JP4873545B2 - Field management support method and system - Google Patents

Description

  The present invention relates to a field management support method and system using a geographic information system.

In farm work, by performing the best work at the best time, it is possible to obtain effects such as improved crop quality and increased yield.
Traditionally, farmers often determine when to work, what they do, etc. based on past experience and intuition.
As a technique for supporting these determinations, for example, a technique described in Patent Document 1 below is known.
The one described in Patent Document 1 below is a system that assists agricultural workers in determining the time and content of appropriate pesticide application work and fertilization work for each field, and the weather model predicts the weather for each farmland. The growth model predicts the growth of crops for each farmland, the farmland map detects the state of the soil for each farmland, and the work history storage unit stores the history of pesticide application work and fertilization work for each farmland. . Proposal application work calculation unit, the weather information from the weather model, the information showing the prediction of crop growth from the growth model, the information showing the soil condition from the farmland map, and the pesticide application for each farmland stored in the work history memory Based on the history of work and fertilization work, the plan of the agricultural chemical spraying work plan and the fertilization work plan that yields the maximum yield is calculated.

JP-A-11-313594

However, the technique described in Patent Document 1 requires an observation device for each field to acquire crop growth and weather data. Therefore, the installation and management costs of the observation apparatus depend on the number of fields, and there is a problem that the cost increases when the number of fields to be managed is very large.
In addition, for a farm field in which no observation device is installed, information serving as a criterion for judging farm work cannot be calculated. In addition, since observation is performed on a field basis, there is a problem that differences within the field such as differences in the distribution of growth within the field cannot be grasped.

  An object of the present invention is to calculate and visually display information that is a criterion for agricultural work, such as a difference in the distribution of the degree of growth in the field, without installing an observation device for acquiring a growth index for each field. Another object of the present invention is to provide a field management support method and system that can support farm work in a field with an inexpensive configuration.

In order to achieve the above object, a field management support method according to the present invention is a field management support method for supporting farm work for each crop variety in a management target field,
A support device that supports farm work in the field
The first step of recognizing the growth state of the crop variety in the management target field based on the wavelength component of the satellite image data obtained by photographing the area including the management target field, and the work conditions and work contents according to the growth state of the crop variety It is searched from the stored database whether the working conditions corresponding to the growth state of the crop varieties recognized in the first step are stored, and if stored, the stored working conditions are stored in association with the working conditions. A second step of reading out the work content and outputting the work content for each management target field is executed to support the farm work in the management target field.
The first step includes
The method includes the step of recognizing the growth state of the crop varieties based on the actual growth values of the crop varieties at the fixed point observation point in the management target field and the predicted growth values analyzed based on the wavelength components of the satellite image data.
The second step includes
The position and shape information of the management target field is read from the field database storing the position and shape information on the map of the management target field, and the management target field shape is drawn on the map including the management target field. The method includes a step of drawing and outputting the shape in a display form corresponding to the growth state of the crop variety.
Further, the second step includes a step of drawing and outputting the drawn management target field shape in a display form corresponding to the work content corresponding to the growing state of the crop varieties.

The field management support system according to the present invention includes:
A support device that supports farm work in the field
First means for recognizing the growth state of the crop varieties in the management target field based on the wavelength component of the satellite image data obtained by photographing the area including the management target field;
It was searched from the database storing the work conditions and work contents according to the growth state of the crop varieties whether the work conditions corresponding to the growth state of the crop varieties recognized in the first step were stored. In this case, the apparatus includes a second unit that reads out the work content stored in association with the work condition and outputs the work content for each management target field.
The first means includes
It comprises means for recognizing the growth state of the crop varieties based on the actual growth value of the crop varieties at the fixed point observation point in the management target field and the predicted growth value analyzed based on the wavelength component of the satellite image data.
In addition, the second means is
The position and shape information of the management target field is read from the field database storing the position and shape information on the map of the management target field, and the management target field shape is drawn on the map including the management target field. It is characterized by comprising means for drawing and outputting the shape in a display form corresponding to the growth state of the crop variety.
Further, the second means includes means for drawing and outputting the drawn management target field shape in a display form corresponding to the work content corresponding to the growing state of the crop varieties.

The present invention has the following effects.
Even without installing an observation device to determine the growth index for each field, information that will be used as the basis for determining farm work is calculated and displayed visually, and materials for decision making in farm work such as differences in the distribution of growth within the field Can be provided.

Hereinafter, the present invention will be specifically described with reference to the drawings showing an embodiment of the present invention.
FIG. 1 is a diagram showing an embodiment of a field management support system according to the present invention.
As shown in FIG. 1, the farm field management system in the present embodiment includes a central processing unit 1 serving as a management apparatus that takes in weather information from a weather information site 12 via the Internet 13 and manages farm fields, and a character and graphic screen. , A terminal database 2 that stores map data as background data, and a field database 4 that stores field data having position information on the field graphics, information such as the type of cultivated crop, and soil information. A weather database 5 that stores weather information such as temperature, precipitation, sunshine duration, and solar radiation in mesh units, a corrected growth index database 6 that stores a corrected growth index output from the corrected growth index calculation process, and a cultivated crop Working condition database 7, which stores conditions for determining the work contents from the type and growth index Growth curve database 8 storing a conditional expression for calculating a growth index from the type of crop and weather conditions, a satellite image database 9 storing information of satellite images in a target range, and a growth index at a fixed point observation point And a pixel-corrected growth index database 11 that stores a corrected growth index for each pixel of the satellite image calculated by analyzing the satellite image.

The central processing unit 1 which is an agricultural field management device has a data input process 13, a data display process 14, a corrected growth index calculation process 15, a work content determination process 16, and a database access process 17.
The data input process 13 is a process for receiving weather data provided from the weather information site 10 via the Internet 11 and data input by the terminal device 2.
The data display process 14 is a process of drawing a field on map data as background data, changing the display method according to the date and type of work, and displaying the field on the terminal device 2.
The database access processing 17 refers to and updates the data in the map database 3, the field database 4, the weather database 5, the corrected growth index database 6, the work content database 7, the growth curve database 8, and the satellite image database 9 as necessary. It is processing.

The data stored in each database will be described.
FIG. 2 is a diagram showing the data structure of the field data stored in the field database 4.
The field data has a field number 22 for identifying the field. Further, it has a graphic ID 21 and shape information 24 for specifying a graphic, and a display color 23 for displaying the graphic. Further, the field data includes information such as a cultivation year 25, a crop variety 26, a farmer name 27, soil information 28, and a growth index initial value 29. It also holds work content data related to the field.
The work content data is associated with each field number, and a work ID 29 is set for each work content, and data such as work date 30, work content 31, item 32, and numerical value (quantity) 33 corresponding to the item. It consists of
The field shape information 24 represents the shape type as a coordinate value, and is composed of data of the number of vertices on the two-dimensional plane of the field and the X and Y coordinate values of each vertex.

FIG. 3 is a diagram showing the data structure of weather data stored in the weather database 5.
The meteorological data is data such as meteorological data ID 31 for specifying the meteorological data, date 32 when the meteorological data is acquired, mesh ID 33 and temperature 34 of the mesh to which the meteorological data corresponds, precipitation 35, sunshine duration 36, and solar radiation 37. It is configured.
The mesh represents an observation section of weather data, and each mesh has a mesh ID 301, a lower left X coordinate value 302, a lower left Y coordinate value 303, an upper right X coordinate value 304, and an upper right Y coordinate value 305. . Weather data is registered in descending order with respect to date 32. In addition, a record storing the normal value of each item is also registered.

FIG. 4 is a diagram showing the data structure of the work condition data stored in the work condition database 7.
The work condition data includes a work condition ID for uniquely identifying the work condition, a crop variety as a condition, a growth index as a condition for generating the work, a work ID indicating the work to be generated, and the like.
The work data has a work ID for uniquely identifying the work, a crop type, work contents representing the work contents, items, and quantity calculation formulas of values set in the items. The quantity calculation formula is, for example, a formula for calculating the amount of fertilization of nitrogen that yields the maximum when the work content is fertilization and the item is nitrogen, and can calculate soil information, weather data, and the like of the field as parameters. Set the expression. These work condition data are constructed based on experience and models.

FIG. 5 is a diagram showing a data structure of a growth curve stored in the growth curve database 8.
The growth index is an index that represents the growth status of a crop, for example, an index that represents the number of leaves or the height of a crop. The growth curve data includes crop index, growth index boundary to which the curve is applied, its conditions, and a growth index calculation formula. This data is constructed based on experience and models.
For example, the growth index calculation formula can be expressed as [Growth index] = i + [Effective integrated temperature] / c, where i is the initial value of the growth index and c is the integrated temperature required to increase the growth index by one. it can.
Here, the effective integrated temperature is a value obtained by integrating the effective temperature from the calculation start date to the target date. The effective temperature T is a lower limit value of the temperature effective for growing the crop, and b is the growing value of the crop. Assuming that the upper limit value of the effective temperature is T = 0 when [daily average temperature] <a, T = [daily average temperature] −a, b when a <= [daily average temperature] <= b. In the case of <= [daily average temperature], T = b−a.

FIG. 6 is a diagram showing the data structure of the satellite image data stored in the satellite image database 9.
The satellite image data is composed of an image file name having date of photographing, coordinate data (lower left X coordinate, lower left Y coordinate, upper right X coordinate, upper right Y coordinate) and data of wavelength bands of red wavelength and near infrared wavelength. . This data is registered by input from the terminal device 2.

  FIG. 7 is a diagram showing a data structure of fixed point growth index data stored in the fixed point growth index database 10, and for each fixed point ID, the X coordinate, the Y coordinate, the measurement date, and the measurement date are measured. Consists of growth index data. This data is created based on the growth index at the actual observation point.

FIG. 8 is a diagram showing a data structure of pixel corrected growth index data stored in the pixel corrected growth index database 11.
Pixel-corrected growth index data consists of the image date used for correction, the geographical coordinates indicating the rectangle, the vegetation index, and the corrected growth index for each correction ID, and is superimposed on the map data and field data for central processing. It can be displayed on a display terminal attached to the device 1. This data is registered by the corrected growth index calculation processing 15.
The vegetation index is an index that is considered to represent the amount and activity of vegetation from the reflection characteristics of the spectrum.

  FIG. 9 is a diagram showing the data structure of the corrected growth index data stored in the corrected growth index database 6. The corrected growth index data is composed of the image shooting date used for the correction, the field number of the related field, and the corrected growth index. This data is registered by the corrected growth index calculation processing 15.

FIG. 10 is a flowchart showing the processing procedure of the corrected growth index calculation processing 15.
In the corrected growth index calculation process 15, first, in step 101, the satellite image database 9 is referred to, a satellite image file is read, a vegetation index is calculated for each pixel from the red wavelength and near infrared wavelength data, and each pixel is calculated. Then, it is stored in the pixel corrected growth index database 11 in the format of FIG.
The formula of the vegetation index NDVI is as follows: red wavelength is R and near infrared wavelength is IR.
NDVI = (IR−R) / (IR + R).
Next, in step 102, the fixed point actual value of the growth index is input from an input device (not shown) attached to the central processing unit 1 and stored in the fixed point growth index database 10.

Next, in step 103, the vegetation index at the observation position of the growth index measured value is acquired from the pixel corrected growth index database 11.
Next, in step 104, a vegetation index acquired from the pixel corrected growth index database 11 and a regression curve of the growth index measured value are calculated. The regression curve is calculated, for example, by a least square method using the vegetation index as the X axis and the growth index actual measurement value as the Y axis.

Next, in step 105, a corrected growth index is calculated from the vegetation index of each pixel using a regression curve, and is stored in the pixel corrected growth index database 11 in the format of FIG. 8 for each pixel. Further, the coordinate value of each field is retrieved from the field database 4, and the corrected growth index in the area overlapping each field is retrieved from the pixel corrected growth index database 11 based on the coordinate value, and the value of the pixel corrected growth index thus retrieved is retrieved. Are stored in the corrected growth index database 6 for each field number in the format of FIG. 9, and the process is terminated.
Here, each pixel represents a range of the lower left X coordinate, the lower left Y coordinate, the upper right X coordinate, and the upper right Y coordinate of the satellite image, and the satellite image of the target farm field includes a plurality of pixels. By averaging the pixel-corrected growth index values of the target field, the average pixel-corrected growth index of the target field is obtained.

FIG. 11 shows the layer structure of the map database 3, the field database 4, and the corrected growth index database 6.
The map data stored in the map database 3 has a layer structure with a plurality of layers as shown in FIG. 11, road figures are stored in the road layer 116, river figures are stored in the road 117, When all the layers in the map database 113 are overlapped, the same image as the map 111 is obtained.
Similarly, the field data stored in the field database 4 is the 2005 field figure for the 2005 field layer 118, the 2004 field figure for the 2004 field layer 119, and the 2003 field figure for the 2003 field layer 120. Are stored for each year, and can be displayed superimposed.
Similarly, the corrected growth index data stored in the corrected growth index database 6 has a multi-layer structure for each year, and can be displayed superimposed on a field layer, a road layer of a map, or the like.

Next, the processing procedure of the work content determination processing 16 will be described with reference to the flowchart of FIG.
First, in step 121, a calculation start date and a calculation end date are set from the input device.
Next, in step 122, the field to be processed is acquired from the field database 4.
Next, in step 123, a search is performed to determine whether the weather information on the predicted date of the mesh ID of the weather mesh in which the field to be processed exists exists in the weather DB5. If it exists, the meteorological data of the target date is acquired in step 124. If it does not exist, normal weather data is acquired from the weather DB 5 in step 125.

Next, in step 126, a growth index calculation formula that satisfies the conditions of the crop varieties and growth indexes of the field to be processed is searched from the growth curve database 8. That is, a search is made for a growth curve that matches the boundary condition of the growth curve database 8 among the growth curves that match the crop varieties in the field. And the weather data acquired at step 124 or step 125 are applied, and a growth prediction value (growth index) is calculated. The predicted growth value is the aforementioned growth index.
In this case, when the growth index is predicted for the first time, a growth index calculation formula that matches the boundary conditions of the growth curve database 8 is searched using the growth index initial value 29 of FIG.

Next, in step 127, the corrected growth index database 6 is searched from the corrected growth index database 6 for growth correction data (corrected growth index) on the calculation date. If there is, the predicted growth value is replaced with a corrected growth index in step 128.
Next, in step 129, the operation content database 7 is searched for work contents exceeding the predicted growth value of the condition on the calculation day in the work of the crop varieties. That is, the work condition DB 7 is obtained that matches the crop varieties in the field, and the work contents satisfying the growth prediction value of the previous day <growth index condition <= the growth prediction value of the processing day are retrieved.
If there is, the work content / item is extracted in step 130 and registered in the work content data of the field database 4.
In step 131, the calculation date is advanced by one day. In step 132, it is determined whether or not the calculation date exceeds the calculation end date. If it exceeds, it is determined whether or not there is an unprocessed field in step 133, and if not, the processing ends.

  FIG. 13 is an example in which the corrected growth index is displayed superimposed on the field graphic. Displays the difference in the distribution of growth index in the field and supports the decision making of work.

  FIG. 14 is an example in which farm fields are displayed in different colors under the condition of work contents. For example, a harvesting work schedule is supported by setting the condition of the work content as harvesting and displaying the color code in the early and late days of the work day.

  FIG. 15 shows an example in which work on a specific field is displayed in time series. In each field, support decision making to perform appropriate work at appropriate time.

It is a system configuration figure showing an embodiment of the invention. It is a figure which shows the data structure of agricultural field data. It is a figure which shows the data structure of weather data. It is a figure which shows the data structure of work data. It is a figure which shows the data structure of a growth curve. It is a figure which shows the data structure of satellite image data. It is a figure which shows the data structure of fixed point growth index data. It is a figure which shows the data structure of pixel correction | amendment growth index data. It is a figure which shows the data structure of field correction | amendment growth index data. It is a flowchart which shows a correction | amendment growth index calculation process step. It is a figure which shows the layer structure of a map database, a field database, and correction | amendment growth index data. It is a flowchart which shows work content determination processing. It is a figure which shows the example of a display of a correction | amendment growth index. It is a figure which shows the example of a color-coded display of the field on condition of work content. It is a figure which shows the example which displayed the operation | work of the specific agricultural field in time series.

Explanation of symbols

1 Central processing unit (management device)
2 terminal device 3 map database 4 field database 5 weather database 6 corrected growth index database 7 working condition database 8 growth curve database 9 satellite image database 10 fixed point growth index database 11 pixel growth index database 12 weather information site 13 data input processing 14 data display Process 15 Corrected growth index calculation process 16 Work content determination process

Claims (4)

A field management support method for supporting farm work for each crop variety in a managed field,
A support device that supports farm work in the field
Calculate the vegetation index representing the amount and activity of the cultivars of the crop varieties in the managed field based on the wavelength components of the red wavelength and near-infrared wavelength of the satellite image data that captured the area including the managed field. A first step of storing in
A second step of accepting a fixed-point measured value of a growth index representing the growth status of a crop in a managed field and storing it in a fixed-point growth index database;
A third step of obtaining, from the growth index database, a vegetation index at the observation position of the fixed point actual measurement value of the growth index;
Fourth step of calculating a regression curve based on the vegetation index acquired from the growth index database and the fixed point measured value of the growth index by the least square method with the vegetation index as the X axis and the fixed index measured value of the growth index as the Y axis When,
A fifth step of calculating the growth index correction data in the management target field by the regression curve and storing it in the corrected growth index database;
A sixth step of acquiring, from the corrected growth index database, correction data of a growth index corresponding to the management target field, based on the coordinate values of the management target field;
A seventh step of displaying the correction data of the growth index corresponding to the management target field superimposed on the graphic representing the management target field for each year in a display form according to the growth state;
A field management support method comprising: supporting farm work in a management target field. 2. The field according to claim 1, wherein the seventh step includes a step of drawing and outputting a figure of the field to be managed in a display form corresponding to a work content corresponding to a growing state of the crop variety. Management support method. A field management support system for supporting farm work for each crop variety in a managed field,
  A support device that supports farm work in the field
Calculate the vegetation index representing the amount and activity of the cultivars of the crop varieties in the managed field based on the wavelength components of the red wavelength and near-infrared wavelength of the satellite image data that captured the area including the managed field. A first means for storing
A second means for accepting a fixed-point measured value of a growth index representing a growth status of a crop in a management target field and storing it in a fixed-point growth index database;
A third means for obtaining a vegetation index at the observation position of the fixed point actual measurement value of the growth index from the growth index database;
A fourth means for calculating a regression curve based on the vegetation index obtained from the growth index database and the fixed point actual measurement value of the growth index using the vegetation index as the X axis and the fixed index actual value of the growth index as the Y axis by the least square method. When,
A fifth means for calculating correction data for a growth index in a field to be managed by the regression curve and storing the correction data in a corrected growth index database;
A sixth step of acquiring, from the corrected growth index database, correction data of a growth index corresponding to the management target field, based on the coordinate values of the management target field;
A seventh means for displaying the correction data of the growth index corresponding to the management target field in superposition with the graphic representing the management target field for each year in a display form according to the growth state;
A farmland field management support system characterized by supporting farm work in a field to be managed. The field according to claim 3, wherein the seventh means includes means for drawing and outputting a figure of the field to be managed in a display form corresponding to the work content corresponding to the growth state of the crop variety. Management support system.

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