evaluation region from the image region that is unsuitable for evaluating the management indicator, and identifies the image region other than the second evaluation region as the first evaluation region.

[0017] (10) The specific device described in (9) above, wherein the second evaluation area is the image area corresponding to the periphery of the field.

[0018] (11) The specific device described in (10) above, wherein the peripheral portion is the portion within a predetermined distance from the edge of the field.

[0019] (12) The peripheral portion is a portion of the area of ​​the field that is a predetermined proportion of the specific device described in (10) above.

[0020] (13) The second evaluation area is the image area corresponding to the heterogeneous part of the field, as described in (9) above.

[0021] (14) The specified device described in (13) above, wherein the foreign part includes at least one of the water inlet, water outlet, levee, entrance / exit and structure present in the field.

[0022] (15) A method for identification that includes acquiring image information relating to an image of a predetermined field, determining an image region corresponding to the field within the image based on the acquired image information, and identifying a first evaluation region from the image region that is suitable for evaluating management indicators for managing the field.

[0023] (16) A program that causes a computer to function as one of the specific devices described in (1) to (14) above. [Effects of the Invention]

[0024] According to the specific device, specific method, and program of the present invention, a first evaluation region suitable for evaluating management indicators is specified among the image regions corresponding to the field. As a result, the management indicators of the field can be evaluated more accurately. Therefore, it becomes possible to manage the field more precisely.

Brief Description of the Drawings

[0025] [Figure 1] It is a block diagram showing an example of the configuration of a management system according to an embodiment. [Figure 2] It is a diagram showing an example of the usage state of the imaging device shown in FIG. 1. [Figure 3] It is a block diagram showing an example of the configuration of the information processing device shown in FIG. 1. [Figure 4] It is a block diagram showing an example of the functional configuration of the information processing device shown in FIG. 3. [Figure 5] It is a diagram showing an example of the image region determined by the information processing device shown in FIG. 4. [Figure 6] It is a diagram showing an example of the first evaluation region and the second evaluation region specified by the information processing device shown in FIG. 4. [Figure 7] It is a flowchart showing an example of the process executed by the information processing device shown in FIG. 1 etc. [Figure 8] It is a diagram showing an example of the first evaluation region and the second evaluation region specified by the information processing device according to Modification 1. [Figure 9] It is a flowchart showing an example of the process executed by the information processing device according to Modification 2.

Modes for Carrying Out the Invention

[0026] Hereinafter, embodiments of the specific device, specific method, and program of the present invention will be described with reference to the attached drawings. In the figures, the same members are denoted by the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for convenience of explanation and may be different from the actual ratios. <000​​​<Overall configuration of Management System 1> Figure 1 shows an example of the configuration of a management system 1 according to one embodiment of the present invention. The management system 1 is used for managing a field (for example, field FD in Figure 2, described later) using so-called remote sensing. Users of the management system 1 are, for example, field managers, agricultural workers, field operators, or field owners. The management system 1 includes, for example, an imaging device 10 and an information processing device 20. The imaging device 10 and the information processing device 20 are connected, for example, via a network. Here, the information processing device 20 corresponds to a specific example of the particular device of the present invention.

[0028] The imaging device 10 includes a camera, such as a multispectral camera or a hyperspectral camera, and, according to the control of the information processing device 20, captures images of the field in multiple wavelength bands and generates images. The imaging device 10 includes, for example, a visible light measuring unit 10A and an infrared light measuring unit 10B, and generates images of visible light (for example, wavelengths from 400 nm to 700 nm) and infrared light (for example, wavelengths from 800 nm to 1 nm). The visible light measuring unit 10A generates images in, for example, the red wavelength range (around 610 nm), the green wavelength range (around 550 nm), and the blue wavelength range (around 470 nm). The infrared light measuring unit 10B generates images in, for example, the near-infrared wavelength range (around 850 nm). Image information regarding the images of the field captured by the imaging device 10 is sent to the information processing device 20.

[0029] The imaging device 10 includes, for example, a bandpass filter, an imaging optical system, an image sensor, and a digital signal processor (DSP). The bandpass filter selectively transmits light in a predetermined wavelength range (visible light and infrared light range). The imaging optical system forms an optical image of the light transmitted through the bandpass filter onto a predetermined imaging plane. The image sensor has a light-receiving surface at the same position as the imaging plane of the imaging optical system and converts the optical image in the predetermined wavelength range into an electrical signal. The digital signal processor processes the electrical signal output from the image sensor to generate image information related to the field image.

[0030] Figure 2 shows an example of how a field field FD is photographed using the imaging device 10. The imaging device 10 is mounted on an autonomously flying unmanned aerial vehicle A, such as a drone, and photographs the field field FD and the plants P growing in the field field FD from above. By photographing the field field FD from above in this way, it is possible to photograph the entire field field FD quickly and easily. The imaging device 10 may photograph a portion of a single field FD, and these images may be stitched together to form a single image of the field FD. The image information relating to the image of the field FD taken by the imaging device 10 includes, for example, shooting location information relating to the location where the image was taken. Shooting location information can be included in the image information by using, for example, GPS (Global Positioning System). Shooting location information includes, for example, information on the longitude, latitude, and altitude of the location where the imaging device 10 took the image of the field FD. The image information includes, for example, shooting location information embedded in TIFF format.

[0031] The information processing device 20 is, for example, a computer such as a PC (Personal Computer) or a tablet terminal. This information processing device 20 controls, for example, the imaging of field FD by the imaging device 10, and evaluates management indicators for managing field FD based on image information related to the images of field FD captured by the imaging device 10.

[0032] Management indicators are indicators that can be calculated from image information of field FD, and include, for example, vegetation indicators that represent the vegetation state of field FD. Vegetation indicators are indicators devised with the aim of understanding the state of vegetation using a simple calculation formula that takes advantage of the characteristics of light reflection by plants P, and represent the amount and vitality of plants P. Vegetation indices include, for example, NDVI, GNDVI (Green Normalized Difference Vegetation Index), mNDVI (modified NDVI), RVI (Ratio Vegetation Index), DVI (Difference Vegetation Index), TVI (Transformed Vegetation Index), IPVI (Infrared Percentage Vegetation Index), SR (Simple Ratio), SGR (Specific Growth Rate), PRI (Photochemical Reflectance Index), RGR (Relative Growth Rate), NPCI (Normalized Pigment Chlorophyll Index), SRPI (Simple Ratio Pigment Index), NPQI (Normalized Phaeophytinization Index), SIPI (Structure-Insensitive Pigment Index), PI1, PI2, PI3, or PI4. Management indices may also be indicators representing the state of plant P, specifically, the size of the leaves, the number of leaves, the degree of leaf overlap, or the size of the stem. For example, management indicators may include vegetation cover, leaf area, leaf age, or plant height. Depending on the condition of the field FD, management indicators may also be indicators related to the work performed on the field FD, such as indicators representing the amount of fertilizer applied to the field FD, the timing of fertilization, the risk of lodging, the amount and timing of use of lodging prevention agents, etc. Management indicators may also be indicators related to the soil fertility of the field FD.

[0033] Figure 3 is a block diagram showing an example of the schematic configuration of the information processing device 20. The information processing device 20 includes, for example, a CPU (Central Processing Unit) 21, ROM (Read Only Memory) 22, RAM (Random Access Memory) 23, storage 24, a communication interface 25, and an operation display unit 26. Each component is connected to the others via a bus 27 so as to be able to communicate with each other.

[0034] The CPU 21 controls each of the above configurations and performs various calculations according to the programs recorded in the ROM 22 and storage 24. The specific functions of the CPU 21 will be described later.

[0035] ROM22 stores various programs and data.

[0036] RAM23 is used as a working area to temporarily store programs and data.

[0037] Storage 24 stores various programs, including the operating system, and various data. For example, storage 24 stores field information relating to field FD. The field information includes, for example, at least one of the following: information relating to the location of field FD and information relating to the shape of field FD. The field information includes, for example, information relating to the latitude and longitude of multiple locations along the contour of field FD. For example, if field FD has a rectangular planar shape, the field information includes information relating to the latitude and longitude of at least four vertices of this rectangle. The field information may be information registered using, for example, a map information system or a smart agriculture system, or information processed from a map information system or a smart agriculture system. Alternatively, the field information may be information obtained by surveying. The field information may also include information relating to the size of field FD, information relating to plants P growing in field FD, or information relating to the manager of field FD.

[0038] The storage 24 may have an application installed for calculating management indicators from image information of the field FD. The storage 24 may also store image information related to images of the field FD previously taken by the imaging device 10.

[0039] The communication interface 25 is an interface for communicating with other devices. Various wired or wireless communication interfaces can be used as the communication interface 25. The communication interface 25 is used, for example, to receive image information and field information from the field FD from the imaging device 10 or an external device, or to transmit shooting conditions to the imaging device 10.

[0040] The operation display unit 26 is composed of a touch panel including, for example, a display unit such as an LCD (liquid crystal display) or an organic EL display and a touch sensor. A display unit for displaying various information and an operation unit for receiving various user operations may be provided separately. In this case, the display unit may consist of the above-mentioned display, viewer software, or a printer, and the operation unit may consist of a touch sensor and a pointing device such as a mouse or a keyboard.

[0041] <Functions of the Information Processing Device 20> Figure 4 is a block diagram showing the functional configuration of the information processing device 20. The information processing device 20 functions as an acquisition unit 211, a determination unit 212, a specification unit 213, a calculation unit 214, and an output unit 215, by having the CPU 21 read a program stored in the storage 24 and execute processing.

[0042] The acquisition unit 211 acquires image information relating to images taken of a predetermined field (for example, field FD). The acquisition unit 211 acquires image information from, for example, the imaging device 10. The acquisition unit 211 may also acquire image information from the storage 24 or an external storage device. The image information acquired by the acquisition unit 211 includes, for example, shooting location information relating to the location where the field FD was photographed.

[0043] The acquisition unit 211 further acquires field information relating to field FD. The acquisition unit 211 acquires field information from, for example, storage 24. The field information includes, as described above, at least one of the following: information relating to the location of field FD and information relating to the shape of a given field. The acquisition unit 211 may also acquire field information from an external system such as a map information system or a smart agriculture system.

[0044] The determination unit 212 determines the image region corresponding to the field FD within the image in which the field FD was photographed, based on the image information acquired by the acquisition unit 211. For example, the determination unit 212 determines the image region based on field information acquired by the acquisition unit 211 in addition to the image information. Specifically, it determines the image region by associating the shooting position information included in the image information with information regarding the location of the field FD or information regarding the shape of the field FD included in the field information.

[0045] Figure 5 shows an example of an image Im taken of a rectangular field FD. The determination unit 212 determines the image region 30 corresponding to the field FD within the image Im, for example, based on image information related to the image Im and field information related to the field FD.

[0046] The determination unit 212 may determine the image region without using field information. In this case, the determination unit 212 may determine the image region using, for example, machine learning. The determination unit 212 can determine the image region using, for example, an automatic field information generation function that uses deep learning techniques such as semantic segmentation.

[0047] The identification unit 213 identifies a first evaluation area from the image area determined by the decision unit 212. The first evaluation area is an area of ​​the image area suitable for evaluating the management indicator. The identification unit 213 identifies the first evaluation area by, for example, identifying a second evaluation area that is different from the first evaluation area. The second evaluation area is an area of ​​the image area that is not suitable for evaluating the management indicator. As will be described in detail later, in this embodiment, since the first evaluation area is identified from the image area by the identification unit 213 in this way, the management indicator for field FD can be evaluated more accurately even when the image area includes a second evaluation area that is not suitable for evaluating the management indicator. Therefore, it becomes possible to manage the field more accurately. The first evaluation area and the second evaluation area (first evaluation area 31 and second evaluation area 32 in Figure 6) identified by the identification unit 213 will be described below with reference to Figure 6.

[0048] Figure 6 shows an example of a first evaluation region 31 and a second evaluation region 32 identified from the image region 30 by the identification unit 213. The identification unit 213 identifies, for example, the image region 30 corresponding to the periphery of the field FD as the second evaluation region 32, and the image region 30 other than the second evaluation region 32, i.e., the central part of the image region 30, as the first evaluation region 31. The second evaluation region 32 is provided in a frame shape within the image region 30, for example, and the first evaluation region 31 is provided inside the second evaluation region 32. This second evaluation region 32 corresponds, for example, to the ridge of the field FD.

[0049] The periphery of the field FD is, for example, the portion below a predetermined distance from the edge of the field FD. The periphery of the field FD may also be a predetermined percentage of the area of ​​the field FD. When the field FD is, for example, about 30m x 100m in size, the predetermined distance is, for example, about 2m to 3m, and the predetermined percentage is, for example, about 5% to 15%. The identification unit 213 may adjust the size or shape of the second evaluation area 32 according to the size or shape of the field FD. The identification unit 213 identifies the second evaluation area 32, for example, based on at least one of image information and field information.

[0050] The identification unit 213 may specify the entire image region 30 as the first evaluation region 31. For example, when the area of ​​the field FD is smaller than a predetermined area, the identification unit 213 specifies the entire image region 30 as the first evaluation region 31. This makes it possible to maintain the size of the first evaluation region 31 to be greater than or equal to a predetermined size.

[0051] Alternatively, when the area of ​​the field FD is larger than a predetermined area, the identification unit 213 identifies the entire image area 30 as the first evaluation area 31. As the field FD increases, the proportion of the area occupied by the ridges relative to the area of ​​the field FD often decreases. Therefore, even if the entire image area 30 is identified as the first evaluation area 31, the error in the management indicators caused by the ridges becomes smaller.

[0052] The calculation unit 214 calculates a control index based on the image information acquired by the acquisition unit 211. Here, the calculation unit 214 calculates the control index based on the image information in the first evaluation area 31 identified by the identification unit 213. For example, the calculation unit 214 calculates a control index for each unit area obtained by dividing the first evaluation area 31 into multiple unit areas, and then calculates a representative value of the control index in the first evaluation area 31. A unit area is, for example, a pixel. Representative values ​​are, for example, the mean, median, mode, standard deviation, or processed values ​​thereof. For example, the calculation unit 214 may divide the first evaluation area 31 into multiple areas according to the control index calculated for each pixel, and calculate a representative value for each of these areas.

[0053] The calculation unit 214 may calculate management indicators based on image information in the first evaluation area 31 and the second evaluation area 32 identified by the identification unit 213. In this case, the calculation unit 214 may, for example, weight the first evaluation area 31 and the second evaluation area 32 so that the importance of the first evaluation area 31 is higher than that of the second evaluation area 32, and calculate representative values ​​for management indicators in field FD.

[0054] The output unit 215 outputs indicator information related to the management indicators calculated by the calculation unit 214. The indicator information includes, for example, representative values ​​of the management indicators in the first evaluation area 31. The output unit 215 outputs the indicator information by, for example, displaying these representative values ​​on the operation display unit 26. For example, the average value of NDVI is output as the representative value.

[0055] The indicator information may include maps created based on the management indicators calculated by the calculation unit 214. For example, the output unit 215 outputs a fertilization map based on the amount of fertilizer applied calculated for each area of ​​the first evaluation area 31. At this time, the output unit 215 sets a predetermined value for the management indicator in the second evaluation area 32 and outputs the map. The predetermined value for the management indicator is, for example, zero. The predetermined value for the management indicator may be the average, mode, median, minimum, or maximum value of the management indicators calculated for each unit area of ​​the first evaluation area 31 or the second evaluation area 32.

[0056] The output unit 215 may output indicator information for multiple years. For example, the output unit 215 may output historical indicator information along with current indicator information. This makes it easier for users to compare historical and current indicator information. Alternatively, the output unit 215 may output future indicator information predicted based on current or historical indicator information.

[0057] <Overview of processing by the information processing device 20> The processing performed by the information processing device 20, that is, the method by which the information processing device 20 performs the specified processing, will be described in detail below.

[0058] Figure 7 is a flowchart showing the procedure of processing performed in the information processing device 20. The processing of the information processing device 20 shown in the flowchart of Figure 7 is stored as a program in the storage 24 of the information processing device 20 and is executed by the CPU 21 controlling each part.

[0059] (Step S101) The information processing device 20 first acquires image information and field information related to a predetermined field (for example, a field FD) that has been photographed. For example, the information processing device 20 acquires image information from the imaging device 10 and field information from the storage device 24.

[0060] (Step S102) The information processing device 20 determines the image region (for example, the image region 30 in Figure 5) that corresponds to the field FD within the image, based on the image information and field information acquired in step S101.

[0061] (Step S103) The information processing device 20 identifies a second evaluation region (for example, the second evaluation region 32 in Figure 6) from the image region determined in the processing of step S102. The second evaluation region is, for example, an image region corresponding to the periphery of the field.

[0062] (Step S104) The information processing device 20 identifies the image region other than the second evaluation region identified in step S103 as the first evaluation region (for example, the first evaluation region 31 in Figure 6). The first evaluation region is, for example, the image region corresponding to the central part of the field.

[0063] (Step S105) The information processing device 20 calculates a control index based on the image area of ​​the first evaluation region identified in step S104. The information processing device 20, for example, calculates a control index for each unit region obtained by dividing the first evaluation region into multiple unit regions, and then calculates a representative value of the control index in the first evaluation region. A unit region is, for example, a pixel.

[0064] (Step S106) The information processing device 20 outputs indicator information related to the management indicators calculated in step S105 and then terminates the process. For example, the information processing device 20 outputs indicator information by displaying the representative value of the management indicators in the first evaluation area calculated in step S105 on the operation display unit 26.

[0065] <Effects of the Information Processing Device 20 and Management System 1> In the information processing device 20 and management system 1 of this embodiment, a first evaluation area 31 suitable for evaluating management indicators is identified from the image area 30 corresponding to the field FD. This allows for more accurate evaluation of the management indicators of the field FD. Therefore, it becomes possible to manage the field FD more effectively. The effects of this will be explained in detail below using comparative examples.

[0066] Field managers can manage their fields using management indicators such as NDVI calculated from field images. For field management, for example, representative values ​​of the management indicators calculated for each field are used. The information processing device in the comparative example, for example, uses location information included in the field information to determine the image region corresponding to the field in the image, and calculates representative values ​​of the management indicators based on this image region.

[0067] However, fields contain a mixture of areas where plants are growing normally and areas where plants are barely growing. Areas where plants are barely growing include, for example, the edges of the field, such as the levees, and are not suitable for evaluating management indicators. If the image area includes such areas that are not suitable for evaluating management indicators, these areas will become noise, and there is a risk that it will not be possible to calculate appropriate representative values ​​for management indicators.

[0068] In contrast, the management system 1 and information processing device 20 according to this embodiment determine the image region 30 corresponding to the field FD within the image Im, and then identify a first evaluation region 31 suitable for evaluating the management indicator from this image region 30. In other words, when the image region 30 includes a portion unsuitable for evaluating the management indicator, i.e., a second evaluation region 32, the second evaluation region 32 and the first evaluation region 31 are distinguished. This makes it possible to calculate a representative value of the management indicator for each field FD using, for example, only the first evaluation region 31, or by reducing the contribution of the second evaluation region 32 compared to the first evaluation region 31. Therefore, it becomes possible to perform agricultural work such as fertilizing the field FD according to this representative value of the management indicator, and to manage the field FD more accurately. For example, in fields where the evaluation of the management indicator is relatively low, measures for improvement can be taken.

[0069] The following describes modified versions of the information processing device 20 described in the above embodiment. In order to avoid repetition, detailed explanations of configurations similar to those described in the above embodiment of the information processing device 20 will be omitted.

[0070] [Example 1] Figure 8 shows an example of the first evaluation area 31 and the second evaluation area 32 identified by the information processing device 20 according to Modification 1.

[0071] The information processing device 20 may identify the image region 30 corresponding to the heterogeneous part of the field FD as the second evaluation region 32. The heterogeneous part of the field FD is a part in which the state of plants differs significantly from the rest of the field FD, for example, a part in which plants are not growing. Examples of heterogeneous parts of the field FD include the water inlet, water outlet, levees, entrances and exits, and structures present in the field. Examples of entrances and exits include the entrance and exit of agricultural machinery, etc. Examples of structures include steel towers, etc. The heterogeneous part of the field FD may also be a part in which the state of plant growth differs significantly from the rest of the field FD. After identifying the second evaluation region 32, the information processing device 20 identifies the image region 30 other than the second evaluation region 32 as the first evaluation region 31.

[0072] The information processing device 20 identifies the image region 30 corresponding to the heterogeneous area as the second evaluation region 32, for example, based on the image information. The information processing device 20 may identify the second evaluation region 32 using machine learning, or it may identify the second evaluation region 32 based on predetermined rules. The information processing device 20 may identify the second evaluation region 32 using statistical processing or the like. The information processing device 20 may identify the second evaluation region 32 based on field information, or it may identify the second evaluation region 32 based on both image information and field information. The information processing device 20 may identify the image region 30 corresponding to the periphery and heterogeneous areas of the field as the second evaluation region 32.

[0073] Similar to the embodiment described above, this information processing device 20 also identifies a first evaluation area 31 suitable for evaluating management indicators from among the image areas 30 corresponding to field FD. This allows for more accurate evaluation of field FD management indicators. Therefore, it becomes possible to manage field FD more effectively.

[0074] [Differentiation 2] Figure 9 is a flowchart showing the procedure for processing performed in the information processing device 20 according to Modification 2. The processing of the information processing device 20 shown in the flowchart of Figure 9 is stored as a program in the storage 24 of the information processing device 20 and is executed by the CPU 21 controlling each part.

[0075] (Steps S201, S202) The information processing device 20 performs steps S201 and S202 in the same manner as steps S101 and S102 described in the above embodiment. Specifically, after acquiring image information and field information, the information processing device 20 determines an image region (for example, image region 30 in Figure 5) that corresponds to a predetermined field (for example, field FD) within the image (for example, image Im in Figure 5).

[0076] (Step S203) The information processing device 20 calculates a management index for each of the multiple unit regions into which the image region determined in step S102 is divided. A unit region is, for example, a pixel.

[0077] (Step S204) The information processing device 20 identifies a first evaluation area and a second evaluation area based on the management indicators for each unit area calculated in the process of step S203. For example, the information processing device 20 identifies unit areas with management indicators greater than a predetermined value as the first evaluation area, and unit areas with management indicators less than or equal to the predetermined value as the second evaluation area.

[0078] (Steps S205, S206) The information processing device 20 performs steps S205 and S206 in the same manner as steps S105 and S106 described in the above embodiment, and then terminates the process.

[0079] Similar to the embodiment described above, this information processing device 20 also identifies a first evaluation area 31 suitable for evaluating management indicators from among the image areas 30 corresponding to field FD. This allows for more accurate evaluation of field FD management indicators. Therefore, it becomes possible to manage field FD more effectively.

[0080] As described above, the specific apparatus, method, and program of the present invention have been explained in embodiments and modifications. However, it goes without saying that the present invention can be appropriately added to, modified, and omitted by those skilled in the art within the scope of its technical concept.

[0081] For example, the plant P in the above embodiment may be rice, soybeans, adzuki beans, or wheat, etc.

[0082] Furthermore, although the above embodiments describe an example in which the information processing device 20 identifies a first evaluation area 31 and a second evaluation area 32 from the image area 30, the information processing device 20 may identify three or more evaluation areas from the image area depending on their importance in evaluating the management indicators.

[0083] Furthermore, the information processing device 20 may combine the methods for identifying the first evaluation area 31 and the second evaluation area 32 described in the above embodiment, modification 1, and modification 2, respectively.

[0084] Furthermore, the processing units in the flowcharts of the above embodiments are divided according to the main processing content in order to facilitate understanding of each process. The present invention is not limited by the way the processing steps are classified. Each process can be further divided into more processing steps. Also, one processing step may perform even more processes.

[0085] The means and methods for performing various processing tasks in the systems described above can be implemented by either dedicated hardware circuits or a programmed computer. The program may be provided, for example, on a computer-readable recording medium such as a flexible disk or CD-ROM, or it may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable recording medium is usually transferred to and stored in a storage unit such as a hard disk. Furthermore, the program may be provided as a standalone application software, incorporated into the software of the device as a function of the system, or provided as a cloud service. [Explanation of Symbols]

[0086] 1 Management system, 10 Imaging device, 10A visible light measurement section, 10B Infrared light measurement section, 20 Information Processing Equipment, 21 CPUs, 211 Acquisition Department; 212 Decision Section, 213 Specific Department; 214 Calculation unit, 215 Output section, 22 ROMs, 23 RAM, 24 storage, 25 communication interfaces, 26 Operation display section.

Claims

1. An acquisition unit that acquires image information related to images taken of a designated field, A determination unit that determines the image region corresponding to the field within the image based on the acquired image information, A special unit identifies a first evaluation area within the aforementioned image area that is suitable for evaluating management indicators for managing the field. A specific device equipped with the following features.

2. The acquisition unit further acquires field information relating to the field, The determination unit determines the image region based on the image information and the field information, as described in claim 1.

3. The identification device according to claim 2, wherein the identification unit identifies the first evaluation area based on at least one of the image information and the field information.

4. The identification device according to claim 2, wherein the field information includes at least one of information relating to the location of the field and information relating to the shape of the field.

5. The identification device according to claim 1, further comprising a calculation unit that calculates the management index based on the acquired image information.

6. The identification device according to claim 5, wherein the calculation unit calculates the management index based on the image information in the identified first evaluation area.

7. The identification device according to claim 6, wherein the calculation unit calculates a representative value of the management indicator obtained for each of the unit areas into which the first evaluation area is divided.

8. The calculation unit calculates the management index for each of the unit regions into which the image region is divided, The identification device according to claim 5, wherein the identification unit identifies the first evaluation area based on the calculated management index for each unit area.

9. The identification device according to claim 1, wherein the identification unit identifies a second evaluation region from the image region that is unsuitable for evaluating the management indicator, and identifies the image region other than the second evaluation region as the first evaluation region.

10. The identification device according to claim 9, wherein the second evaluation area is the image area corresponding to the periphery of the field.

11. The identifiable device according to claim 10, wherein the peripheral portion is the portion at or below a predetermined distance from the edge of the field.

12. The specific device according to claim 10, wherein the peripheral portion is a predetermined proportion of the area of ​​the field.

13. The identification device according to claim 9, wherein the second evaluation area is the image area corresponding to the heterogeneous part of the field.

14. The identifying device according to claim 13, wherein the heterogeneous part includes at least one of a water inlet, water outlet, levee, entrance / exit, and structure present in the field.

15. To acquire image information related to images taken of a designated field, Based on the acquired image information, the image region corresponding to the field within the image is determined, To identify a first evaluation area from the aforementioned image area that is suitable for evaluating management indicators for managing the field. A specific method including

16. A program that causes a computer to function as a specific device as described in any of claims 1 to 14.

Images