EP4681527A1

EP4681527A1 - Information processing device, information processing method, and program

Info

Publication number: EP4681527A1
Application number: EP24770908.2A
Authority: EP
Inventors: Yusuke Nara; Takahiro Ohgushi; Masayuki Sugioka; Yutaro KATO
Original assignee: Omron Corp; Omron Tateisi Electronics Co
Current assignee: Omron Corp
Priority date: 2023-03-15
Filing date: 2024-03-13
Publication date: 2026-01-21
Also published as: AU2024234980A1; WO2024190803A1; JPWO2024190803A1

Abstract

An information processing device including an adjustment unit configured to adjust a target region for acquisition of information on a crop; an acquisition unit configured to acquire the information on the crop in the target region; and a generation unit configured to generate the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

TECHNICAL FIELD

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

BACKGROUND OF INVENTION

As a smart agriculture system, it has been considered to utilize a drone or the like for agricultural work. For example, Patent Document 1 discloses a technique of inputting a normalized difference vegetation index (NDVI) of a crop to a drone management terminal, estimating an optimal value of a spray amount of a spray agent in accordance with a learning model, and outputting spray information defining the spray amount of the spray agent to be applied to a field that is a spraying target, thereby causing a spraying drone to spray the spray agent in accordance with the spray information.

CITATION LIST

PATENT LITERATURE

Patent Document 1: JP 2021-114271 A

SUMMARY

TECHNICAL PROBLEM

As disclosed in Patent Document 1, an attempt to generate an NDVI map in a field and utilize the NDVI map for smart agriculture is known. However, there is various work such as sowing, flower thinning, pollination, fruit thinning, fertilization, and disease control in agricultural work, and a timing of the work and a degree of the work cannot be determined by a map of a single index such as the NDVI map. Further, portions of the crop to be observed vary depending on differences in the agricultural work, such as whether or not the crop is suitable for pollination and whether or not the crop is to be fertilized.
Therefore, a mapping technology from a single viewpoint as disclosed in Patent Document 1 has a problem that the management of the field throughout an entirety of the agricultural work of one year cannot be realized.
The present invention has been made in view of the above problems, and an object of the present invention is to provide a technology capable of capturing a field with a desired resolution.

SOLUTION TO PROBLEM

An information processing device according to an aspect of the present invention includes:

an adjustment unit configured to adjust a target region for acquisition of information on a crop;
an acquisition unit configured to acquire the information on the crop in the target region; and
a generation unit configured to generate the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the present invention, it is possible to provide a technology capable of capturing a field with a desired resolution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual diagram of a smart agriculture system.
FIG. 2A is a diagram illustrating an example of a configuration of a server in the present embodiment.
FIG. 2B is a diagram illustrating an example of basic agricultural work data.
FIG. 2C is a diagram illustrating an example of field data.
FIG. 2D is a diagram illustrating an example of crop data.
FIG. 2E is a diagram illustrating an example of work history information.
FIG. 3A is a diagram illustrating a processing flowchart.
FIG. 3B is a diagram illustrating a processing sequence.
FIG. 4A is an example of a screen to be displayed on a display device.
FIG. 4B is an example of a screen after an area of the region is adjusted.
FIG. 4C is an example of a screen when a region has been adjusted in field units.

DESCRIPTION OF EMBODIMENTS

Hereinafter, 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 thereto, and various variations can be made without departing from the gist thereof. In the drawings, the same elements are denoted by the same reference numerals, and redundant description will be omitted. Further, dimensional ratios in the drawings are not limited to ratios shown in the drawings.

1. System

FIG. 1 is a diagram illustrating a smart agriculture system according to the present embodiment. As illustrated in FIG. 1, an example of the smart agriculture system of the present embodiment may include a system that outputs information required at a desired resolution from a database that records the information on crops in a field in accordance with a user operation, and may further include a system that measures the inside of a field at a desired resolution and constructs the database.
In the system that outputs information required at a desired resolution, a target region for acquisition of the information on the crop is adjusted in accordance with a user operation, the information on the crops in the target region is acquired from a database, and the information on the crops in the entire field or part of the field is generated based on the information on the crops in one or more regions (hereinafter referred to as a "first embodiment"). Here, the database may be a database in which information on the crop in the field is recorded in advance. This makes it possible to capture the field with a desired resolution.
In the present embodiment, the "resolution" is the granularity or a level of detail of information when the information is subdivided into small pieces or the information is aggregated.
For example, this means that, when information of the field is output as a map, the amount of flowering that was previously output in 10 m² units is divided into 1 m² units and output in finer detail, or the amount of flowering that was previously output in 1 m² units is aggregated and output in 10 m² units. Further, for example, the outputting means that the amount of flowering in a certain region, which has been output on a weekly basis, is subdivided and output on a daily basis, or the amount of flowering, which has been output on a daily basis, is aggregated and output on a weekly basis. Further, for example, the outputting means that two-dimensional image data output as the amount of flowering in a certain region is subdivided into three-dimensional image data and output in detail, or is subdivided into multidimensional data obtained by further adding environmental information such as humidity or temperature to two-dimensional image data and output in detail.
The above is a typical example of "capturing the field with a desired resolution", and "capturing the field with a desired resolution" includes adjusting a detection target or a comparison target in accordance with a purpose of a user from among a large amount of observation data of the field and outputting a result of the adjustment as required information. In this case, a plurality of pieces of the information on the crop acquired for a partial region of the field may be integrated to generate the information on the crop in the entire field or part of the field. In this case, in order to capture the field with a desired resolution, the information may be subdivided or aggregated from other viewpoints such as the time unit for aggregating as described above or other environmental information, in addition to adjusting an area unit of each region in the field that is a target in the field. Accordingly, it is possible to provide a technology capable of capturing the information on the crop in the entire field with a resolution required for target agricultural work, a field operation plan, or the like.
For example, when a yield of a crop is predicted, it is preferable to be able to quickly ascertain the number of flowers or the number of fruits attached to the crop in the field with a certain degree of accuracy. To this end, it is preferable to generate information on yield prediction of the crop in the entire field or part of the field by setting a large target region for acquisition of the information on the crop, measuring an approximate value of the number of flowers or the number of fruits in each region set to be large, and outputting data obtained by aggregating the approximate value of the number of flowers or the number of fruits in each region from the database, rather than perfectly counting the number of flowers or the number of fruits attached to the crop.
Further, when an abnormality of a crop is diagnosed, it is necessary to specifically specify a crop in which symptoms of a disease appear in leaves, fruits, or the like among a large number of crops that are not basically affected by the disease. To this end, the target region for acquisition of the information on the crop is set to be small, leaves and fruits in each region set to be narrow, in this case, individual crops are measured, and data obtained by integrating information on a state of leaves in each region is output from the database, thereby generating information on abnormality diagnosis of a crop in the entire field or a part thereof. The abnormality diagnosis in the present embodiment may include diseases, pests, growth disorder, and other abnormalities.
As described above, a range of the target region for acquisition of the information on the crop may change based on information on the purpose of the agricultural work to be executed. That is, the resolution of the field may vary.
Processes such as collecting various pieces of data, recording the collected data, analyzing the collected data, and providing an analysis result in the system of the present embodiment may be realized by a measurement device or a server installed in the field, or a combination thereof, as illustrated in FIG. 1.
FIG. 1 is a conceptual diagram illustrating a relationship between terminals and sensors used in a field and a cloud server (hereinafter, also simply referred to as a "server") in a smart agriculture system of the present embodiment. As illustrated in FIG. 1, a smart agriculture system 1 of the present embodiment may include user equipment 100, a server 200, a measurement device 300, and a work device 400. The user equipment 100, the server 200, the measurement device 300, and the work device 400 may be connected via a network N. Hereinafter, each configuration will be described in detail assuming that the information processing device of the present embodiment is the server 200.

1.1. Server

The server 200 illustrated in FIG. 1 measures the inside of the field with a desired resolution to construct a database, adjusts the target region for acquisition of the information on the crop in the field in accordance with a user operation, acquires the information on the crop in the target region from the database, and generates the information on the crop in the entire field or part of the field based on the acquired the information on the crop in one or more regions. When the database is constructed, the server 200 may transmit the adjusted region to each measurement device 300 and acquire the information on the crop from the measurement device 300.
The server 200 may execute a process of measuring the inside of the field with a desired resolution and constructing the database with a single server and a process of outputting information required with a desired resolution in accordance with a user operation, or may execute these processes separately by a plurality of servers.
In the smart agriculture system 1, the server 200 may be a cloud server or may be an edge server. The edge server may be installed in the field or around the field to perform data processing and analysis. Accordingly, since the data is not transmitted to the cloud server and the processing is performed on the edge server side, a delay in communication is less likely to occur, and the processing load can be distributed. The server 200 may be a terminal having a function similar to that of an edge server or the like.
A hardware configuration and function 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 and 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 another computer. The server 200 is a general-purpose computer, and may be configured of one computer or may be configured of a plurality of computers distributed on the network N.
The processor 210 executes a process, a function, or a method realized by a code or an instruction included in a program stored in the storage 250. The processor 210 includes, for example, without limitation, one or more central processing units (CPUs), micro processing units (MPUs), graphics processing units (GPU)s, microprocessors, processor cores, multiprocessors, application-specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs), and may realize processes, functions, or methods disclosed in the embodiments by logic circuits (hardware) or dedicated circuits formed in an integrated circuit (IC) chip, large scale integration (LSI)), and the like.
The processor 210 executes a process, a function, or a method realized by a code or an instruction included in a program stored in the storage 250. As illustrated in FIG. 2A, the processor 210 of the present 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 determination 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 pieces of data to and from other devices via the network N. The communication may be performed in a wired or wireless manner, and any communication protocol may be used as long as mutual communication can be performed. For example, the communication interface 220 is implemented as hardware such as a network adapter, various types of communication software, or a combination thereof.
The network N may be, for example, without 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), part of the Internet, part of a public switched telephone network (PSTN), a mobile phone network, integrated service digital networks (ISDNs), wireless LANs, long term evolution (LTE), code division multiple access (CDMA), Bluetooth (registered trademark), or satellite communication, or may be a combination thereof. The network may include one or more networks.
The input and output interface 230 includes an input device that inputs various operations to the server 200 and an output device that outputs a processing result processed by the server 200. For example, the input and output interface 230 includes an information input device such as a keyboard, a mouse, and a touch panel, and an information output device such as a display. The server 200 may receive a predetermined input or may execute a predetermined output by connecting an external input and output interface 230.
The memory 240 temporarily stores a program loaded from the storage 250 and provides a work area to the processor 210. The memory 240 also temporarily stores various pieces of data generated while the processor 210 is executing the program. The memory 240 may be a high-speed random access memory, such as a DRAM, SRAM, DDR RAM or another random access solid state storage devices, or may be a combination thereof.
The storage 250 stores a program, each functional unit, and various pieces of data. The storage 250 may be, for example, a non-volatile memory such as one or more magnetic disk storage devices, optical disc storage devices, flash memory devices, or other non-volatile solid state storage devices, or may be a combination thereof. Other examples of the storage 250 may include one or more storage devices remotely located from the processor 210.

1.1.1. Transmission/Reception Unit

The transmission/reception unit 211 may function as, for example, a transmission unit that transmits various types of information to other devices such as the user equipment 100 or the measurement device 300 via the communication interface 220 and the network N, or a reception unit that receives various types of information from other devices such as the user equipment 100 or the measurement device 300.
For example, the transmission/reception unit 211 may receive information for adjusting the target region for acquisition of the information on the crop in the field in accordance with an operation performed on the terminal 100 by the user. The transmission/reception unit 211 may transmit the information on the crop in the entire field or part of the field generated by the generation unit to the terminal 100, and the terminal 100 may control a display of the information.
Further, the transmission/reception unit 211 may transmit information on the region adjusted by the adjustment unit 212 to the measurement device, and the acquisition unit 213 may acquire the information on the crop in the target region from the measurement device via the transmission/reception unit 211.

1.1.2. Adjustment Unit

The adjustment unit 212 adjusts the region in the field, which is the target of acquisition of the information on the crop. Specifically, in response to a user operation performed on the terminal 100, information for adjusting the target region for acquisition of the information on the crop in the field may be received via the transmission/reception unit 211, and the adjustment unit 212 may adjust the region in the field accordingly. Further, when the database is constructed, the database may be constructed by measuring the inside of the field with a desired resolution using the measurement device 300 or the like and collecting measurement results.
Here, the region adjusted by the adjustment unit 212 may be defined in a section having substantially a uniform area of the field, or may be defined by the crop.
Defining the region by sections means, for example, that the field is ascertained in units of area, the field is divided into sections having substantially equal area, and the divided sections are used as targets of acquisition of the information on the crop, as illustrated in FIG. 1. The adjustment unit 212 can adjust a size of an area of each section into which the field is divided, such as dividing the field into 25, 36, or 49 segments.
Depending on a shape of the field, the field may not be divided into sections having equal areas. Therefore, it is not necessary to strictly determine the uniformity of the area of the sections, and what kind of section division is to be made in cases in which the field is divided into 25, 36, or 49 segments s, for example, may be determined in advance.
Defining the region in the crop means, for example, that the field is captured in units of crops rather than in units of areas, and one or more crops are set as targets for acquiring the information on the crop. Here, "one crop" refers, for example, to a plant on a per-stalk unit.
Further, when the region is defined by the crop, the entire crop may be the target of acquisition of the information on the crop, or part of the crop may be the target of acquisition of the information on the crop among one or more crops. Here, the part of the crop is not particularly limited, and examples thereof may include stems, roots, leaves, flowers, fruits, and the like. That is, the adjustment unit 212 may adjust, as the region, a target portion of the crop from which the acquisition unit 213 acquires the information on the crop.
FIG. 4A illustrates a display example of a screen D1 that is displayed on the display devices of the user equipment 100 by the display control unit 218 described later, when the region in the field is adjusted. A display region R1 of FIG. 4A is a display region of a map, and a field, and areas obtained by dividing the field into arbitrary sizes are shown in the map. In FIG. 4A, densities of flowers and buds in each region are represented by a difference in color shade as in a heat map, but a display mode of the screen D1 is not limited thereto.
For example, the user can adjust the target region for acquisition of the information on the crop in the field by selecting a specific region as the target region in the display region R1. In the example of FIG. 4A, four regions are selected as the target regions. Accordingly, the information on the crop in the target region selected by the user is displayed in a display region R2. Specifically, the current number of flowers and buds in the target region may be displayed, and further, the number of flowers and buds targeted in the target region and the number of flowers and buds to be reduced may be displayed, thereby displaying the necessary work. The display region R2 may further display the number of flowers and buds in the entire field.
When the user changes the selection of the target region in the display region R1, the information on the target region displayed in the display region R2 may be changed accordingly.
Further, as illustrated in FIG. 4A, the screen D1 may have a button O1 for adjusting the region to a range of a unit of work of an agricultural worker, a button O2 for freely changing a region size, a switch button O3 for changing the information on the crop to be displayed from flowers and buds to a flowering rate or disease, and the like.
FIG. 4B shows an example of a screen after the area of the region is adjusted according to an operation on the button O1 or O2. In this screen, each area is displayed in a large size by the button O2 for arbitrarily changing the region size. Thus, when the adjustment unit 212 adjusts the area of the region and the target region is changed accordingly, the information on the target region displayed in the display region R2 may be changed accordingly.
The adjustment unit 212 may adjust the region based on the information on the purpose of the agricultural work to be executed. In this case, the adjustment unit 212 may refer to a basic agricultural work data 251 in which a basic agricultural work method is recorded for each crop. The adjustment unit 212 may refer to such a basic agricultural work method for each crop to estimate the purpose of the agricultural work and the information on the crop to be acquired by the acquisition unit 213 in accordance with the purpose, and adjust an appropriate region in accordance with the information on the crop to be acquired.
To be specific, in addition to changing the information on the crop to be displayed, the adjustment unit 212 may adjust the size of the target region in accordance with the information on the crop to be displayed, in response to the operation on the switch button O3. For example, when the information on the crop to be displayed is the number of flowers and buds, a viewpoint of how many flowers and buds are present when the field is viewed in a wide area is important, whereas when the information on the crop to be displayed is a disease, a viewpoint of specifically specifying a plant body affected by the disease is required. Therefore, when the information on the crop to be displayed is the number of flowers and buds, the adjustment unit 212 adjusts the size of the target region that is relatively wide through an operation on the switch button O3, whereas when the information on the crop to be displayed is disease, the adjustment unit 212 may adjust the size of the target region that is relatively narrow and is in a plant body unit.
Further, as another example, when "fruit" is selected by an operation on the switch button O3, the adjustment unit 212 may adjust the size of the target region in accordance with the information on the crop to be displayed, in addition to changing the information on the crop to be displayed. In general, in order to obtain a fruit satisfying a quality criterion, the upper limit number of fruits that can be produced is approximately determined with respect to the number of trees and leaves. Therefore, when there are too many fruits, fruit thinning work for adjusting the quantity is required in order to prevent quality defects. However, when viewed from the entire large farm, a person cannot determine whether the fruit is dense or sparse in a certain region. Therefore, according to the data in which the coordinates in the field and the number of fruits are associated with each other, the adjustment unit 212 can generate a density distribution of the fruits in the field, and may adjust a region having a size appropriate for indicating the density distribution. This makes it possible to make the size or quality of the crop produced in the field uniform.
In this case, in the display region R2, a current number of fruits or a size distribution of the fruits in the target region may be displayed, and the number of fruits and a size distribution of the target fruits in the target region, and the sizes and the number of fruits to be reduced may be displayed so that necessary work is displayed. The example of the fruit thinning also applies to bud thinning and flower thinning.
FIG. 4C shows a display example of a screen D2 that is displayed on the display device of the user equipment 100 by the display control unit 218 to be described later, when the region is adjusted on a field basis. In the display region R3 of the screen D2, information on the reservation is displayed in a list. An owner of the field displayed in the screen D2 may be the same or different. This makes it possible to ascertain the progress of agricultural work between fields or the sales prospect. Further, as the "resource" or the like, a physical/human resource that can be provided or a physical/human resource that is desired may be displayed. This enables efficient sharing of resources between fields.
Further, in addition to the adjustment of the region, the adjustment unit 212 may adjust a time interval at which the acquisition unit 213 acquires the information on the crop from the database or acquires the information from the measurement device 300, a frequency of the acquisition, and the like. For example, in the case of aiming at yield prediction, the adjustment unit 212 can adjust the frequency of acquisition of the information on the crop used for yield prediction. Such a time axis may also be included in one of the elements constituting the resolution of the field. In this case, the adjustment unit 212 may set a minimum time interval in units of days of sensing, may set a period interval by setting a day as a start point and a day as an end point as desired by the user, or may set the minimum time interval in units of weeks, months, quarters, or half a year/one year.
Further, as illustrated in FIG. 4A, the screen D1 may have a slider O4 for setting a time of the information output by the adjustment unit 212. For example, according to an operation on the slider O4, the current flower and bud information displayed in the display region R1 or R2 can be changed to past flower and bud information one day ago, one week ago, or the like. Further, when future flower and bud information can be output based on the current and past flower and bud information using an arbitrary model, display of the future flower and bud information may be controlled according to an operation on the slider O4.
The model is not particularly limited, but can be created, for example, by performing machine learning using information on the passage of time of the number of flowers, the number of buds, the number of fruits, and the like in each region in the past several years as teacher data. This makes it possible to predict the number of flowers, the number of buds, and the number of fruits that will be expected in the future, based on information such as the number of flowers, the number of buds, and the number of fruits in the past of this year. The learning may further include environmental data and the like.
An example of the basic agricultural work data 251 is shown in FIG. 2B. In the basic agricultural work data 251, a "basic agricultural work ID" is an ID for uniquely specifying information on a basic agricultural work method for each crop. Further, the basic agricultural work data 251 may include a type of crop, an annual agricultural work schedule, detailed information for all agricultural work, and the like. The basic agricultural work data 251 may be information created by the agricultural worker in advance.
The annual agricultural work schedule may record a procedure of agricultural work throughout the year. Thus, agricultural work to be done now and agricultural work to be done in the future can be ascertained by referring to the date. The record of a procedure of the agricultural work throughout the year is also referred to as a cultivation history.
The detailed information for all agricultural work may include details of a method of performing all agricultural work. Further, information that is preferably acquired when the agricultural work is performed may be recorded together with a method of performing all agricultural work.
The information to be acquired here may include, for example, information that defines a quantitative aspect that yield prediction work is performed by counting the number of flowers, and information that defines a qualitative aspect such as a form of the flowers to be counted. The yield prediction work can be executed only with the information defining the quantitative aspect of being performed by counting the number of flowers but, for example, since fruits may not be produced depending on shapes of the flowers, the accuracy of the yield prediction work is further improved by including the information defining a qualitative aspect in which such flowers are excluded from the counting.
Further, since the presence or absence of a disease is an object of a disease inspection, information that defines a qualitative aspect for determining the presence or absence of a disease may be included. Specific examples of the information defining the qualitative aspect for determining the presence or absence of a disease include visually recognizable features appearing in a crop body, such as black spots of leaves and brown sap of branches, that is, features that can be specified by image data or the like. Further, as the quantitative aspect, an increase rate of black spots of leaves in a certain period may be used. This makes it possible to distinguish between "leaves that are not diseased but have black spots" and "leaves that are diseased and therefore have black spots", and to estimate the occurrence of disease based on an amount of increase.
Further, there are environmental factors such as a time (period) when a disease easily occurs, and weather/environmental conditions (high temperature and high humidity) in which a disease easily occurs. Therefore, in addition to the image data, the environmental factors can also be determination factors for determining the presence or absence of a disease. Further, as other environmental factors that may be used for disease inspection, it may be considered that the disease is spread from tree to tree, and it may be considered whether the disease is present in a surrounding section.
As described above, the information to be acquired differs per type of agricultural work, and may include either one of the information defining the quantitative aspect or the information defining the qualitative aspect. The adjustment unit 212 may estimate the purpose of the agricultural work and the information on the crop to be acquired in accordance with the purpose by referring to the basic agricultural work data 251, and adjust the appropriate region accordingly.
The adjustment unit 212 may adjust the region according to an operation of the user, or may refer to the field data 252 in which a method of determining one or more regions is determined in advance when there is no operation of the user. Sections may be promptly specified in the cases in which the field is divided into 25, 36, or 49 segments by referring to the field data 252. Such a section can be used, for example, as an initial value of the region shown in the display region R1.
An example of field data 252 is illustrated in FIG. 2C. In the field data 252, the "field ID" is an ID for uniquely specifying information of the field. In the field data 252, the allocation of the sections in the cases in which the field is divided into 25, 36, or 49 segments may be recorded in advance. Further, as information on 25-division sections, information for specifying each section in division into 25 sections, for example, "25-1", "25-2",.... may be defined. The field data 252 may be information created by the farmer in advance.
As will be described later, when the adjustment unit 212 adjusts the region, the acquisition unit acquires the information on the crop in each region from the database in accordance with the adjustment, or instructs the measurement device 300 to acquire the information on the crop in each region through measurement. When the information is acquired from the database, the information on the crop is associated with the point information. Further, when the information is acquired through measurement in the measurement device 300 in response to an instruction, it is desirable to specify from which region the information is acquired. In this regard, when the adjustment unit 212 adjusts the region using the field data 252, the measurement data such as the image data acquired by the measurement device 300 may be recorded in the database in association with coordinates of the section in the field and coordinates of the image in the section. Further, coordinates of the actual position in the image may be recorded by image processing on the image data recorded in the database. Accordingly, information for specifying each region can also be specified according to the adjusted region, and the acquisition work of the information in the acquisition unit 213 can be quickly executed.
Further, various pieces of data acquired in the field may be accumulated in the field data 252. Various pieces of data acquired in the field may be data acquired by the acquisition unit for each region or may be data acquired regardless of the region (for example, environmental data such as temperature).

1.1.3. Acquisition Unit

The acquisition unit 213 may acquire the information on the crop in the target region in accordance with a user operation from the database or the like that records the information on crops in a field, or may acquire information obtained by the measurement device 300 measuring the inside of the field. Specifically, the acquisition unit 213 may acquire the information on the crop in the region adjusted by the adjustment unit 212 from the database or the like in accordance with an operation on the user equipment 100, or may transmit the information on the region defined by the adjustment unit 212 to one or more measurement devices 300 via the transmission/reception unit 211 and receive the information on the crop in the region from one or more measurement devices 300 via the transmission/reception unit 211.
Further, when various types of measurement data in the field are accumulated in a database in advance, the acquisition unit 213 may acquire the information on the crop in the region by referring to the database. Here, the database may be an external database in which weather data and the like are recorded, or may be the field data 252 or crop data 253 to be described later. Further, the database may be data that is configured by collecting data of an environment in the field and data of the crop, which is data that can constitute a current or past digital twin of the field.
Further, the acquisition unit 213 may execute both a method of acquiring the information on the crop from the measurement device 300 and a method of acquiring the information on the crop in the region by referring to the database. For example, in the prediction of flowering, the acquisition unit 213 may acquire information on the buds of the crop from the measurement device 300 and acquire environmental information such as temperature from the database.
The measurement device 300 is a device that measures a crop 520 or a section 530 in the region 510 of a field 500 and acquires information on the crop being cultivated. The measurement device 300 is not particularly limited, and may be, for example, a device including various sensors provided at any position in the field, a drone including various sensors and flying in the field, an unmanned vehicle that travels in the field, a smartphone including various sensors, a handheld computer device, or a terminal operated by a person such as a wearable terminal.
The acquisition unit 213 may record the acquired the information on the crop in the crop data 253. As described above, the acquisition unit 213 may record the acquired the information on the crop in the field data 252.
An example of the crop data 253 is illustrated in FIG. 2D. In the crop data 253, "crop ID" is an ID for uniquely specifying the information on the crops. Position information in the field, imaging information related to the crop or a plant body, work history information on the crop in the field, environmental information such as temperature, humidity, weather, and soil conditions, evaluation information on the number or quality of flowers, buds, fruits, and the like, disease information, and the like are recorded as the information on the crop in the crop data 253. Further, information recorded in the database, such as information on the number or quality of flowers, buds, fruits, and the like, may be obtained based on the imaging information. Specifically, the information may be information obtained by counting the number of fruits shown in the imaging information or calculating the size of the fruits.
For example, when the adjustment unit 212 adjusts the region for acquiring the information on the crop, the acquisition unit 213 specifies position information corresponding to the target region and acquires the information on the crop corresponding to the position information from the database as described above. The generation unit 214 to be described later may generate the information on the crop in the entire field or part of the field based on the information on the crop.
An example of the work history information is further illustrated in FIG. 2E. As illustrated in FIG. 2E, the work history information may be recorded per type of work. For example, in the case of the yield prediction, the yield prediction is described as a work purpose, and a work date and time (May) when the work is performed, a region (36 divided sections) adjusted by the adjustment unit 212, the information on the crop acquired for each region (the number of flowers of section Nos. 01 to 36), and the like may be recorded.
Here, the acquisition unit 213 may determine the association with the range or the number (section Nos. 01 to 36) of each of the 36 divided sections adjusted by the adjustment unit 212 by referring to the field data 252. Further, the acquisition unit 213 may determine the information on the crop to be acquired for each region by referring to the basic agricultural work data 251.
Further, generation information and evaluation information on the crops in the entire field may be recorded in the crop data 253. The generated information on the crop in the entire field may be information obtained by the generation unit 214 to be described later generating the information on the crop in the entire field or part of the field based on the information on the crop in one or more regions. In the example of the yield prediction of FIG. 2D, the number of flowers in the entire field or part of the field generated from the number of flowers in each region may be recorded as the generation information.
Further, the evaluation information may be information obtained by the evaluation unit 215 to be described later evaluating the crop based on at least one of the information on the crop in one or more regions or the information on the crop in the entire field or part of the field. In the example of the yield prediction of FIG. 2D, the prediction of the yield in the entire field or part of the field generated from the number of flowers in each region or the number of flowers in the entire field or part of the field may be recorded as the evaluation information.
The acquisition unit 213 may acquire the information on the crop using a sensor or may acquire information input by another person. The information to be acquired by the acquisition unit 213 using the sensor includes information to be acquired by the acquisition unit 213 by referring to a database in which information acquired by the sensor is accumulated in advance. Here, the sensor may be at least one of an image sensor, a component sensor, or an environment sensor.
The image sensor is not particularly limited as long as the image sensor is a sensor capable of capturing a still image or a moving image. The image sensor may be, for example, a sensor mounted on a drone, a fixed-point camera installed in a field, a camera of a terminal such as a wearable device, or a camera installed in a self-propelled measurement device 300, as illustrated in FIG. 1.
The component sensor is not particularly limited, and examples thereof may include a sensor configured to be capable of performing, for example, a predetermined analysis such as a fluorescence analysis or a spectroscopic analysis.
The environment sensor is a sensor for measuring environmental information in the field of the crop 520. The environment sensor is not particularly limited, and examples thereof may include a weather sensor, a soil sensor, and a gas sensor. The soil sensor may be a sensor that acquires information on soil such as the amount of moisture, nutrients, acidity, and underground temperature of the soil. Further, the weather sensor may be a sensor that acquires information on weather, such as temperature, humidity, amount of sunshine, sunshine intensity, sunshine hours, amount of rain, and weather. The environment sensor is not limited to the soil sensor and the weather sensor, and any sensor capable of measuring various types of environmental information in the field may be applied.

1.1.4. Generation Unit

The generation unit 214 generates the information on crops in the entire field or part of the field based on the information on the crop in one or more regions. For example, the generation unit 214 may generate the information on the crops in the entire field or part of the field based on the information on the crop in one or more regions, as described in the display region R1 in FIG. 4A. In this case, as an initial value or the like, the acquired information on the crop in the section No. may be mapped based on the information on the section recorded in the field data 252. Further, when the user performs an operation on, for example, O1 to O4, information on the crop in the entire field or part of the field may be generated according to a region adjusted by the operation as illustrated in FIG. 4B. Further, the generation unit 214 may generate information between fields in accordance with a user operation as illustrated in FIG. 4C.
In addition to the above, the generation unit 214 may generate the information on the crop in the target region selected by the user according to the adjusted region, as shown in the display region R2. As an example, the generation unit 214 may generate the information on the target crop in a target region, in addition to the information on the crops in the target region. In particular, the information shown in the display region R2 may include, in addition to the above, information on an actual crop such as growth phase, quantity, size, and presence or absence of disease, information on a state of a target crop such as appropriate amounts of flowers or buds, and information on necessary work such as the number of flowers to be thinned in the region from a difference between the information on the state of the target crop and the information on the actual crop, which is created by the work determination unit 216 to be described later.
The generation unit 214 may generate the information on the crops in the entire field or part of the field using the information acquired by the acquisition unit 213, the information evaluated by the evaluation unit 215, the information determined by the work determination unit 216, and the like. The display control unit 218, which will be described later, transmits the information generated by the generation unit 214 to the user equipment 100 via the transmission/reception unit 211. As a result, screens as illustrated in FIGS. 4A to C are displayed on the display devices of the user equipment 100.
Further, when there is a portion in which the information on the crops is not obtained in a certain region, for example, due to a sensor not being installed, the generation unit 214 may complement the information on the crop in the region through estimation. For example, in the case of a soil sensor, there may be a method of estimating the acidity at a third point through extrapolation or interpolation of the acidity at two points and complementing the acidity, when the acidity at the two points is obtained. However, the complementing method is not limited thereto. Further, in the case of the number of fruits, for example, complementation such as estimating that the number of fruits of the section No 1-1 is 100, the number of fruits of the section No 1-3 is 150, and therefore, the number of fruits of a section 1-2 is 125 although the number of fruits of the section 1-2 cannot be measured is considered.
Further, as another method, the number may be estimated according to a past actual value in the region and an actual value of this year in the field. Specifically, although the section 1-2 could not be measured, it is considered that the number of fruits is 50 in an actual value of the last year, and the number of fruits in the section 1-2 is estimated as 60 since an actual value of this year is 120% more fruits on average as a whole than the last year in the entire field.
Further, the number may be estimated according to the latest actual value in the region and a rate of increase and decrease in a surrounding region. Specifically, the number of fruits in the section 1-2 cannot be measured in a measurement on a certain day, but it is considered that, since the number of fruits in the section 1-2 is 100 in a measurement on a previous day, and the number of fruits in a surrounding section on a certain day is reduced by 5% as a whole, the number of fruits in the section 1-2 on a certain day is estimated as 95.

1.1.5. Evaluation Unit

The evaluation unit 215 evaluates the crop based on at least one of the information on the crop in one or more regions or the information on the crop in the entire field or part of the field. The evaluation unit 215 may perform evaluation regarding the crop based on the information on the crop, may perform evaluation regarding the crop based on the information on the crop and the information on the field, or may perform evaluation regarding the crop and evaluation regarding the field based on the information on the crop and the information on the field.
As an example of evaluating the crop based on the information on the crop, the evaluation unit 215 may evaluate the number of flowers or the number of fruits included in the range of the image data from the image data associated with certain position information. Similarly, the evaluation unit 215 may evaluate, from the image data, sizes of the fruits included in the range of the image data. The evaluated information may be recorded in a database as one of pieces of data constituting a digital twin.
Further, an evaluation of the crop, and a difference between the information on the actual crop and the information on the target crop are included. The work determination unit 216 to be described later may determine information on necessary work such as the number of flowers to be thinned in the region from a difference between the information on the state of the target crop evaluated by the evaluation unit 215 and the information on the actual crop.
Further, as an example of performing evaluation regarding the crop based on the information on the crop, the evaluation unit 215 may predict a flowering time, predict a yield, estimate a disease occurrence rate, or the like based on the information on the crop.
In the evaluation, the evaluation unit 215 may perform evaluation regarding the crop in accordance with a learning model or an algorithm. For example, in the crop yield prediction work, the number of flowers or the number of fruits attached to the crop are specified as information to be acquired, a region necessary for this is adjusted, and the evaluation unit 215 may generate the information on the crop yield prediction in the entire field or part of the field by aggregating an approximate value of the number of flowers or fruits that can be counted from the image.
On the other hand, in practice, flowers or fruits attached to the crop are often hidden behind leaves and cannot be seen, and the number of flowers or fruits that can be counted in an image of one field of view is often different from the number of flowers or fruits that are actually present in the field of view. However, when it is attempted to count up to such hidden flowers or fruits in detail, much time and processing capacity are required, and this may not be said to be appropriate. Therefore, in the crop yield prediction work, the evaluation unit 215 may aggregate the approximate values of the numbers of flowers or fruits with higher accuracy using a model in which the number of flowers or fruits that can be counted from the image is associated with the number of flowers or fruits that is actually present when the back of the leaf or the like is examined in the field of view.
The model is not particularly limited, but can be created by, for example, performing machine learning using teacher data in which image data of the crop is associated with the number of fruits actually obtained from an image range thereof. This makes it possible to construct a model for estimating the number of the fruits that are not shown in the image data based on the image data. In the learning, in addition to the image data of the crop, the number of fruits that can be counted from the image data may be included as teacher data.
The evaluation unit 215 may record the evaluation regarding the crop or 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 agricultural work based on the evaluation regarding the crop. In this case, the work determination unit 216 may determine the content of the agricultural work based on the evaluation regarding the crop with reference to the basic agricultural work data 251. For example, when the presence or absence of an abnormality in leaves of each tree No is detected for the purpose of disease inspection and a disease situation is evaluated as the evaluation regarding the crop, the work determination unit 216 may determine the content of the agricultural work such as whether to treat some trees, whether to prune a portion affected by the disease, or whether to prevent the disease, depending on the disease information.
Further, the work determination unit 216 may calculate a difference between the information on the state of the target crop and the information on the actual crop, as shown in the display region R2 in FIG. 4A. The difference may be indicative of the amount of agricultural work required. Therefore, the work determination unit 216 may further output a work plan in the region based on the difference. Here, the work plan may be a work plan including an order of work and an arrangement of personnel not only in the region but also in the entire field, in addition to the personnel or work time required for work in the region.
Specifically, the work determination unit 216 predicts a harvest amount of the season from the number of buds in any region or the entire field, and sets a target value in any region or the entire field. When an actual measurement value of the number of the fruits in the same region is larger or smaller than the target value through the subsequent measurement, the work determination unit 216 may output a work amount required to reach the target value. Next, the work determination unit 216 may estimate the number of required workers from the work amount and present the arrangement and order for efficiently performing the work, as the 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 work device 400. The work device 400 is not particularly limited, and is not particularly limited as long as the work device includes a mechanism capable of executing agricultural work such as pruning, flower thinning, fruit thinning, pesticide spraying, and pollen spraying. The work device 400 may be the same machine as the measurement device 300 or may be a different machine.
Further, when a human performs work, the work instruction unit 217 may transmit information on content of the determined agricultural work to the user equipment 100 or the like possessed by the worker.

1.1.8. Display Control Unit

The display control unit 218 transmits the 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 equipment 100 via the transmission/reception unit 211, and causes the display device of the user equipment to perform display control as illustrated in FIGS. 4A to 4C.
Further, when the information processing device of the present embodiment is not a server but a terminal directly operated by the user, the display control unit 218 may control to display at least one of the information on the crop in each target region acquired by the acquisition unit 213, the information on the crop in the entire field or part of the field generated by the generation unit 214, or the evaluation regarding the crop by the evaluation unit 215 in the input and output interface 230 such as a display.

1.1.9. Sensing Device Management Unit

The sensing device management unit 219 manages a state of the measurement device 300 so that data can be normally collected, and perform correction, calibration, or the like as necessary. For example, the sensing device management unit 219 may manage whether or not a sensing device such as the measurement device 300 is normally operating, or may manage an operating time of the measurement device 300 or the like. In this case, when an abnormality such as occurrence of a communication error or occurrence of device alarm is detected, the sensing device management unit 219 may display the fact that the abnormality occurs on the display unit of the input and output interface 230.

1.2 Operation Processing

Next, an operation of the smart agriculture system will be described. FIG. 3A shows a flowchart of a process of outputting information required at a desired resolution from the database that records the information on the crop in a field according to a user operation in the smart agriculture system of the present embodiment, and FIG. 3B is a sequence diagram illustrating an example of a process of measuring the inside of the field at a desired resolution and constructing a database in the smart agriculture system of the present embodiment.
As illustrated in FIG. 3A, in step A01, the adjustment unit 212 of the server 200 adjusts the target region for acquisition of the information on the crop in the field, in accordance with the user's operation on the terminals 100. In this case, the adjustment unit 212 may adjust the region and specify specific content of the information on the crop to be acquired, based on the purpose of the agricultural work selected by the user and the basic agricultural work data 251.
In step A02, the acquisition unit 213 of the server 200 acquires the information on the crop in the target region. Specifically, the acquisition unit 213 may acquire the information on the crop in the target region from a database that can constitute a digital twin.
In step A03, the generation unit 214 of the server 200 generates the information on crops in the entire field or part of the field based on the information on the crop in one or more regions. Specifically, various types of information in the target region may be generated or a map of the entire field may be generated, based on the information on the crop in the target region acquired by the acquisition unit 213.
For example, as illustrated in FIG. 4A, when the user taps the switch button O3 for changing to a flowering rate or disease in accordance with the purpose of the agricultural work, the adjustment unit 212 may adjust the region based on the basic agricultural work data 251, and the generation unit 214 may display a map including a region having the adjusted size in the display region R1 based on the information on the crop in one or more regions.
In this case, the adjustment unit 212 may change a size of an initial region according to the tap of the button O3. To be specific, when the user taps "flower and bud" of the button O3, the region of the resolution suitable for displaying the flower and the bud may be adjusted by the adjustment unit 212, and a map or the like may be displayed on the display region R1 using the adjusted region as initial information. When the user taps "disease" of the button O3, the region of the resolution suitable for displaying the disease may be adjusted by the adjustment unit 212, and a map or the like may be displayed on the display region R1 using the adjusted region as initial information. In this case, the size of the initial region may be different in accordance with the information on the crop such as "flower and bud" or "disease" selected by the button O3.
Further, the user may drag a map of R1 to specify a target region, or tap a O2 button or a O1 button to increase or decrease an area of the target region or each region to be displayed, and in this case, the adjustment unit 212 may obtain information of the region designated by the user.
Further, as illustrated in FIG. 4A, when the user selects a specific target region in the field, the adjustment unit 212 may adjust the target region for acquisition of the information on the crop according to the operation of the user, the acquisition unit 213 may acquire the information on the crop in the target region from the database that can construct the digital twin, and the generation unit 214 may generate and display the information on the crop in the selected target region in the display region R2 based on the information on the crop in one or more regions.
Next, an example of a case in which the measurement device 300 measures the inside of the field and the database that construct the digital twin is constructed will be described hereinafter.
As illustrated in FIG. 3B, in step B01, the adjustment unit 212 of the server 200 adjusts the target region for acquisition of the information on the crop in the field. In this case, the adjustment unit 212 may adjust the region and specify the specific content of the information on the crop to be acquired, based on the purpose of the agricultural work and the basic agricultural work data 251.
In step B02, the transmission/reception unit 211 of the server 200 may transmit information on the region adjusted by the adjustment unit 212 and the information on the crop acquired by the sensor to the measurement device 300. The information transmitted to the measurement device 300 is information for indicating, to the measurement device 300, a region that is a measurement target, and the information on the crops to be measured in the target region using a sensor or the like. Based on the instruction information, the measurement device 300 may execute acquisition of the information on the crops in the region that is a measurement target. In step B03, the transmission/reception unit 211 of the server 200 receives the information on the crop acquired for each region from the measurement device 300.
In step B04, the acquisition unit 213 of the server 200 may record the acquired information in the crop ID. Further, in step B05, the generation unit 214 of the server 200 generates the information on the crop in the entire field or part of the field, and in step B06, the evaluation unit 215 of the server 200 evaluates the crop based on at least one of the information on the crop in one or more regions or the information on the crop in the entire field or part of the field.
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 the information on the determined content of the agricultural work to the work device 400.

1.3. Examples

Hereinafter, the smart agriculture system will be further described using specific examples.

1.3.1. Improving Accuracy of Crop Yield Prediction

When the crop yield prediction is performed visually, double count, omission, oversight, and the like occur frequently as the field becomes larger in scale. By improving the accuracy of the crop yield prediction, it is possible to make a cultivation/supply plan without any unreasonable or wasteful effort against a demand fluctuation in supply destinations such as wholesalers, retailers, and restaurants.
In this regard, in the crop yield prediction using the present system, the adjustment unit 212 may adjust the region in a relatively wide section in the selection of the user or the initial setting, and the acquisition unit 213 may acquire the number of fruits, the number of flowers, or the like as the information on the crop in the target region by referring to the database. The generation unit 214 may generate the information on the crop in the entire field or part of the field based on the information on the crop in the region.
The evaluation unit 215 may evaluate, for example, the number of fruits, the number of flowers from the image data. The evaluation may be performed using a model obtained by machine learning using learning data in which image data is associated with fruits and flowers of crops. When such evaluation is performed for each region, the number of fruits, the number of flowers, and the like in the entire field or a part thereof can be evaluated, and the crop yield prediction can be performed. Further, the evaluation unit 215 may output the distribution of the sizes of the fruits in the field, and the work determination unit 216 may determine necessary work such as fertilization to homogenize the size or quality of the fruits according to the distribution.

1.3.2. Reduction of Production Damage due to Disease/Abnormality

In the case of visual detection of disease/abnormality, as the field becomes larger in scale, omission, oversight, and the like occur frequently. In particular, when the field becomes large in scale, it is difficult to frequently inspect the state of the crop in the entire field, and therefore, only a representative plant is inspected, and disease or abnormality in the field is determined based on the representative plant. However, the present invention is not limited to the representative plant, it is not possible to predict where the disease will occur and spread, and such visual determination is not sufficient. Therefore, in order to reduce production damage due to disease/abnormality, it is necessary to detect the crop in the entire field with a resolution close to a micro level.
In this regard, in the reduction of production damage due to disease/abnormality using the present system, the adjustment unit 212 may adjust the region in the relatively narrow section or in units of crops in the selection of the user or the initial setting, and the acquisition unit 213 may acquire and record disease/abnormality or the like as the information on crops in the target region. The generation unit 214 may generate and record the information on the crop in the entire field or part of the field based on the information on the crop in the region.
The evaluation unit 215 may evaluate, for example, whether the crop is diseased/abnormal or normal from the image data. The evaluation may be performed using a model obtained by machine learning using learning data in which the image data is associated with disease/abnormality of crops. Further, the evaluation unit 215 may further consider information on the disease/abnormality of crops in a region adjacent to the target region in addition to the target region. Since the disease/abnormality tends to spread to adjacent crops, the accuracy of disease/abnormality determination by the evaluation unit 215 in the target region can be improved by considering the adjacent region. Further, the evaluation unit 215 may output caution information on the disease/abnormality of crops in the adjacent region based on a disease/abnormality determination result of the target region, or may output a distribution of disease/abnormality in the field.
Further, the acquisition unit 213 or the generation unit 214 of the server 200 accumulates the information on the crop as data in time series, so that the work determination unit 216 can also make a pest control work plan at an appropriate time and place.

1.3.3. Ensuring of Stable Production Amount and Quality of Crop

In order to ensure stable production amount and quality of the crop, appropriate adjustment work is required according to a state of the crop. Here, the adjustment work is not particularly limited, but examples thereof may include thinning, fruit thinning, thinning of leaves, pinching, and flower thinning. Examples of the state of the crop may include a state of the fruit such as a size, fruit weight (estimated value), and color; and a state of the flower such as a bud, flowering stage, and a pollination state.
In agricultural work for ensuring stable production amount and quality of crops, an agricultural production/management person having a skilled technology capable of determining the state of crops as described above and performing appropriate adjustment work according to the state makes each determination, and instructs an agricultural worker about work content. Further, when the field becomes larger, it is substantially difficult to check individual states of all the crops and determine the adjustment work, and therefore, only an individual state of the representative plant is checked and adjustment work for crops around the representative plant is also determined. However, when it is not possible to check individual states of the crops and determine the adjustment work in this way, it is not possible to achieve both an increase in a scale of the field and optimization of the ensuring of the stable production amount and quality of the crops.
On the other hand, in ensuring stable production amount and quality of crops using the present system, the adjustment unit 212 may adjust the region in the relatively narrow section or in units of crops in the selection of the user or the initial setting, and the acquisition unit 213 may acquire and record the number of fruits, the number of flowers, a leaf area index (LAI), the state of fruits, the state of flowers, and the like as the information on crops in the target region. The generation unit 214 may generate and record the information on the crop in the entire field or part of the field based on the information on the crop in the region.
Further, the acquisition unit 213 or the generation unit 214 of the server 200 accumulates the information on the crop as data in time series, so that the work determination unit 216 can also make an adjustment work plan at an appropriate time and an appropriate place. Further, for example, the work determination unit 216 may determine agricultural work content for equalizing the quality of the fruit per tree by counting the number of fruits in one plant or tree, and the work instruction unit 217 may instruct the execution of the agricultural work content.
Alternatively, the work determination unit 216 may present a deviation degree between the representative plant and each region in the field with respect to the state of the representative plant that has been well cared for, based on the information on the crop acquired as described above, thereby determining to preferentially perform the adjustment work on a region that has not been well managed.

1.3.4. Improvement of Production Amount Due to Improved Pollination Accuracy

In order to improve the production amount by improving the pollination accuracy, it is necessary to perform appropriate pollination work to an appropriate place according to a flowering state of the crop. In particular, since some crops have a limited time for efficient pollination after flowering, it is important to find a flowering flower and quickly perform a pollination operation on the flower in order to improve the production amount. Further, pollen itself is often expensive, and an efficient pollination operation is desired.
However, it is necessary to thoroughly look at and check individual crops for flowering, and it is necessary to determine a flowering state and pollination state of flowers for each position and ascertain a place where pollination work should be performed, and therefore, as the field becomes larger in scale, omission, oversight, and the like occur frequently.
On the other hand, in the improvement of the production amount due to improved pollination accuracy of the crop using the present system, the adjustment unit 212 may adjust the region in a relatively narrow section or in units of crops, and the acquisition unit 213 may acquire and record the number of flowers, the state of the flowers, and the like as the information on the crop in the target region. The generation unit 214 may generate and record the information on the crop in the entire field or part of the field based on the information on the crop in the region.
Further, the acquisition unit 213 or the generation unit 214 of the server 200 accumulates the information on crops as data in time series, so that the work determination unit 216 can also make a pollination work plan at an appropriate time and place.

1.3.5. Cost reduction and environmental conservation through Pinpoint Application of Pesticides and Fertilizers

Pinpoint application of pesticides and fertilizers makes it possible to optimize and reduce an amount of usage of the pesticides and fertilizers, and accordingly, easily achieve cost reduction and environmental conservation. Pinpoint application of pesticides and fertilizers requires work to be performed according to the state of the crop.
For cost reduction and environmental conservation through pinpoint application of pesticides and fertilizers of crops using the present system, the adjustment unit 212 may adjust the region in the relatively narrow section or in units of crops in the selection of the user or the initial setting, and the acquisition unit 213 may acquire and record the state of fruits, the state of leaves, and the like as the information on crops in the target region. The generation unit 214 may generate and record the information on the crop in the entire field or part of the field based on the information on the crop in the region.
Further, the acquisition unit 213 or the generation unit 214 of the server 200 accumulates the information on the crop as data in time series, so that the work determination unit 216 can also make a work plan for applying pesticides and fertilizers at an appropriate time and place.
Specifically, work of pesticides and fertilizers may be planned from time-series change in the amount or the state (bud, flower, and fruit) of the crop in any region acquired by the acquisition unit 213. For example, since an amount and location of buds that have decreased from the previous day substantially match an amount and location of flowers that have flowered, the work determination unit 216 may propose preferentially performing the pollination work. Further, when there is a situation in which the fertilization probability is high within two days from the flowering and is low thereafter, the work determination unit 216 may manage a time from the flowering as a parameter and preferentially present a region where agricultural work is to be performed so that pollination work is performed on a location of a flower on the second day of flowering without pollination.
Further, as another example, when it is found that a trouble due to a disease or a pest occurs when fruit thinning work is scheduled, the work determination unit 216 may output a priority of material arrangement, execution time, and adjustment work of a pest control measure based on an infection range of the disease or the pest acquired by the acquisition unit 213.

2. Information Processing Method

In an information processing method according to the present embodiment, an information processing device executes adjusting a target region for acquisition of information on a crop; acquiring the information on the crop in the target region; and generating the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.
A specific aspect of the method of the present embodiment has been described in the operation process, and therefore, detailed description thereof is omitted here.

3. Program

A program according to the present embodiment causes an information processing device to execute adjusting a target region for acquisition of information on a crop; acquiring the information on the crop in the target region; and generating the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.
The program may be recorded in a readable recording medium. A specific aspect of the processing executed by the program of the present embodiment has been described in the operation processing, and thus detailed description thereof will be omitted here.

INDUSTRIAL APPLICABILITY

The present invention has industrial applicability as an element technology that can be used for a smart agriculture system.

[REFERENCE SIGNS]

1: system, 200: server, 210: processor, 211: transmission/reception unit, 212: adjustment unit, 213: acquisition unit, 214: generation unit, 215: evaluation unit, 216: work determination unit, 217: work instruction unit, 218: display control unit, 219: sensing device management unit, 220: communication interface, 230: input and output interface, 240: memory, 250: storage, 251: basic agricultural work data, 252: field data, 253: crop data, 260: communication bus, 300: measurement device, 400: work device, 500: field, 510: region, 520: crop, 530: Section.

Claims

An information processing device comprising:
an adjustment unit configured to adjust a target region for acquisition of information on a crop;

an acquisition unit configured to acquire the information on the crop in the target region; and

a generation unit configured to generate the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.
The information processing device according to claim 1, wherein the generation unit generates information on a target crop in the region, in addition to the information on the crop in the target region.
The information processing device according to claim 1, further comprising an evaluation unit configured to evaluate the crop based on at least one of the information on the crop in the one or more regions or the information on the crop in the entire field or part of the field.
The information processing device according to claim 3, wherein the evaluation of the crop includes a difference between information on an actual crop and the information on the target crop.
The information processing device according to claim 1, wherein the adjustment unit adjusts the region based on information on a purpose of agricultural work to be executed.
The information processing device according to claim 1, wherein
the region is defined by a section of the field having substantially a uniform area, and

the adjustment unit adjusts an area of the section as the region.
The information processing device according to claim 1, wherein
the region is defined by the crop; and

the adjustment unit adjusts, as the region, a target portion of the crop from which the acquisition unit acquires the information on the crop.
The information processing device according to claim 1, wherein
the acquisition unit acquires the information on the crop using a sensor, and

the sensor is at least one of an image sensor, a component sensor, or an environment sensor.
The information processing device according to claim 1, further comprising a transmission/reception unit configured to transmit information on the region adjusted by the adjustment unit to a measurement device,
wherein the acquisition unit acquires the information on the crop in the target region from the measurement device.
The information processing device according to claim 3, comprising a work determination unit configured to determine content of agricultural work based on the evaluation regarding the crop.
The information processing device according to claim 10, comprising a work instruction unit configured to transmit information on the determined content of the agricultural work to a work device.
An information processing method comprising:
adjusting, by an information processing device, a target region for acquisition of information on a crop;

acquiring, by the information processing device, the information on the crop in the target region; and

generating, by the information processing device, the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.
A program for causing an information processing device to execute:
adjusting a target region for acquisition of information on a crop;

acquiring the information on the crop in the target region; and

generating the information on the crop in an entire field or part of the field based on the information on the crop in one or more regions.