WO2024190803A1 - Information processing device, information processing method, and program - Google Patents

Abstract

This information processing device comprising: an adjustment unit that adjusts a region of interest for acquiring information pertaining to crops; an acquisition unit that acquires the information pertaining to the crops in the region of interest; and a generation unit that generates information pertaining to the crops in the whole or a part of a field on the basis of the information pertaining to the crops in one or a plurality of the regions.

Description

Information processing device, information processing method, and program

The present invention relates to an information processing device, an information processing method, and a program.

The use of drones and other devices in agricultural work as part of a smart agriculture system is being considered. For example, Patent Document 1 discloses a technology in which the normalized difference vegetation index (NDVI) of a crop is input into a drone management terminal, the optimal amount of spraying agent is estimated according to a learning model, and spraying information that defines the amount of spraying agent to be applied to the target field is output, causing a spraying drone to spray the spraying agent according to the spraying information.

JP 2021-114271 A

As disclosed in Patent Document 1, there are known attempts to generate NDVI maps for fields and use them in smart agriculture. However, agricultural work involves a variety of tasks, such as sowing seeds, flower thinning, pollination, fruit thinning, fertilization, and disease prevention, and the timing and extent of these tasks cannot be determined by a single index map such as an NDVI map. In addition, the parts of the crops that should be looked at differ depending on the agricultural work, such as whether or not the field is suitable for pollination and whether or not fertilization is required.

Therefore, there is a problem in that the mapping technology from a single perspective, such as that disclosed in Patent Document 1, does not allow for management of farm fields throughout the entire year of agricultural work.

The present invention was made in consideration of the above problems, and aims to provide a technology that can capture a farm field with the desired resolution.

An information processing device according to one aspect of the present invention includes:
an adjustment unit that adjusts an area from which information about agricultural crops is to be acquired;
An acquisition unit that acquires information about the agricultural crops in the target area;
A generation unit generates information about the crops in the entire field or a part of the field based on information about the crops in one or more of the areas.

The present invention provides technology that can capture a field with the desired resolution.

This is a conceptual diagram of a smart agriculture system. FIG. 2 is a diagram illustrating an example of a configuration of a server according to the present embodiment. FIG. 2 is a diagram showing an example of basic farm work data. FIG. 2 is a diagram illustrating an example of farm field data. FIG. 4 is a diagram showing an example of agricultural product data. FIG. 11 is a diagram illustrating an example of work history information. FIG. 13 is a diagram showing a processing flowchart. FIG. 13 is a diagram showing a processing sequence. 1 shows an example of a screen displayed on a display device. 11 shows an example of a screen after the area of the region has been adjusted. An example of the screen when the area is adjusted to a farm field unit is shown.

Below, an embodiment of the present invention (hereinafter referred to as "the present embodiment") will be described in detail with reference to the drawings as necessary, but the present invention is not limited to this, and various modifications are possible without departing from the gist of the invention. Note that in the drawings, the same elements are given the same reference numerals, and duplicated explanations will be omitted. Furthermore, the dimensional ratios of the drawings are not limited to those shown.

1. System Fig. 1 is a schematic diagram showing a smart agriculture system of the present embodiment. As shown in Fig. 1, an example of the smart agriculture system of the present embodiment includes a system that outputs requested information at a desired resolution from a database that records information about agricultural crops in a field in response to a user's operation, and may also include a system that measures the inside of the field at a desired resolution and builds the database.

In a system that outputs the required information at the desired resolution, the target area for acquiring information about agricultural crops is adjusted in response to user operation, information about the agricultural crops in the target area is acquired from a database, and information about the agricultural crops in the entire field or part of the field is generated based on the information about the agricultural crops in one or more areas (hereinafter referred to as the "first embodiment"). Here, the database may be one in which information about the agricultural crops in the field has been recorded in advance. This makes it possible to capture the field at the desired resolution.

In this embodiment, "resolution" refers to the granularity or level of detail of information when it is subdivided or aggregated.

For example, when information on a farm field is output on a map, the amount of flowering that was output in 10 ^m2 units may be divided into 1 ^m2 units and output in more detail, or the amount of flowering that was output in 1 ^m2 units may be aggregated and output in 10 ^m2 units. Also, for example, the amount of flowering in a certain area that was output in one-week units may be divided into one-day units and output in more detail, or the amount of flowering that was output in one-day units may be aggregated and output in one-week units. Furthermore, for example, two-dimensional image data that was output as the amount of flowering in a certain area may be divided into three-dimensional image data and output in more detail, or environmental information such as humidity and temperature may be added to the two-dimensional image data, and the multidimensional data may be divided and output in more detail.

The above is a typical example of "capturing a field at the desired resolution," but "capturing a field at the desired resolution" also includes adjusting the detection target and comparison target according to the user's purpose from the vast amount of observation data of the field, and outputting the desired information. In this case, multiple pieces of information about crops acquired for certain areas of the field may be integrated to generate information about crops in the entire field or in part of it. In this case, in order to capture the field at the desired resolution, the area unit of each area within the field that is the target may be adjusted, and information may be further subdivided or aggregated from other perspectives, such as the time unit for aggregation as described above or other environmental information. This makes it possible to provide a technology that can capture information about crops in the entire field at the resolution required for the desired agricultural work, field management plan, etc.

For example, when predicting yields of agricultural crops, it is preferable to be able to quickly ascertain the number of flowers and fruits that the crops in a field have produced with a certain degree of accuracy. To achieve this, rather than counting the exact number of flowers and fruits that the crops have produced, it is preferable to broadly define the target area for obtaining information about the crops, measure the approximate number of flowers and fruits in each broadly defined area, and output the aggregated data of the approximate number of flowers and fruits in each area from a database, thereby generating information related to the predicted yield of the crops in the entire field or in part of it.

Furthermore, when diagnosing abnormalities in agricultural crops, it is necessary to specifically identify crops that show symptoms of disease on the leaves or fruit, among many other crops that are basically not affected by disease. To achieve this, the area from which information about the crops is acquired is narrowly set, and the leaves and fruits of each narrowly set area, in this case each individual crop, are measured, and integrated data on the condition of the leaves in each area is output from the database, thereby generating information related to abnormality diagnosis of crops in the entire field or part of it. Note that abnormality diagnosis in this embodiment may also include diseases, pests, growth disorders, and other abnormalities.

As described above, the scope of the area from which information about agricultural crops is acquired can change based on information about the purpose of the agricultural work being performed. In other words, the resolution of the field can change.

In the system of this embodiment, each process, such as collecting various types of data, recording the collected data, analyzing the collected data, and providing the analysis results, may be realized by a measuring device or a server installed in the field, or a combination of these, as shown in Figure 1.

FIG. 1 shows a conceptual diagram illustrating the relationship between terminals and sensors used in a field and a cloud server (hereinafter also simply referred to as a "server") in the smart agriculture system of this embodiment. As shown in FIG. 1, the smart agriculture system 1 of this embodiment may include a user terminal 100, a server 200, a measurement device 300, and an operation device 400. The user terminal 100, the server 200, the measurement device 300, and the operation device 400 may be connected via a network N. Below, each configuration will be described in detail assuming that the information processing device of this embodiment is the server 200.

1 measures the inside of the farm field at a desired resolution and constructs a database, and also adjusts a target area in the farm field from which information on the farm crops is to be acquired in response to a user's operation, acquires information on the farm crops in the target area from the database, and generates information on the farm crops in the whole or part of the farm field based on the acquired information on the farm crops in one or more areas. When constructing the database, the server 200 may transmit the adjusted area to each measuring device 300 and acquire information on the farm crops from the measuring device 300.

The server 200 may be a single server that performs the process of measuring the inside of the farm field at the desired resolution and constructing the database, and the process of outputting the requested information at the desired resolution in response to user operations, or these processes may be divided and performed by multiple servers.

In the smart agriculture system 1, the server 200 may be a cloud server or an edge server. The edge server may be installed in or near the field and perform data processing and analysis. This makes it possible to process data on the edge server side without sending it to a cloud server, which reduces communication delays and distributes the processing load. The server 200 may also be a terminal with the same functions as an edge server.

The hardware configuration and functional configuration of the server 200 will be described with reference to FIG. 2A. The server 200 includes, for example, a processor 210, a communication interface 220, an input/output interface 230, a memory 240, a storage 250, and one or more communication buses 260 for interconnecting these components.

The server 200 may be, for example, a desktop, laptop, or other computer. The server 200 may also be a general-purpose computer and may be configured as a single computer, or may be configured as multiple computers distributed over a network N.

Processor 210 executes processes, functions, or methods implemented by code or instructions included in a program stored in storage 250. Processor 210 may include, by way of example and not limitation, one or more central processing units (CPUs), MPUs (Micro Processing Units), GPUs (Graphics Processing Units), microprocessors, processor cores, multiprocessors, ASICs (Application-Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), etc., and may implement the processes, functions, or methods disclosed in each embodiment by logic circuits (hardware) or dedicated circuits formed in integrated circuits (IC (Integrated Circuit) chips, LSIs (Large Scale Integration)), etc.

The processor 210 executes processes, functions, or methods that are realized by code or instructions included in a program stored in the storage 250. As shown in FIG. 2A, the processor 210 of this embodiment may be configured to function as a transmission/reception unit 211, an adjustment unit 212, an acquisition unit 213, a generation unit 214, an evaluation unit 215, a work decision unit 216, a work instruction unit 217, a display control unit 218, and a sensing device management unit 219.

The communication interface 220 transmits and receives various data to and from other devices via the network N. The communication may be performed either wired or wirelessly, and any communication protocol may be used as long as the devices can communicate with each other. For example, the communication interface 220 is implemented as hardware such as a network adapter, various types of communication software, or a combination of these.

The network N may be, by way of example and not limitation, an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a mobile telephone network, Integrated Service Digital Networks (ISDNs), wireless LANs, Long Term Evolution (LTE), Code Division Multiple Access (CDMA), Bluetooth (registered trademark), satellite communications, or any combination thereof. A network may include one or more networks.

The input/output interface 230 includes an input device for inputting various operations to the server 200, and an output device for outputting the results of processing performed by the server 200. For example, the input/output interface 230 includes information input devices such as a keyboard, mouse, and touch panel, and information output devices such as a display. Note that the server 200 may accept a specified input and execute a specified output by connecting an external input/output interface 230.

Memory 240 temporarily stores programs loaded from storage 250 and provides a working area for processor 210. Memory 240 also temporarily stores various data generated while processor 210 is executing a program. Memory 240 may be, for example, a high-speed random access memory such as DRAM, SRAM, DDR RAM, or other random access solid-state storage device, or a combination of these.

Storage 250 stores programs, each functional unit, and various data. Storage 250 may be, for example, one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or non-volatile memories such as other non-volatile solid-state storage devices, or may be a combination of these. Another example of storage 250 is one or more storage devices installed remotely from processor 210.

The transmission/reception unit 211 may function as a transmission unit that transmits various information to other devices such as the user terminal 100 and the measurement device 300 via the communication interface 220 and the network N, or as a reception unit that receives various information from other devices such as the user terminal 100 and the measurement device 300.

For example, the transmission/reception unit 211 may receive information for adjusting the area in the field from which information about agricultural crops is to be acquired, in response to an operation performed by the user on the terminal 100. The transmission/reception unit 211 may also transmit information about agricultural crops in the entire field or part of the field generated by the generation unit to the terminal 100, and the terminal 100 may control the display of this information.

Furthermore, the transmission/reception unit 211 may transmit information regarding the area adjusted by the adjustment unit 212 to the measurement device, and the acquisition unit 213 may acquire information regarding the crops in the target area from the measurement device via the transmission/reception unit 211.

1.1.2 Adjustment Unit The adjustment unit 212 adjusts the area in the field from which information on agricultural crops is to be acquired. Specifically, in response to a user's operation on the terminal 100, the adjustment unit 212 may receive information for adjusting the area in the field from which information on agricultural crops is to be acquired via the transmission/reception unit 211, and adjust the area in the field accordingly. In addition, when constructing a database, the database may be constructed by measuring the inside of the field at a desired resolution using the measuring device 300 or the like and collecting the data.

Here, the area adjusted by the adjustment unit 212 may be defined by a section of the field of approximately equal area, or may be defined by crop.

Defining an area by divisions means, for example, regarding the field as a unit of area as shown in FIG. 1, dividing the field into approximately equal areas, and using the divided divisions as the subject for acquiring information about agricultural crops. The adjustment unit 212 can adjust the size of the area of each divided division, for example, dividing the field into 25, 36, or 49 divisions.

Depending on the shape of the field, it may not be possible to divide the field into sections of equal area. Therefore, the equality of the area of the sections does not need to be strictly determined, and it may be decided in advance how the field will be divided, for example, into 25, 36, or 49 sections.

Defining an area by crop means, for example, regarding a field in terms of crop units rather than area units, and regarding one or multiple crops, the subject of crop-related information acquisition. Here, "one crop" means, for example, a plant plant unit.

When the region is defined by a crop, in addition to the whole of one crop being the target for acquiring information about the crop, a part of a crop, or a plurality of crops, may be the target for acquiring information about the crop. Here, the part of the crop is not particularly limited, but examples include a stem, root, leaf, flower, or fruit. In other words, the adjustment unit 212 may adjust, as the region, the target part of the crop about which the acquisition unit 213 acquires information about the crop.

FIG. 4A shows an example of screen D1 displayed by the display control unit 218 (described later) on the display device of the user terminal 100 when adjusting the area of the field. The display area R1 in FIG. 4A is a map display area, and the map shows the field and areas into which the field is divided into arbitrary sizes. In FIG. 4A, the density of flowers and buds in each area is represented by different shades of color, like a heat map, but the display form of screen D1 is not limited to this.

For example, the user can adjust the target area in the field for obtaining information about crops by selecting a specific area in display area R1 as the target area. In the example of FIG. 4A, four areas are selected as target areas. As a result, information about crops in the target area selected by the user is displayed in display area R2. Specifically, the current number of flowers and buds in the target area may be displayed, and further, the number of flowers and buds that are targeted in the target area and the number of flowers and buds that need to be reduced may be displayed to indicate the necessary work. Furthermore, display area R2 may further display the number of flowers and buds in the entire field.

If the user changes the selection of the target area in display area R1, the information about the target area displayed in display area R2 may also change accordingly.

Furthermore, as shown in FIG. 4A, screen D1 may have a button O1 for adjusting the area to the range of the farm worker's unit work, a button O2 for arbitrarily changing the area size, and a switch button O3 for changing the information about the crops displayed from flowers and buds to flowering rate and disease damage.

FIG. 4B shows an example of the screen after the area of the region has been adjusted by operating button O1 or O2. In this screen, each region is displayed larger by pressing button O2, which changes the region size as desired. In this way, when the area of the region is adjusted by adjustment unit 212 and the target region changes as a result, the information about the target region displayed in display region R2 may also be changed accordingly.

The adjustment unit 212 may adjust the area based on information regarding the purpose of the agricultural work to be performed. In doing so, the adjustment unit 212 may refer to basic agricultural work data 251 that records basic agricultural work methods for each agricultural crop. By referring to such basic agricultural work methods for each agricultural crop, the adjustment unit 212 may estimate the purpose of the agricultural work and, accordingly, information regarding the agricultural crop that should be acquired by the acquisition unit 213, and adjust the appropriate area according to the information regarding the agricultural crop that should be acquired.

Specifically, in addition to changing the information on the agricultural crop to be displayed by operating the switch button O3, the adjustment unit 212 may also adjust the size of the target area according to the information on the agricultural crop to be displayed. For example, if the information on the agricultural crop to be displayed is the number of flowers and buds, it is important to consider how many flowers and buds there are in a wide area of the field, whereas if the information on the agricultural crop to be displayed is about a disease, it is necessary to consider how specifically to identify the plant body that is infected with the disease. Therefore, if the information on the agricultural crop to be displayed is the number of flowers and buds, the adjustment unit 212 may adjust the size of the target area to be relatively wide by operating the switch button O3, whereas if the information on the agricultural crop to be displayed is about a disease, the adjustment unit 212 may adjust the size of the target area to be relatively narrow, on a plant body basis.

As another example, when "fruit" is selected by operating the switch button O3, in addition to changing the information on the agricultural crop to be displayed, the adjustment unit 212 may adjust the size of the target area according to the information on the agricultural crop to be displayed. Generally, in order to obtain fruits that meet quality standards, the upper limit of the number of fruits that can be produced for the number of trees and leaves is roughly determined. Therefore, when there are excessive fruits, a thinning operation is required to adjust the quantity in order to prevent poor quality. However, when viewed from the perspective of the entire vast farm, a human cannot determine whether fruits are dense or sparse in a certain area. Therefore, according to data that associates coordinates in the field with the number of fruits, the adjustment unit 212 can generate a density distribution of fruits in the field, and may adjust the area to an appropriate size to show the density distribution. This makes it possible to standardize the size and quality of agricultural crops produced in the field.

In this case, the display area R2 may display the current number of fruits in the target area and the distribution of fruit sizes, and may further display the desired number of fruits in the target area and the distribution of fruit sizes, as well as the number of fruits and the size of the fruits to be reduced, thereby indicating the necessary work. Note that the above example of fruit thinning also applies to bud and flower removal.

FIG. 4C shows an example of screen D2 displayed by the display control unit 218 (described later) on the display device of the user terminal 100 when the area is adjusted to field units. Reservation information is displayed in a list in the display area R3 of screen D2. The fields displayed on screen D2 may have the same owner or different owners. This makes it possible to grasp the progress of agricultural work between fields and sales forecasts. In addition, material and human resources that can be provided or material and human resources that are required may be displayed as "resources". This enables efficient sharing of resources between fields.

Furthermore, in addition to adjusting the area, the adjustment unit 212 may adjust the time interval when the acquisition unit 213 acquires information about the agricultural crops from a database or from the measurement device 300, and the frequency of such acquisition. For example, if the purpose is yield prediction, the adjustment unit 212 can adjust the frequency of acquiring information about the agricultural crops used in the yield prediction. Such a time axis may also be included as one of the elements that configure the resolution of the field. In this case, the adjustment unit 212 may set the minimum time interval to days when sensing is performed, or may set the period interval by setting a start date and an end date arbitrarily by the user, or may set it in units of weeks, months, quarters, half a year, or one year.

Furthermore, as shown in FIG. 4A, screen D1 may have a slider O4 that sets the time of information output by adjustment unit 212. For example, by operating slider O4, the current flower and bud information displayed in display area R1 or R2 can be changed to past flower and bud information from one day ago or one week ago. Furthermore, if an arbitrary model allows information on future flowers and buds to be output based on past and present flower and bud information, it may be possible to control the display of future flower and bud information by operating slider O4.

The model is not particularly limited, but can be created, for example, by machine learning using training data such as information regarding the passage of time, such as the number of flowers, buds, and fruit in each area over the past few years. This makes it possible to predict the future number of flowers, buds, and fruit based on information such as the past number of flowers, buds, and fruit this year. The above learning may also include environmental data, etc.

FIG. 2B shows an example of basic farm work data 251. In the basic farm work data 251, the "basic farm work ID" is an ID for uniquely identifying information on basic farm work methods for each farm crop. The basic farm work data 251 may also include the type of farm crop, an annual farm work schedule, detailed information on each farm work, and the like. The basic farm work data 251 may also be information created in advance by the farmer.

The annual farm work schedule may record the procedures for farm work throughout the year. This allows you to refer to the dates to understand what farm work needs to be done now and what farm work needs to be done in the future. The record of the procedures for farm work throughout the year is also called the cultivation history.

The detailed information for each agricultural task may include details on how to perform each agricultural task. Furthermore, in addition to the method of performing each agricultural task, information that would be good to obtain when performing that agricultural task may also be recorded.

The information acquired here may include, for example, information that specifies the quantitative aspect of the yield prediction work, that is, by counting the number of flowers, as well as information that specifies the qualitative aspect, such as the shape of the flowers to be counted. The yield prediction work can be carried out with just information that specifies the quantitative aspect, that is, by counting the number of flowers, but since, for example, depending on the shape of a flower, it may not ultimately bear fruit, the accuracy of the yield prediction work can be improved by including information that specifies the qualitative aspect of excluding such flowers from the count.

In addition, since the purpose of disease inspection is to determine the presence or absence of disease, information defining the qualitative aspects for determining the presence or absence of disease may be included. Specific examples of information defining the qualitative aspects for determining the presence or absence of disease include visually perceptible characteristics that appear on the crop body, such as black spots on leaves and brown sap on branches, i.e. characteristics that can be identified by image data, etc. In addition, the rate of increase in black spots on leaves over a certain period of time may be used as a quantitative aspect. This makes it possible to distinguish between "leaves that have black spots but are not diseased" and "leaves that have black spots because they are diseased", and the occurrence of disease can be estimated based on the amount of increase.

In addition to these, there are environmental factors such as the time (period) when disease is likely to occur and weather/environmental conditions (high temperature and humidity) that are likely to cause disease. Therefore, in addition to image data, environmental factors can also be used as a judgment factor for determining whether or not disease is present. In addition, as another environmental factor that can be used in disease inspection, it is also possible to take into account whether or not disease has occurred in surrounding plots, taking into account that disease spreads from tree to tree.

As described above, the information to be acquired varies for each agricultural task, and may include either information defining the quantitative aspect or information defining the qualitative aspect. By referring to such basic agricultural task data 251, the adjustment unit 212 may estimate the purpose of the agricultural task and the information to be acquired related to the agricultural crop according to that purpose, and adjust the appropriate area accordingly.

The adjustment unit 212 may adjust the area according to a user operation, or, if there is no user operation, may refer to field data 252 that defines in advance how one or more areas are determined. By referring to the field data 252, it may be possible to quickly identify divisions, such as when dividing the field into 25, 36, or 49 divisions. Such divisions may be used as the initial values of the area shown in the display area R1, etc.

FIG. 2C shows an example of field data 252. In field data 252, "field ID" is an ID for uniquely identifying information about the field. In field data 252, the allocation of plots when the field is divided into 25, 36, or 49 sections may be recorded in advance. Information about the 25-divided plots may also be specified as information for identifying each plot when the field is divided into 25 sections, such as "25-1", "25-2", etc. Note that field data 252 may be information created in advance by a farmer.

As described below, when the adjustment unit 212 adjusts the area, the acquisition unit acquires information about the crops in each area from the database accordingly, or instructs the measurement device 300 to acquire information about the crops in each area by measurement. When acquiring information from the database, the information about the crops corresponds to location information. When instructing the measurement device 300 to acquire information by measurement, it is desirable to specify from which area the information was acquired. In this regard, when the adjustment unit 212 adjusts the area using the field data 252, the measurement data such as image data acquired by the measurement device 300 may be recorded in the database in association with the coordinates of the plot in the field and the coordinates of the image in the plot. Furthermore, for the image data recorded in the database, the coordinates of the position of the fruit in the image may be recorded by further image processing. This allows information identifying each area to be identified according to the adjusted area, and the acquisition unit 213 can quickly perform the information acquisition work.

In addition, the field data 252 may store various data acquired within the field. The various data acquired within the field may be data acquired by the acquisition unit for each area, or may be data acquired regardless of the area (e.g., environmental data such as temperature).

The acquisition unit 213 may acquire information about the crops in a target area in response to a user's operation from a database or the like that records information about the crops in the field, or may acquire information obtained by measuring the field with the measuring device 300. Specifically, the acquisition unit 213 may acquire information about the crops in the area adjusted by the adjustment unit 212 from a database or the like in response to an operation on the user terminal 100, or may transmit information about the area defined by the adjustment unit 212 to one or more measuring devices 300 via the transmission/reception unit 211, and receive information about the crops in the area from the one or more measuring devices 300 via the transmission/reception unit 211.

In addition, instead of this, if various measurement data within the field has been accumulated in advance in a database, the acquisition unit 213 may refer to the database to acquire information regarding the crops in the area. Note that the database here may be an external database in which weather data and the like are recorded, or it may be the field data 252 or the crop data 253 described below. The database may also be data constructed by collecting environmental data and crop data in the field, and may be data capable of constructing a current or past digital twin of the field.

Furthermore, the acquisition unit 213 may execute both a method of acquiring information about agricultural crops from the measurement device 300 and a method of acquiring information about agricultural crops in an area by referring to a database. For example, when predicting flower blooming, the acquisition unit 213 may acquire information about buds on a crop-by-crop basis from the measurement device 300, and acquire environmental information such as temperature from the database.

The measuring device 300 is a device that measures the crops 520 and plots 530 in the area 510 of the field 500 and acquires information about the crops being cultivated. There are no particular limitations on the measuring device 300, but it may be, for example, a device equipped with various sensors mounted at any position in the field, a drone equipped with various sensors and flying in the field, an unmanned aerial vehicle that propels itself in the field, a smartphone equipped with various sensors, a handheld computing device, a wearable terminal, or other terminal operated by a person.

The acquisition unit 213 may record the acquired information about the agricultural crops in the agricultural crop data 253. Also, as described above, the acquisition unit 213 may record the acquired information about the agricultural crops in the farm field data 252.

FIG. 2D shows an example of crop data 253. In crop data 253, "crop ID" is an ID for uniquely identifying information about the crop. In crop data 253, information about the crop may include location information within the field, imaging information related to the plant or plant body, work history information about the crop in the field, environmental information such as temperature, humidity, weather, and soil condition, evaluation information about the number and quality of flowers, buds, fruits, etc., disease information, etc. Furthermore, information recorded in the database, such as information about the number and quality of flowers, buds, fruits, etc., may be obtained based on imaging information. Specifically, the information may be obtained by counting the fruits captured in the imaging information or calculating their size.

For example, when the adjustment unit 212 prepares an area for acquiring information about agricultural crops, the acquisition unit 213 identifies location information corresponding to the targeted area and acquires information about the agricultural crops corresponding to the location information from a database such as the one described above. Then, the generation unit 214, which will be described later, may generate information about the agricultural crops in the entire field or in part of the field based on the information about the agricultural crops.

FIG. 2E shows an example of work history information. As shown in FIG. 2E, work history information may be recorded for each task. For example, in the case of yield prediction, the yield prediction is entered as the task purpose, and the date and time when the task was performed (May), the area adjusted by the adjustment unit 212 (36 divided plots), information on the crops obtained for each area (number of flowers in plots No. 01 to 36), etc. may be recorded.

Here, the acquisition unit 213 may determine the correspondence between the ranges and numbers (section No. 01 to 36) of each of the 36 divided sections adjusted by the adjustment unit 212 by referring to the farm field data 252. In addition, the acquisition unit 213 may determine the information on agricultural crops to be acquired for each area by referring to the basic farm work data 251.

Furthermore, the crop data 253 may record generation information and evaluation information regarding the crops in the entire field. The generation information regarding the crops in the entire field may be information generated by the generation unit 214 described below on the basis of information regarding the crops in one or more areas. In the example of yield prediction in FIG. 2D, the number of flowers in the entire field or part of the field generated from the number of flowers in each area may be recorded as the generation information.

Furthermore, the evaluation information may be information on the crops evaluated by the evaluation unit 215 described below based on at least one of information on the crops in one or more areas and information on the crops in the entire field or part of it. In the example of the yield prediction in FIG. 2D, the evaluation information may be a prediction of the yield in the entire field or part of it, generated from the number of flowers in each area or the number of flowers in the entire field or part of it.

The acquisition unit 213 may acquire information about agricultural crops using a sensor, or may acquire information entered by another person. Note that the information acquired by the acquisition unit 213 using a sensor also includes information acquired by the acquisition unit 213 by referring to a database in which information acquired by the sensor in advance has been accumulated. Here, the sensor may be at least one of an image sensor, a component sensor, and an environmental sensor.

The image sensor is not particularly limited as long as it is a sensor capable of capturing still or video images. For example, as shown in FIG. 1, the image sensor may be a sensor mounted on a drone, a fixed camera installed in a field, a camera on a terminal such as a wearable device, or a camera installed on a self-propelled measuring device 300.

There are no particular limitations on the component sensor, but examples include sensors that are configured to perform specific analyses such as fluorescent analysis and spectroscopic analysis.

The environmental sensor is a sensor for measuring environmental information in the field of the crops 520. Examples of environmental sensors include, but are not limited to, weather sensors, soil sensors, and gas sensors. The soil sensor may be a sensor that acquires information about the soil, such as the soil moisture content, nutrients, acidity, and underground temperature. The weather sensor may be a sensor that acquires information about the weather, such as temperature, humidity, amount of sunlight, intensity of sunlight, hours of sunlight, amount of rainfall, and weather. The environmental sensor is not limited to soil sensors and weather sensors, and any sensor that can measure various types of environmental information in the field may be used.

1.1.4. Generation unit The generation unit 214 generates information on crops in the entire field or a part of the field based on information on crops in one or more regions. For example, the generation unit 214 may generate information on crops in the entire field or a part of the field based on information on crops in one or more regions, as shown in the display area R1 of FIG. 4A. At this time, the information on crops in the acquired section No. may be mapped based on information on the section recorded in the field data 252 as an initial value or the like. In addition, when a user performs an operation on O1 to O4, etc., information on crops in the entire field or a part of the field may be generated according to the area adjusted by the operation, as shown in FIG. 4B. Furthermore, the generation unit 214 may generate information between fields according to a user operation, as shown in FIG. 4C.

In addition to the above, the generating unit 214 may generate information about the crops in the target area selected by the user according to the adjusted area, as shown in the display area R2. As an example, the generating unit 214 may generate information about the target crop in the target area in addition to the information about the crops in the target area. Specifically, the information shown in the display area R2 may include, in addition to the above, information about the actual crop, such as the growth phase, quantity, size, and presence or absence of disease, information about the condition of the target crop, such as the appropriate amount of flowers and buds, and information about the necessary work, such as the number of flowers to be removed, in the area based on the difference between the information about the condition of the target crop created by the work determining unit 216 described below and the information about the actual crop.

The generating unit 214 may generate information about the crops in the entire field or in part of the field using information acquired by the acquiring unit 213, information evaluated by the evaluating unit 215, information determined by the work determining unit 216, etc. The information generated by the generating unit 214 is transmitted to the user terminal 100 by the display control unit 218 (described later) via the transmitting/receiving unit 211. As a result, screens such as those shown in Figures 4A to 4C are displayed on the display device of the user terminal 100.

In addition, if information about agricultural crops cannot be obtained in a certain area because a sensor is not installed, the generation unit 214 may supplement the information about agricultural crops in that area by estimation. For example, in the case of a soil sensor, when the acidity at two points is obtained, the acidity at a third point can be estimated by extrapolating or interpolating the acidity at the two points to supplement the information. However, the method of supplementation is not limited to this. In addition, in the case of the number of fruits as an example, for example, there are 100 fruits in section No. 1-1 and 150 fruits in section No. 1-3, therefore, although no measurement was made in section 1-2, it is estimated that there are 125 fruits.

Another method would be to estimate based on past performance values in that area and this year's performance values in the field. Specifically, although measurements could not be taken for section 1-2, last year's performance value was 50 fruits, and this year's performance value for the entire field is an average of 120% more fruits than last year, so the number of fruits in section 1-2 could be estimated to be 60.

Otherwise, estimates may be made based on the most recent actual values in that area and the rate of increase or decrease in the surrounding areas. Specifically, a measurement on one day could not measure the number of fruits in section 1-2, but a measurement on the previous day showed that there were 100 fruits in section 1-2, and the number of fruits in the surrounding sections on one day had decreased by 5% overall, so the number of fruits in section 1-2 on that day could be estimated to be 95.

The evaluation unit 215 performs an evaluation of the crop based on at least one of information on the crop in one or more regions and information on the crop in the entire field or a part of the field. The evaluation unit 215 may perform an evaluation of the crop based on the information on the crop, may perform an evaluation of the crop based on the information on the crop and the information on the field, or may perform an evaluation of the crop and an evaluation of the field based on the information on the crop and the information on the field.

As an example of evaluating agricultural crops based on information about the crops, the evaluation unit 215 may evaluate the number of flowers or the number of fruits contained within the range of image data based on image data associated with certain location information. Similarly, the evaluation unit 215 may evaluate the size of the fruits contained within the range of image data based on the image data. This evaluated information may be recorded in a database as one of the data constituting the digital twin.

It may also include an evaluation of the crop, and the difference between the information about the actual crop and the information about the target crop. The work determination unit 216, which will be described later, may determine information about the necessary work, such as the number of flowers to be thinned, in the area, based on the difference between the information about the state of the target crop evaluated by the evaluation unit 215 and the information about the actual crop.

As another example of evaluating agricultural crops based on information about the agricultural crops, the evaluation unit 215 may predict the flowering time, the yield, or estimate the incidence of disease, based on the information about the agricultural crops.

In addition, when making the above evaluation, the evaluation unit 215 may evaluate the crops according to a learning model or algorithm. For example, in a crop yield prediction task, the number of flowers and the number of fruits on the crops are identified as the information to be obtained, and the necessary area is adjusted for this purpose, and the evaluation unit 215 may generate information related to the crop yield prediction for the entire field or part of the field by tallying up approximate values for the number of flowers and fruits that can be counted from the image.

On the other hand, in reality, many of the flowers and fruits on crops are hidden behind the leaves and cannot be seen, and the number of flowers and fruits that can be counted in an image of one field of view often differs from the number of flowers and fruits that actually exist in that field of view. However, if an attempt is made to count such hidden flowers and fruits in detail, it would take up a lot of time and processing power, which may not be considered appropriate. For this reason, in the task of predicting crop yields, the evaluation unit 215 may use a model that associates the number of flowers and fruits that can be counted from an image with the number of flowers and fruits that actually exist when the undersides of leaves in that field of view are examined, thereby more accurately tallying up approximate values for the number of flowers and fruits.

The model is not particularly limited, but can be created, for example, by machine learning using training data that associates image data of crops with the number of fruits actually obtained from the image range. This makes it possible to build a model that estimates the number of fruits not shown in the image data based on the image data. In addition to the image data of crops, the learning described above may also include the number of fruits that can be counted from the image data as training data.

The evaluation unit 215 may record the evaluation regarding the crop and the evaluation regarding the field in the crop data 253.

1.1.6 Work determination unit The work determination unit 216 determines the content of the farm work based on the evaluation of the farm crop. At this time, the work determination unit 216 may determine the content of the farm work based on the evaluation of the farm crop by referring to the basic farm work data 251. For example, when the presence or absence of abnormalities in the leaves of each tree number is detected for the purpose of disease inspection and the disease state is evaluated as the evaluation of the farm crop, the work determination unit 216 may determine the content of the farm work, such as whether to treat some of the trees, prune diseased areas, or prevent disease, according to the disease information.

The work determination unit 216 may also calculate the difference between information about the target crop condition and information about the actual crop, as shown in display area R2 in FIG. 4A. This difference can be said to indicate the amount of agricultural work required. Therefore, the work determination unit 216 may further output a work plan for the area based on this difference. Here, the work plan may be a work plan that includes not only the personnel and work time required for work in that area, but also the order of work and the allocation of personnel not only for that area but also for the entire field.

Specifically, the work determination unit 216 predicts the harvest volume for that season from the number of buds in any area or the entire field, and sets a target value for any area or the entire field. If subsequent measurements show that the actual number of fruit in the same area is greater or less than the target value, the work determination unit 216 may output the amount of work required to bring it closer to the target value. Next, the work determination unit 216 may estimate the necessary personnel from the amount of work, and present the arrangement and order for carrying out the work efficiently, as the above-mentioned work plan.

1.1.7 Work instruction unit The work instruction unit 217 transmits information on the determined content of the agricultural work to the working device 400. The working device 400 is not particularly limited, but may be any device having a mechanism capable of performing agricultural work such as pruning, flower thinning, fruit thinning, pesticide spraying, and pollen spraying. The working device 400 may be the same machine as the measuring device 300, or may be a different machine.

In addition, when the work is performed by a human, the work instruction unit 217 may transmit information regarding the content of the determined agricultural work to a user terminal 100 or the like possessed by the worker.

1.1.8. Display Control Unit The display control unit 218 transmits each piece of information generated by the generation unit 214, the evaluation unit 215, the work determination unit 216, and the work instruction unit 217 to the user terminal 100 via the transmission/reception unit 211, and controls the display on the display device of the user terminal, as shown in FIGS. 4A to 4C.

In addition, if the information processing device of this embodiment is not a server but a terminal directly operated by a user, the display control unit 218 may control the display on an input/output interface 230 such as a display of at least one of the information on the crops in each targeted area acquired by the acquisition unit 213, the information on the crops in the entire field or part of the field generated by the generation unit 214, and the evaluation of the crops by the evaluation unit 215.

1.1.9 Sensing Device Management Unit The sensing device management unit 219 manages the state of the measuring device 300 so that data can be collected normally, and may perform correction or calibration as necessary. For example, the sensing device management unit 219 may manage whether sensing devices such as the measuring device 300 are operating normally, or manage the operating time of the measuring device 300. At this time, if an abnormality is detected, such as a communication error or an alarm being sounded, the sensing device management unit 219 may display this on the display unit of the input/output interface 230.

1.2. Operation Processing Next, the operation of the smart agriculture system will be described. Fig. 3A shows a flowchart of a process of outputting requested information at a desired resolution from a database that records information about agricultural crops in a field in response to a user's operation by the smart agriculture system of this embodiment, and Fig. 3B shows a sequence diagram showing an example of a process of measuring the inside of a field at a desired resolution and constructing a database by the smart agriculture system of this embodiment.

As shown in FIG. 3A, in step A01, in response to a user's operation on the terminal 100, the adjustment unit 212 of the server 200 adjusts the area in the field from which information about agricultural crops is to be acquired. At this time, the adjustment unit 212 adjusts the area based on the purpose of the agricultural work selected by the user and the basic agricultural work data 251, and may also specify the specific content of the information about agricultural crops to be acquired.

In step A02, the acquisition unit 213 of the server 200 acquires information about the agricultural crops in the target area. Specifically, the acquisition unit 213 may acquire information about the agricultural crops in the target area from a database that can configure a digital twin.

In step A03, the generation unit 214 of the server 200 generates information about the crops in the entire field or a part of the field based on the information about the crops in one or more areas. Specifically, based on the information about the crops in the target area acquired by the acquisition unit 213, various information about the target area may be generated, or a map of the entire field may be generated.

For example, as shown in FIG. 4A, when the user taps the switch button O3 to change to flowering rate or disease depending on the purpose of the farm work, the adjustment unit 212 adjusts the area based on the basic farm work data 251, and the generation unit 214 may display a map including an area of the adjusted size in the display area R1 based on information about the crops in one or more areas.

At this time, the adjustment unit 212 may vary the size of the initial area by tapping button O3. Specifically, when the user taps "Flowers and buds" on button O3, the adjustment unit 212 may adjust the area with a resolution suitable for displaying flowers and buds, and a map or the like may be displayed in display area R1 using this as initial information. Also, when the user taps "Disease" on button O3, the adjustment unit 212 may adjust the area with a resolution suitable for displaying disease, and a map or the like may be displayed in display area R1 using this as initial information. At this time, the size of the initial area may vary depending on the information about the crop, such as "Flowers and buds" or "Disease", selected by button O3.

In addition, the user may drag the R1 map to specify the target area, or tap the O2 button or O1 button to expand or narrow the area of the target area or each area to be displayed, and at that time, the adjustment unit 212 may obtain information on the area specified by the user.

Also, as shown in FIG. 4A, when a user selects a specific target area within a field, the adjustment unit 212 adjusts the target area for acquiring information about the crops in response to the user's operation, the acquisition unit 213 acquires information about the crops in the target area from a database that can construct a digital twin, and the generation unit 214 generates and displays information about the crops in the selected target area in the display area R2 based on the information about the crops in one or more areas.

Next, an example of measuring a field using the measuring device 300 and constructing a database that can be used to create a digital twin will be described below.

As shown in FIG. 3B, in step B01, the adjustment unit 212 of the server 200 adjusts the area in the field from which information about agricultural crops is to be acquired. At this time, the adjustment unit 212 adjusts the area based on the purpose of the agricultural work and the basic agricultural work data 251, and may also specify the specific content of the information about agricultural crops to be acquired.

In step B02, the transmitting/receiving unit 211 of the server 200 may transmit to the measuring device 300 information about the area adjusted by the adjustment unit 212 and information about the crops acquired by the sensor. The information transmitted to the measuring device 300 instructs the measuring device 300 on the area to be measured and information about the crops to be measured in that area using a sensor or the like. Based on this instruction information, the measuring device 300 may acquire information about the crops in the area to be measured. Then, in step B03, the transmitting/receiving unit 211 of the server 200 receives the information about the crops acquired for each area from the measuring device 300.

In step B04, the acquisition unit 213 of the server 200 may record the acquired information in a crop ID. In step B05, the generation unit 214 of the server 200 generates information about the crops in the entire field or in part of the field, and in step B06, the evaluation unit 215 of the server 200 performs an evaluation of the crops based on at least one of the information about the crops in one or more areas and the information about the crops in the entire field or in part of the field.

Then, in step B07, the work determination unit 216 of the server 200 determines the work content, and in step B08, the work instruction unit 217 of the server 200 transmits information regarding the determined content of the agricultural work to the work device 400.

1.3. Examples The smart agriculture system will now be further described with reference to specific examples.

1.3.1. Improving the accuracy of agricultural crop yield predictions When agricultural crop yield predictions are done visually, the larger the field, the more likely it is that double counting, omissions, and other errors will occur. By improving the accuracy of agricultural crop yield predictions, it will be possible to create cultivation and supply plans that are efficient and wasteful in response to fluctuations in demand from wholesalers, retailers, restaurants, and other supply destinations.

In this regard, in predicting crop yields using this system, the adjustment unit 212 may adjust the area in a relatively large section as selected by the user or as an initial setting, and the acquisition unit 213 may refer to a database and acquire information about the crops, such as the number of fruits or flowers, in the targeted area. The generation unit 214 may then generate information about the crops in the entire field or in part of it, based on the information about the crops in the area.

The evaluation unit 215 may, for example, evaluate the number of fruits or flowers from the image data. The evaluation may be performed using a model that has been machine-learned using learning data that associates image data with the fruits and flowers of agricultural crops. By performing such an evaluation for each area, the number of fruits or flowers in the entire field or in part of it can be evaluated, and the yield of agricultural crops can be predicted. The evaluation unit 215 may also output the distribution of fruit sizes within the field, and the work determination unit 216 may accordingly determine the necessary work, such as fertilization, to homogenize the size and quality of the fruits.

1.3.2. Reducing production damage caused by disease/abnormalities When detecting disease/abnormalities visually, the larger the field, the more likely it is that there will be oversights and omissions. In particular, when the field is large, it becomes difficult to frequently inspect the condition of the crops in the entire field, so sometimes only representative plants are inspected and diseases and abnormalities in the field are judged based on that. However, it is not possible to predict where disease will occur and spread, not just in representative plants, so such visual judgments are not sufficient. Therefore, in order to reduce production damage caused by disease/abnormalities, it is necessary to detect the crops in the entire field with a resolution close to that of a micron.

In this regard, when using this system to reduce production damage caused by disease/abnormalities, the adjustment unit 212 may adjust the area in relatively small sections or crop units as selected by the user or in the initial settings, and the acquisition unit 213 may acquire and record information about disease/abnormalities and the like in the targeted area as information about the crops. The generation unit 214 may then generate and record information about the crops in the entire field or in part of the field based on the information about the crops in the area.

The evaluation unit 215 may, for example, evaluate whether the crop is diseased/abnormal or normal from the image data. The evaluation may be performed using a model that has been machine-learned using learning data that associates image data with crop diseases/abnormalities. Furthermore, the evaluation unit 215 may further consider information on crop diseases/abnormalities in areas adjacent to the target area in addition to the target area. Since diseases/abnormalities tend to spread to adjacent crops, the evaluation unit 215 can improve the accuracy of disease/abnormality determination in the target area by considering the adjacent areas. Furthermore, the evaluation unit 215 may output warning information about crop diseases/abnormalities in the adjacent areas based on the disease/abnormality determination result in the target area, or may output the distribution of diseases/abnormalities in the field.

In addition, the acquisition unit 213 and generation unit 214 of the server 200 can accumulate information about agricultural crops as data in chronological order, allowing the work decision unit 216 to create a pest control work plan at the appropriate time and place.

1.3.3. Ensuring stable production volume and quality of agricultural crops Ensuring stable production volume and quality of agricultural crops requires appropriate adjustment work according to the condition of the crops. Adjustment work here is not particularly limited, but examples include thinning, fruit removal, leaf removal, top removal, flower removal, etc. Furthermore, examples of the condition of the crops include the condition of the fruit, such as size, fruit weight (estimated value), and color, and the condition of the flowers, such as buds, flowering stage, and pollination state.

Agricultural work to ensure stable production and quality of agricultural crops is carried out by agricultural producers/managers who have the skilled techniques to judge the condition of the crops as described above and make appropriate adjustments accordingly, making each decision and instructing the agricultural workers on the work to be done. Furthermore, when farm fields become larger, it becomes practically difficult to check the individual conditions of all the crops and make decisions on adjustments, so it is common to check only the condition of a representative plant and then make decisions on adjustments for the crops around it. However, if it is not possible to check the individual conditions of the crops and make decisions on adjustments in this way, it is impossible to enlarge farm fields and optimize the guarantee of stable production and quality of agricultural crops at the same time.

In contrast, to ensure stable production volume and quality of agricultural crops using this system, the adjustment unit 212 may adjust the area by relatively small plot or crop unit as selected by the user or in the initial setting, and the acquisition unit 213 may acquire and record information about the agricultural crops in the targeted area, such as the number of fruits, the number of flowers, the leaf area index (LAI), the condition of the fruits, the condition of the flowers, etc. Then, the generation unit 214 may generate and record information about the agricultural crops in the entire field or in part of it, based on the information about the agricultural crops in the area.

In addition, the acquisition unit 213 and generation unit 214 of the server 200 can accumulate information about agricultural crops as data in chronological order, allowing the work decision unit 216 to create adjustment work plans at the appropriate time and place. Furthermore, for example, by counting the number of fruits on a single stalk or tree, the work decision unit 216 can determine agricultural work content that will equalize the quality of the fruit per tree, and the work instruction unit 217 can instruct the execution of that work.

Alternatively, based on the information about the crops obtained as described above, the work decision unit 216 may present the degree of deviation between the representative plant, which is well-maintained, and each area of the field, and determine that adjustment work should be given priority to areas that are not well-maintained.

1.3.4. Improving production volume by improving pollination accuracy To improve production volume by improving pollination accuracy, appropriate pollination work is required in appropriate locations according to the flowering status of the crop. In particular, for some crops, the time during which efficient pollination can be performed after flowering is limited, so finding flowers that are in bloom and quickly carrying out pollination operations on them are important for improving production volume. Furthermore, pollen itself is often expensive, so efficient pollination operations are desired.

However, flowering must be checked thoroughly for each crop, and the flowering and pollination conditions must be assessed for each location to determine where pollination work should be done, so the larger the field, the more likely it is that pollination will be missed.

In contrast, when using this system to increase production volume by improving the accuracy of pollination of agricultural crops, the adjustment unit 212 may adjust the area on a relatively small plot or crop basis, and the acquisition unit 213 may acquire and record the number of flowers, the state of flowers, and other information relating to the agricultural crops in the targeted area. The generation unit 214 may then generate and record information relating to the agricultural crops in the entire field or in part of it, based on the information relating to the agricultural crops in the area.

In addition, the acquisition unit 213 and generation unit 214 of the server 200 can accumulate information about agricultural crops as data in chronological order, allowing the work decision unit 216 to create pollination work plans at the right time and in the right place.

1.3.5. Cost reduction and environmental conservation through pinpoint application of pesticides and fertilizers By applying pinpoint application of pesticides and fertilizers, it is possible to optimize and reduce the amount of pesticides and fertilizers used, which makes it easier to reduce costs and protect the environment. Pinpoint application of pesticides and fertilizers requires work to be done according to the condition of the crops.

To reduce costs and protect the environment by pinpointing pesticide and fertilizer applications on crops using this system, the adjustment unit 212 may adjust the area by relatively small plots or crop units as selected by the user or in the initial settings, and the acquisition unit 213 may acquire and record information about the condition of the fruit and leaves in the targeted area as information about the crops. The generation unit 214 may then generate and record information about the crops in the entire field or part of the field based on the information about the crops in the area.

In addition, the acquisition unit 213 and generation unit 214 of the server 200 can accumulate information about agricultural crops as data in chronological order, allowing the work decision unit 216 to create work plans for spraying pesticides and fertilizers at the right time and in the right place.

Specifically, the work of pesticides and fertilizers may be planned based on the time series changes in the amount and state (buds, flowers, fruits) of agricultural crops in any area acquired by the acquisition unit 213. For example, the amount and location of buds that have decreased since the previous day roughly matches the amount and location of bloomed flowers, so the work determination unit 216 may suggest that pollination work be performed as a priority. In addition, if there is a circumstance in which the probability of fertilization is high within two days of blooming and decreases thereafter, the work determination unit 216 may manage the time from blooming as a parameter and present areas where agricultural work should be performed with priority, such as pollination work being performed on the locations of unpollinated flowers that are on the second day of blooming.

As another example, if fruit thinning work is planned and it is discovered that problems due to pests or diseases have occurred, the work decision unit 216 may output the priority of the procurement of materials for pest control measures, the implementation timing, and adjustment work based on the infection range of the pest or disease acquired by the acquisition unit 213.

2. Information Processing Method In the information processing method of the present embodiment, an information processing device executes the steps of: adjusting a target area for acquiring information about agricultural crops; acquiring information about agricultural crops in the target area; and generating information about agricultural crops in an entire field or a part of a field based on information about the agricultural crops in one or more of the areas.

Note that the specific aspects of the method of this embodiment have been described above in the operational process, so a detailed explanation will be omitted here.

3. Program The program of the present embodiment causes an information processing device to execute the steps of: adjusting a target area for acquiring information about agricultural crops, acquiring information about agricultural crops in the target area, and generating information about agricultural crops in an entire field or a part of a field based on the information about the agricultural crops in one or more of the areas.

The program may be recorded on a readable recording medium. Note that the specific aspects of the processing executed by the program of this embodiment are described above in the operational processing, so a detailed description will be omitted here.

This invention has industrial applicability as a core technology that can be used in smart agriculture systems.

1...system, 200...server, 210...processor, 211...transmitter/receiver, 212...adjustment unit, 213...acquisition unit, 214...generation unit, 215...evaluation unit, 216...work decision unit, 217...work instruction unit, 218...display control unit, 219...sensing device management unit, 220...communication interface, 230...input/output interface, 240...memory, 250...storage, 251...basic farm work data, 252...field data, 253...crop data, 260...communication bus, 300...measuring device, 400...work device, 500...field, 510...area, 520...crop, 530...plot.

Claims (13)

an adjustment unit that adjusts an area from which information about agricultural crops is to be acquired;
An acquisition unit that acquires information about the agricultural crops in the target area;
A generating unit that generates information about the crops in the entire field or a part of the field based on information about the crops in one or more of the areas.
Information processing device. The generation unit generates information on the target crop in the target area in addition to information on the crop in the target area.
The information processing device according to claim 1 . and an evaluation unit that performs an evaluation on the agricultural crop based on at least one of information on the agricultural crop in the one or more areas and information on the agricultural crop in the entire or part of the farm field.
The information processing device according to claim 1 . The assessment of the crop includes a difference between information about the actual crop and information about the target crop.
The information processing device according to claim 3 . The adjustment unit adjusts the area based on information regarding the purpose of the agricultural work to be performed.
2. An information processing device according to claim 1. The region is defined by a section of the field having a substantially uniform area,
The adjustment unit adjusts the area of the partition as the region.
2. An information processing device according to claim 1. The area is defined by the crop,
The adjustment unit adjusts, as the region, a target part of the crop from which the acquisition unit acquires information about the crop.
2. An information processing device according to claim 1. The acquisition unit acquires information about the agricultural crop using a sensor,
The sensor is at least one of an image sensor, a composition sensor, and an environmental sensor.
2. An information processing device according to claim 1. A transceiver unit that transmits information about the region adjusted by the adjustment unit to a measurement device,
The acquisition unit acquires information about the agricultural crops in the target area from the measurement device.
2. An information processing device according to claim 1. A work determination unit that determines the content of farm work based on the evaluation of the farm crop.
The information processing device according to claim 3 . A work instruction unit that transmits information regarding the determined content of the agricultural work to the working device.
The information processing device according to claim 10. An information processing device,
Adjusting an area for which information about crops is to be acquired;
obtaining information about crops in the area of interest;
generating information about the crops in the whole or part of the field based on information about the crops in one or more of the regions;
Information processing methods. In the information processing device,
Adjusting an area for which information about crops is to be acquired;
obtaining information about crops in the area of interest;
generating information about the crops in the whole or part of the field based on information about the crops in one or more of the regions;
program.

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