JP7007018B2 - Crop growth estimation device, crop growth estimation method, program, and recording medium - Google Patents

Description

The present invention relates to a crop growth estimation device, a crop growth estimation method, a program, and a recording medium.

Conventionally, a method of predicting the yield from image data of crops being bred in a field has been proposed (Japanese Unexamined Patent Publication No. 2003-6612). However, only the prediction of the yield in the specific field from which the image was acquired can be predicted by this prediction method.

Japanese Patent Application Laid-Open No. 2003-6612

In recent years, due to problems such as the impact on agriculture due to global warming and abnormal weather, and food shortages due to population growth, it has become possible to grow crops efficiently and suppress the destruction of harvested crops due to surplus. It is regarded as important. However, from a worldwide perspective, meteorological and environmental conditions differ from continental unit to country unit, so it is not possible to grasp the growth status of agricultural products in Japan, for example, in countries with completely different conditions. , It is actually difficult without direct information from the field.

Therefore, an object of the present invention is to provide, for example, a crop growth estimation system that can estimate the current growth status of a crop and the future growth status even if the crop is not in the field where the crop is grown. And. More specifically, for example, the purpose is to provide a worldwide crop growth estimation system.

In order to achieve the above object, the crop growth estimation device of the present invention is used.
Data acquisition means and
Submodel generation means and
Memories and
Has an estimation means and
The data acquisition means is
It is a means to acquire wide area data and narrow area data,
The wide area data includes at least one of satellite image data and meteorological data in a wide area.
The narrow area data includes environmental data, field data and crop growth status data in the narrow area;
The submodel generation means is
It is a means to generate at least two or more submodels among the following (1) to (6);
The storage means
It is a means to store each of the generated submodels.
The estimation means is
It is a means for generating an estimation result of crop growth in an arbitrary area based on at least two or more submodels of each of the stored submodels from any of the data acquired by the data acquisition means. It is a feature.
(1) From the wide area data, the growth status of the crop is generated as the first submodel (2) From the narrow area data, the growth status of the crop is generated as the second submodel (3) From the growth status data of the crop. , Future growth situation is generated as the third submodel (4) Narrow area data is generated as the fourth submodel from the wide area data (5) Future wide area data is generated from the wide area data as the fifth submodel. (6) From the narrow area data, future narrow area data is generated as the sixth submodel.

The method for estimating crop growth of the present invention is
Data acquisition process and
Submodel generation process and
The memory process and
Has an estimation process and
The data acquisition process is
This is the process of acquiring wide area data and narrow area data.
The wide area data includes satellite image data and meteorological data in a wide area.
The narrow area data includes environmental data, field data and crop growth status data in the narrow area;
The submodel generation step is
It is a step of generating at least two or more submodels among the following (1) to (6);
The storage step is
It is a process of storing each of the generated submodels.
The estimation process is
It is a step of generating an estimation result of crop growth in an arbitrary area based on at least two or more submodels of each of the stored submodels from any of the data acquired in the data acquisition step. It is a feature.
(1) From the wide area data, the growth status of the crop is generated as the first submodel (2) From the narrow area data, the growth status of the crop is generated as the second submodel (3) From the growth status data of the crop. , Future growth situation is generated as the third submodel (4) Narrow area data is generated as the fourth submodel from the wide area data (5) Future wide area data is generated from the wide area data as the fifth submodel. (6) From the narrow area data, future narrow area data is generated as the sixth submodel.

The program of the present invention is characterized in that a computer executes the method for estimating crop growth of the present invention.

The recording medium of the present invention is computer readable in which the program of the present invention is recorded.

According to the present invention, for example, it is possible to estimate the current growth status and future growth status of a crop without being at the site where the crop is growing, and it is also possible to estimate the crop growth worldwide. It will be possible.

FIG. 1 is a block diagram showing an example of the estimation device of the first embodiment. FIG. 2 is a block diagram showing another example of the estimation device of the first embodiment. FIG. 3 is a flowchart showing an example of the estimation method of the first embodiment. FIG. 4 is a block diagram showing another example of the estimation device of the second embodiment. FIG. 5 is a flowchart showing an example of the estimation method of the second embodiment. FIG. 6 is a block diagram showing another example of the estimation device of the third embodiment. FIG. 7 is a flowchart showing an example of the estimation method of the third embodiment. FIG. 8 is a block diagram showing an example of the hardware configuration of the estimation device of the first embodiment.

In the estimation device of the present invention, for example, the wide area is a global area and the narrow area is a field.

In the estimation device of the present invention, for example, the meteorological data in the wide area data includes at least one selected from the group consisting of sunshine time, temperature, soil moisture, and precipitation in the wide area data, and the estimation device in the narrow area data. The environmental data includes at least one selected from the group consisting of temperature, humidity, sunshine time, amount of solar radiation, rainfall, and images of the narrow area in the narrow area, and the field data in the narrow area data includes field data. The crop growth status data in the narrow area data includes at least one selected from the group consisting of crop growth work information, pest outbreak information, and crop growth disorder outbreak information in the field in the narrow area. Includes at least one of crop quantity and crop quality.

The estimation device of the present invention further includes, for example, an output means for outputting an estimation result.

In the estimation method of the present invention, for example, the wide area is a global area and the narrow area is a field.

In the estimation method of the present invention, for example, the meteorological data in the wide area data includes at least one selected from the group consisting of sunshine time, temperature, soil moisture, and precipitation in the wide area data, and the estimation method in the narrow area data. The environmental data includes at least one selected from the group consisting of temperature, humidity, sunshine time, amount of solar radiation, rainfall, and images of the narrow area in the narrow area, and the field data in the narrow area data includes field data. The crop growth status data in the narrow area data includes at least one selected from the group consisting of crop growth work information, pest outbreak information, and crop growth disorder outbreak information in the field in the narrow area. Includes at least one of crop quantity and crop quality.

The estimation method of the present invention further includes, for example, an output means for outputting an estimation result.

Next, an embodiment of the present invention will be described. The present invention is not limited to the following embodiments. In each of the following figures, the same parts are designated by the same reference numerals. Further, the explanations of the respective embodiments can be referred to each other's explanations unless otherwise specified. Further, the configurations of the embodiments can be combined unless otherwise specified.

[Embodiment 1]
FIG. 1 is a block diagram showing a configuration of an example of the crop growth estimation device 1 of the present embodiment. The estimation device 1 includes a data acquisition unit 10, a submodel generation unit 11, a storage unit 12, and an estimation unit 13 as essential components. The estimation device 1 may include, for example, an input means 14 for inputting arbitrary area information to be the target of the estimation result, and an output means 15 for outputting the estimation result obtained by the estimation means 13. The estimation device 1 includes, for example, an integrated device including each means. The estimation device 1 can also be referred to as an estimation system, for example. In the estimation device 1, the submodel generation means 11 and the estimation means 13 are, for example, processing units.

The estimation device 1 is not limited to, for example, an integrated device as shown in FIG. 1, and may include, for example, a terminal and a server, and the two may be connected to each other via a communication network (also referred to as an estimation system). The communication line network is not particularly limited, and a known communication line network can be used. For example, it may be wired or wireless, and specific examples thereof include an Internet line, a telephone line, and a LAN (Local Area Network).

As described above, the data acquisition means 10 is a means for acquiring wide area data 101 and narrow area data 102. The wide area data 101 includes satellite image data and meteorological data in a wide area, and the narrow area data 102 includes environmental data, field data and crop growth status data in a narrow area.

The wide area is, for example, a unit at the global level, and specific examples thereof include continental units such as Eurasia (Asia continent, Europe continent), North America continent, South America continent, Africa continent, Austrian continent, Japan, and China. , America, Brazil, etc. can be exemplified. The wide area data can be said to be, for example, comprehensive data in the wide area. On the other hand, the narrow area can be exemplified by, for example, a unit of a state in a country, a unit of a prefecture, a unit of a municipality, a unit of a specific field, or the like. The narrow area data can be said to be, for example, precise data in a narrow area which is a limited area.

The data acquisition means 10 is not particularly limited, and may be, for example, a means for receiving data from a database. In this case, the estimation device 1 may be connected to the database via a communication network, for example.

As the database of the wide area data, for example, an existing database can be used. Examples of the database include a database of a data integration and analysis system (DIAS), which is a global environment platform. Further, as the database of the narrow area data, for example, a database accumulating environmental data, field data and crop growth status data in the narrow area can be mentioned. The database may be, for example, possessed by the estimation device itself of the present invention, but may be a database outside the estimation device, and the estimation device can acquire information from, for example, an external database.

FIG. 2 shows an example of an estimation device that can be connected to an external database via a communication network. The estimation device 1 can be connected to the external database 21A for wide area data and the external database 21B for narrow area data via the communication network 20.

The submodel generation means 11 is a means for generating at least two or more submodels among the above (1) to (4). In the present embodiment, the means for generating the first submodel, the second submodel, and the third submodel will be described as an example. From the wide area data 101, the crop growth status is generated as the first submodel (111), and from the narrow region data 102, the crop growth status is generated as the second submodel (112), and the crop growth status data is generated. Therefore, the future growth situation is generated as a third submodel (113). Examples of the submodel generation means 11 include a CPU (central processing unit).

The submodel generation means 11 generates a first submodel, a second submodel, and a third submodel by machine learning from the crop growth status data in the wide area data 101, the narrow area data 102, and the narrow area data 102, respectively. can. The first submodel, the second submodel, and the third submodel are newly generated by machine learning every time new wide area data and narrow area data are acquired, and the accuracy of each submodel is improved. Is preferable.

The storage means 12 stores the first submodel 121, the second submodel 122, and the third submodel 123 generated by the submodel generation means 11, respectively. As described above, when each submodel is newly generated, for example, it is preferable to update and store each submodel each time. The storage means 12 is not particularly limited, and for example, as will be described later, a random access memory (RAM), a read-only memory (ROM), a flash memory, a hard disk (HD), an optical disk, a floppy (registered trademark) disk (FD). And so on. The storage means 12 may be a built-in type or an external type.

The input means 14 inputs an arbitrary area to be the target of the estimation result in the estimation means 13. The input means 14 is not particularly limited, and examples thereof include a keyboard and a touch panel, as will be described later.

The estimation means 13 is an arbitrary input based on at least two or more submodels of the first submodel, the second submodel, and the third submodel stored from the data acquired by the data acquisition means 10. Generate estimates of crop growth in the area. Examples of the estimation means 13 include a CPU (Central Processing Unit).

The output means 15 outputs the estimation result obtained by the estimation means 13. The output means 15 is not particularly limited, and examples thereof include output to a monitor such as a display and output to paper.

FIG. 8 illustrates a block diagram of the hardware configuration of the estimation device 1. The estimation device 1 includes, for example, a CPU 31, a memory 32, an interface (I / F) 33, a display 34, a communication device 35, an input device 36, a storage device 37, and the like. The memory 32, the I / F 33, and the storage device 37 are connected to the CPU 31 by, for example, a communication bus. For example, a display 34, a communication device 35, an input device 36, and the like are connected to the I / F 33. In the hardware configuration, communication between internal circuits is connected by a bus.

The CPU 31 is responsible for overall control of the estimation device 1. In the estimation device 1, for example, the program of the present invention and other programs are executed by the CPU 31, and various information is read and written. Specifically, in the estimation device 1, for example, the CPU 31 functions as a data acquisition unit 40 and a submodel generation unit 41.

The memory 32 includes, for example, a main memory, and the main memory is also referred to as a main storage device. When the CPU 31 performs processing, for example, the memory 32 reads various programs such as the program of the present invention stored in the auxiliary storage device described later, and the CPU 31 receives data from the memory 32 and described above. Run the program. The main memory is, for example, a RAM (random access memory). The memory 32 further includes, for example, a ROM (read-only memory).

The I / F 33 connects, for example, between the functional units of the CPU 31, the memory 32, and the like. Further, the I / F 33 can also be connected to, for example, an external device. The estimation device 1 can be connected to the communication network by the communication device 35 connected to the I / F 33, and can also be connected to the external device via the communication network. In the estimation device 1, for example, the I / F 33 or the I / F 33 and the communication device 35 function as an output unit 16.

The storage device 37 is also referred to as a so-called auxiliary storage device with respect to the main memory (main storage device), for example. The storage device 37 includes, for example, a storage medium and a drive for reading and writing to the storage medium. The storage medium is not particularly limited, and may be, for example, an internal type or an external type, and may be an HD (hard disk), FD (floppy (registered trademark) disk), CD-ROM, CD-R, CD-RW, MO, etc. Examples thereof include a DVD, a flash memory, a memory card, and the like, and the drive is not particularly limited. As the storage device 37, for example, a hard disk drive (HDD) in which a storage medium and a drive are integrated can be exemplified. The storage device 37 stores, for example, an operation program such as the program of the present invention, and as described above, when the CPU 31 is executed, the memory 32 reads the operation program from the storage device 37. In the storage device 37, for example, in addition to the program, for example, wide area data 101, narrow area data 102, first submodel 121, second submodel 122, third submodel 123, estimation result 131, and the like are stored. To. In addition, the storage device 37 can also store, for example, information acquired by the data acquisition unit 40, information acquired from the estimation unit 42, and the like.

Examples of the display 34 include an LED display and a liquid crystal display. The communication device 35 only needs to be able to communicate between the estimation device 1 and the external device, and is, for example, a device that can be connected to the above-mentioned communication network. The input device 36 is, for example, a keyboard, a mouse, a touch panel, or the like.

Next, the estimation method of the present embodiment will be described with reference to the flowchart of FIG.

The estimation method of the present embodiment is carried out as follows, for example, by using the estimation device 1 of the present embodiment shown in FIG. The estimation method of the present embodiment is not limited to the use of the estimation device 1 of FIG. In the following embodiments, the wide area is a global level unit, and the wide area data is satellite image data and meteorological data (sunshine time, temperature, soil moisture, and precipitation) acquired from DIAS, and the narrow area is described. The area is a specific field, and the narrow area data is environmental data (temperature, humidity, sunshine time, amount of solar radiation, rainfall, and images of the narrow area), field data (work information on crop growth, occurrence of pests). Information and outbreak information of crop growth disorders) and crop data (crop quantity and crop quality). The amount of the crop is, for example, the yield of the crop, the shipping rate with respect to the number of harvests of the crop, and the like.

(A1) Data acquisition step In the (A1) step, a wide area data acquisition step (A1-1) for acquiring wide area data and a narrow area data acquisition step (A1-2) for acquiring narrow area data are performed. The step (A1-1) and the step (A1-2) may be performed in parallel or separately, for example. The data acquired in the step (A1) is also, for example, the start data of the estimation in the estimation step (A4) described later.

(A2) Submodel generation step In the (A2) step, the growth state of the crop is generated as the first submodel from the wide area data, and the growth of the crop is generated from the narrow area data. A generation step (A2-2) for generating a situation as a second submodel and a generation step (A2-3) for generating a future growth situation as a third submodel from the growth status data of the crop are performed. The (A2-1) step, the (A2-2) step, and the (A2-3) step may be performed in parallel or separately, for example.

Submodel generation can be performed by machine learning, for example, as described above. Each submodel may be generated once, for example, but it is preferable to repeatedly perform and update each time wide area data or narrow area data is acquired.

The first submodel is, for example, a model of the growth condition of a crop derived from the wide area data based on the conditions. The second submodel is, for example, a model of the growth condition of a crop derived from the narrow area data based on the conditions. The third submodel is, for example, a model of the future growth condition of the crop, which is derived from the growth condition data of the crop based on the conditions. In the third submodel, the growth status data of the crop may be, for example, data included in the narrow area data or fictitious data that can be assumed. As mentioned above, the growth condition of the crop includes the quantity of the crop and the quality of the crop. The amount of the crop includes, for example, the yield of the crop, the shipping rate with respect to the number of harvests of the crop, and the like. The quality of the crop includes, for example, quality, good or bad growth, and the like. The good or bad of growth includes, for example, whether or not the crop can grow, the growth rate of the crop, and the like. The quality includes, for example, sugar content, appearance and the like.

(A3) Storage Step The (A3) step is a storage step (A3-1) for storing the first submodel generated in the (A2-1) step, and a storage step for storing the generated second submodel. (A3-2), a storage step (A3-3) for storing the generated third submodel is performed. The (A3-1) step, the (A3-2) step, and the (A3-3) step may be performed in parallel or separately, for example.

(A4) Estimating Step In the (A4) step, at least two or more subs of the first submodel, the second submodel, and the third submodel stored in the data acquired in the data acquisition step (A1) are performed. Based on the model, it produces estimation results of crop growth in any of the above areas. Arbitrary area information is information for identifying an area for which crop growth is to be estimated. The "estimated result of crop growth" can be set arbitrarily, for example, may be the quantity of the crop or the quality of the crop. Further, the "estimated result of crop growth" may be, for example, an estimation of the current result or an estimation of the future result. The estimation result of this can be obtained by selecting the submodel according to the purpose.

Here, from the first submodel generated from the wide area data worldwide, the second submodel generated from the narrow area data of the limited area (field A) of Mie prefecture, and the growth status data of citrus fruits in the field A. The case of estimating the future growth of citrus fruits in Israel using the generated third submodel will be described as an example. In this case, if arbitrary regional information is set in Israel, for example, first, the growth information of citrus fruits in Israel can be estimated from the first submodel. However, since the first submodel is a machine-learned submodel based on comprehensive data over a wide area, the estimation accuracy may not be sufficient by this alone. However, further, by combining the second submodel machine-learned based on the precise data of the limited area called field A, it becomes possible to estimate the growth of citrus fruits with higher accuracy even in Israel where there is no precise data. In addition, when estimating the future growth situation in Israel, the future growth of citrus fruits is further combined with the third submodel machine-learned based on the precise citrus data in the limited plot of field A. Highly accurate estimation of the situation is also possible.

Thus, even in areas where there is actually no precise data on crop growth, submodels based on comprehensive worldwide data and precision data on limited plots and precision on crops in limited plots. By combining with a submodel based on data, it is possible to estimate the growth status of crops accurately and easily.

(A5) Output Step The (A5) step outputs the estimation result obtained by the (A4) step.

According to the present invention, since the growth status of crops can be estimated worldwide in this way, for example, the growth risk to the farmer can be notified, the estimation of the field condition worldwide, the farmer, based on these estimation results. It will be possible to notify domestic and foreign traders or importers / exporters of the estimated yield in a specific area, the quality of harvested crops, etc. Furthermore, based on the estimation results, for example, it is possible to present a more efficient growth method, and to present the distribution of crops from an area where crops are estimated to be surplus to an area where crops are estimated to be in short supply.

Although the first submodel, the second submodel, and the third submodel have been described in the present embodiment, the present invention may generate, for example, at least two or more submodels of the first to sixth submodels. The combination may be appropriately determined according to the purpose. Further, in the present invention, for example, even if all of the first to sixth submodels are generated, the estimation means can appropriately determine the combination of the submodels to be used according to the estimation of the purpose.

[Embodiment 2]
In the present embodiment, in the estimation device of the first embodiment, the submodel generation means is a means for further generating a fourth submodel, and in the estimation method of the first embodiment, the submodel generation method is further described. An example of the process of generating the fourth submodel will be described.

A block diagram of the estimation device 2 of the present embodiment is shown in FIG. 4, and a flowchart of an estimation method using the block diagram is shown in FIG. In FIG. 4, in the estimation device 2, the submodel generation means 11 further generates narrow area data from the wide area data as a fourth submodel (114), and the storage means 12 further generates a fourth submodel. Is the same as that of the second embodiment except that (124) is stored. Further, in FIG. 5, the estimation method further includes a generation step (A2-4) for generating a fourth submodel and a storage step (A3-4) for storing the generated fourth submodel. It is the same as the first embodiment.

The narrow area data is, for example, environmental data in a narrow area, field data, etc. obtained from the wide area data by machine learning or the like. The environmental data, the field data, and the like are, for example, as described above.

In the present invention, the submodel generation means may generate a new submodel based on the information of each submodel, for example, and the storage means is further newly generated accordingly. The submodel can be stored, and the estimation means can use, for example, a new submodel.

[Embodiment 3]
In the present embodiment, in the estimation device of the second embodiment, the submodel generation means further generates the fifth submodel and the sixth submodel, and in the estimation method of the second embodiment, the submodel generation means. Further, an example in which the method is a step of generating the fifth submodel and the sixth submodel will be described.

A block diagram of the estimation device 3 of the present embodiment is shown in FIG. 6, and a flowchart of an estimation method using the block diagram is shown in FIG. In FIG. 6, in the estimation device 3, the submodel generation means 11 further generates future wide area data from the wide area data as a fifth submodel (115), and the future narrow area from the narrow area data. The data is generated as the sixth submodel (116), and the storage means 12 further stores the fifth submodel (125) and the sixth submodel (126), except that the data is stored in the second embodiment. The same is true. Further, in FIG. 7, the estimation method further includes a generation step (A2-5) for generating a fifth submodel, a generation step (A2-6) for generating a sixth submodel, and a generated fifth submodel. It is the same as the second embodiment except that it has a storage step (A3-5) for storing and a storage step (A3-6) for storing the generated sixth submodel.

[Embodiment 4]
In the estimation device and estimation method of the present invention, for example, each of the submodels can be selected according to the target estimation result, whereby the estimation result can be obtained. In this embodiment, a combination of submodels that can be selected according to a target estimation result is illustrated. In the present invention, the combination of the submodels is not limited to these examples.

Table 1 below shows the types of each submodel. Each submodel is generated as an item of a generation model by machine learning, for example, from an item of generation source data.

Table 2 below exemplifies the combination of submodels according to the type of estimation of interest. In the table below, for example, by performing information processing by the submodel in the order of the arrows, the target estimation becomes possible.

Combination A will be described. When estimating the "amount of future crops in the narrow area X", as shown in Table 2 above, the estimation can be performed, for example, by combining the first submodel and the third submodel in this order. That is, when wide-area data is used as the start data for estimation, the "amount as the growth status of the crop" is estimated from the first submodel based on the start data, and the first is based on the estimation result. From the 3 submodels, "the amount of future crops as the growth situation of future crops in the narrow area X" is estimated, and this is used as the estimation result. The amount may be, for example, the yield amount, or the amount or ratio (shipment rate) of crops that can be shipped.

Combination B will be described. When estimating the "amount of future crops in the narrow area X", as shown in Table 2 above, for example, by combining the fourth submodel, the second submodel, and the third submodel in this order, the estimation can be made. It is possible. That is, when wide area data is used as the estimation start data, "narrow area data in the narrow area X (for example, environmental data in the narrow area X)" is estimated from the fourth submodel based on the start data. Next, based on the estimation result, "amount as the growth condition of the crop" is estimated from the second submodel, and further, based on the estimation result, "future in narrow region X" is estimated from the third submodel. Estimate the amount of future crops as the growth status of crops, and use this as the estimation result.

Combination C will be described. When estimating the "amount of future crops in the narrow area X", as shown in Table 2 above, for example, by combining the 4th submodel, the 6th submodel, and the 2nd submodel in this order, the estimation can be made. It is possible. That is, when wide area data is used as the estimation start data, "narrow area data in the narrow area X (for example, environmental data in the narrow area X)" is estimated from the fourth submodel based on the start data. Next, based on the estimation result, "future narrow area data in the narrow area X (for example, environmental data of the narrow area X, field data and crop growth status" is estimated from the sixth submodel, and further. Based on the estimation result, "the amount of future crops as the future growth situation in the narrow area X" is estimated from the second submodel, and this is used as the estimation result.

Combination D will be described. When estimating the "amount of future crops in the narrow region X", as shown in Table 2, for example, the fifth submodel and the first submodel can be combined in this order to make the estimation. That is, when wide-area data is used as the start data for estimation, "future wide-area data (for example, wide-area meteorological data, etc.)" is estimated from the fifth submodel based on the start data, and the estimation is further performed. Based on the result, "the amount of future crops as the future growth situation in the narrow area X" is estimated from the first submodel, and this is used as the estimation result.

[Embodiment 5]
The program of the present embodiment is a program that can execute the estimation method of the first embodiment on a computer. Alternatively, the program of this embodiment may be recorded on a computer-readable recording medium, for example. The recording medium is not particularly limited, and examples thereof include a read-only memory (ROM), a hard disk (HD), an optical disk, a floppy (registered trademark) disk (FD), and the like.

Although the invention of the present application has been described above with reference to the embodiments, the invention of the present application is not limited to the above-described embodiment. Various changes that can be understood by those skilled in the art can be made within the scope of the present invention in terms of the configuration and details of the present invention.

This application claims priority on the basis of Japanese Patent Application No. 2017-026604 filed on February 16, 2017 and incorporates all of its disclosures herein.

According to the present invention, for example, it is possible to estimate the current growth status and future growth status of a crop without being at the site where the crop is growing, and it is also possible to estimate the crop growth worldwide. It will be possible.

1, 2, 3 Estimating device 10 Data acquisition means 11 Submodel generation means 12 Storage means 13 Estimating means 14 Input means 15 Output means 31 CPU
32 Memory 33 Interface (I / F)
34 Display 35 Communication device 36 Input device 37 Storage device 38 Program 101 Wide area data 102 Narrow area data 121 First submodel 122 Second submodel 123 Third submodel 131 Estimated result

Claims (8)

Data acquisition means and
Submodel generation means and
Memories and
Has an estimation means and
The data acquisition means is
It is a means to acquire wide area data and narrow area data,
The wide area data includes at least one of satellite image data and meteorological data in a wide area.
The wide area is global,
The narrow area data includes environmental data, field data and crop growth status data in the narrow area;
The submodel generation means is
It is a means for generating at least two or more submodels including at least one of (1), (4) and (5) among the following (1) to (6);
The storage means
It is a means to store each of the generated submodels.
The estimation means is
From any of the data acquired by the data acquisition means, at least two or more submodels including at least one of the following (1), (4) and (5) among the stored submodels. Based on this, a crop growth estimation device, which is a means for generating an estimation result of crop growth in an arbitrary area.
(1) From the wide area data, the growth status of the crop is generated as the first submodel (2) From the narrow area data, the growth status of the crop is generated as the second submodel (3) From the growth status data of the crop. , Future growth situation is generated as the third submodel (4) Narrow area data is generated as the fourth submodel from the wide area data (5) Future wide area data is generated from the wide area data as the fifth submodel. (6) From the narrow area data, future narrow area data is generated as the sixth submodel. The meteorological data in the wide area data includes at least one selected from the group consisting of sunshine duration, temperature, soil moisture, and precipitation in the wide area.
The environmental data in the narrow area data includes at least one selected from the group consisting of temperature, humidity, sunshine duration, amount of ultraviolet rays, rainfall, and images of the narrow area in the narrow area.
The field data in the narrow area data includes at least one selected from the group consisting of crop growth work information, pest outbreak information, and crop growth disorder outbreak information in the field within the narrow area.
The estimation device according to claim 1 , wherein the crop growth status data in the narrow area data includes at least one of a crop quantity and a crop quality in the narrow area. The estimation device according to claim 1 or 2 , further comprising an output means for outputting an estimation result. Data acquisition process and
Submodel generation process and
The memory process and
Has an estimation process and
The data acquisition process is
This is the process of acquiring wide area data and narrow area data.
The wide area data includes satellite image data and meteorological data in a wide area.
The wide area is global,
The narrow area data includes environmental data, field data and crop data in the narrow area;
The submodel generation step is
It is a step of generating at least two or more submodels including at least one of (1), (4) and (5) among the following (1) to (6);
The storage step is
It is a process of storing each of the generated submodels.
The estimation process is
Based on at least two or more submodels including at least one of (1), (4) and (5) among the stored submodels from any of the data acquired in the data acquisition step. A method for estimating crop growth, which is a step of generating an estimation result of crop growth in an arbitrary area , wherein each step is executed by a computer .
(1) From the wide area data, the growth status of the crop is generated as the first submodel (2) From the narrow area data, the growth status of the crop is generated as the second submodel (3) From the growth status data of the crop. , Future growth situation is generated as the third submodel (4) Narrow area data is generated as the fourth submodel from the wide area data (5) Future wide area data is generated from the wide area data as the fifth submodel. (6) From the narrow area data, future narrow area data is generated as the sixth submodel. The meteorological data in the wide area data includes at least one selected from the group consisting of sunshine duration, temperature, soil moisture, and precipitation in the wide area.
The environmental data in the narrow area data includes at least one selected from the group consisting of temperature, humidity, sunshine duration, amount of ultraviolet rays, rainfall, and images of the narrow area in the narrow area.
The field data in the narrow area data includes at least one selected from the group consisting of crop growth work information, pest outbreak information, and crop growth disorder outbreak information in the field within the narrow area.
The estimation method according to claim 4 , wherein the crop growth status data in the narrow area data includes at least one of a crop quantity and a crop quality in the narrow area, and each step is performed by a computer . The estimation method according to claim 4 or 5 , further comprising an output means for outputting an estimation result , wherein each of the steps is executed by a computer . A program comprising causing a computer to execute each step of the estimation method according to any one of claims 4 to 6 . A computer-readable recording medium on which the program according to claim 7 is recorded.

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