JP2012150068A - Method for predicting wet and dry condition of soil and device for the same - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for predicting wet and dry condition of soil and a device for the same which are capable of excellently obtaining an image of a field without being affected by cloud and the like, and accurately creating a wet and dry condition map of the soil without requiring an analysis of a humus content of the soil.SOLUTION: A method for predicting wet and dry condition of soil comprises: taking visible images of a field immediately after a rainfall and in a dry period 42 and 44 by a helicopter 40 for aerial photographing; taking samples of soil 50 from several locations in the field and analyzing a volume moisture content 52 and vapor phase ratio 54 thereof; calculating a difference in light amount corrected color separation value data obtained by correcting light amount of color separation value data 72 for red, green and blue in the images at the locations where soil samples are taken; creating maps 76 and 78 between difference data 74 of the two images and the volume moisture content 52 as well as the vapor phase ratio 54 to obtain a regression formula; and obtaining a wet and dry condition map 82 of the entire field using the regression formula.

Description

  The present invention relates to a method and apparatus for predicting dryness and wetness of soil, and specifically relates to a method and apparatus for predicting dryness and wetness of soil using a visible image.

If we can predict the wet and dry (dry / wet) of the soil from the visible image of the field,
(1) General imaging devices such as video cameras and digital cameras can be used, and sensors are inexpensive.
(2) Soil humus content, nitrogen nutrition status, or soil fertility can be estimated from visible light reflection data.
Therefore, there is an advantage that it can be used for fertilizer management as well as the quality of drainage.

  From previous studies, it is known that the correlation between the soil humus content and the luminance value in the visible region is lower than that in the dry season in images containing a lot of soil moisture or in the wet season. The reflection of light in the visible range is considered to reflect mainly the soil humus content and wet and dry conditions.

  As a prior art regarding dry and wet prediction of soil, for example, there is one described in Non-Patent Document 1 below. It is shown that the soil volume moisture content can be estimated from the multiple regression equation using the reflection data of the green region and the soil humus content as the explanatory variables using the reflection data of the visible region and the landsat data.

National Agricultural Research Organization for Agricultural and Food Industry Hokkaido Agricultural Research Center Research Report 175, 47-115 (2002), Tetsuya Hatanaka “Development of high-accuracy and detailed evaluation method for field soil productivity factors using Landsat TM data `` Studies on soil management based on it '' (especially P68-72)

However, the above-described background technique has the following disadvantages.
(1) Since the satellite image is used, there is a possibility that the target field cannot be photographed even on a cloudy day or a day without a cloud due to the satellite recurrence period.
(2) Analyzes are made based on the images taken for a certain period of time, and the soil humus content in addition to the luminance value of the images is used as an explanatory variable, so an appropriate soil moisture estimation formula cannot be obtained due to the problem of multicollinearity. there is a possibility.
(3) Soil moisture data at the time of satellite image capture is required to create a regression equation.
(4) In order to create a wet and dry map, it is also necessary to create a humus content map.

  On the other hand, if the difference in the light reflection from the soil can be used as a prediction parameter using the visible images immediately after the rain of the field and the dry season, the analysis of the soil humus content will be unnecessary and appropriate. It is thought that the wetness and dryness of soil can be estimated. In addition, if a helicopter for aerial photography is equipped with a digital camera and a light intensity sensor for correcting the light intensity at the time of shooting, a field image can be taken without being affected by the return cycle of clouds and satellites. You can shoot.

  The present invention focuses on the above points, can obtain a good field image without being affected by clouds, etc., does not require analysis of soil humus content, and creates a soil wet / wet map with high accuracy. It is an object of the present invention to provide a method and apparatus for predicting soil dryness and wetness.

  The present invention obtains a visible image of the land when wet and dry, and the amount of light at the time of capturing each visible image, and samples the soil of the land at a plurality of sampling points by an aerial helicopter. The volume moisture content or gas phase rate is obtained, and then the visible image and the light quantity are used to obtain light quantity corrected color separation value data when the visible image is wet and dried at the sampling point. The difference data is calculated, and the correlation between the volume moisture content or the gas phase rate at the sampling point and the difference data is obtained.

  In another invention, a helicopter for aerial photography obtains a visible image of the land when wet and dry, and the amount of light at the time of capturing each visible image, and samples the soil of the land at a plurality of sampling points. The volumetric water content or the gas phase rate is obtained, and then the visible image and the light quantity are used to obtain the light separation corrected color separation value data when the visible image is wet and dried at the sampling point. In addition, the difference data is calculated, the correlation between the volume moisture content or the gas phase rate at the sampling point and the difference data is obtained, and the volume moisture content or the gas phase rate and the difference are obtained from the obtained correlation. A regression equation with data is obtained, and a dry / wet map for the entire land is created using the regression equation.

  The above and other objects, features and advantages of the present invention will become apparent from the following detailed description and the accompanying drawings.

  According to the present invention, the use of an aerial helicopter takes two visible images when wet and dry, and predicts dry and wet using the difference between them. An image of the land can be obtained satisfactorily without receiving it, and the analysis of the soil humus content is unnecessary, and a dry and wet map of the soil can be created with high accuracy.

It is a block diagram which shows the apparatus structure of the Example of this invention. It is a flowchart which shows the procedure of the dry / wet prediction in the Example of this invention. It is a figure which shows the example of the visible image immediately after the rain of the agricultural field used as a process target, the example of the visible image at the time of drying, and the mode of color separation of a pixel. It is a figure which shows the example of the map which shows the relationship between the difference data of a luminance value, and a gaseous-phase rate. It is a figure which shows an example of the dry / wet map obtained by the present Example. It is a figure which shows the difference in the color of the soil by a point.

  Hereinafter, the best mode for carrying out the present invention will be described in detail based on examples.

  First, the basic concept of the present invention will be described. In general, when moisture is contained in soil, the soil color becomes dark and the reflection amount of light in the visible range such as blue, green, and red decreases. From this, when viewed under the same soil color condition, it can be determined that the darker image (in other words, the lower the reflection amount of blue, green, and red) is, the wetter condition is.

  On the other hand, the original soil color varies depending on the location. An example is shown in FIG. FIGS. 2A and 2B show soil colors at different points, but both points show the condition under dry conditions. Comparing the two, it can be seen that the soil of (B) is clearly blacker than the soil of (A). Accordingly, if the wet and dry conditions are determined simply by the shade of the soil color, the soil of (B) is determined to be in a wet condition than the soil of (A).

  From this point of view, in order to grasp the soil moisture, it is necessary to grasp the amount of change in the soil color with respect to the initial value when wet (original soil color), that is, how much it has become darker than the initial value. There is.

  From the above viewpoint, in the present invention, the soil moisture is estimated by calculating the difference between the light intensity corrected luminance values when dry and when wet. That is, when Ref (when dry) is the luminance value reflecting the original soil color and Ref (when wet) is the original soil color + luminance value reflecting the soil moisture, soil moisture = Ref (when dry)-Ref (When wet). By such a method, the change amount of the soil color in consideration of the difference in the original soil color can be calculated, and the soil moisture can be estimated with high accuracy.

  FIG. 1 shows a block diagram of a dry / wet predicting apparatus 10 according to the first embodiment of the present invention. The dry / wet predicting apparatus 10 is configured using a general computer system, and is configured around a CPU 12. An input device 14 such as a keyboard, a display device 16 such as a liquid crystal display, an output device 18 such as a printer, a program memory 20 and a data memory 30 are connected to the CPU 12.

  A dry / wet prediction program 22 is stored in the program memory 20 and is executed by the CPU 12 so that a dry / wet prediction process is performed. The procedure is shown as a flowchart in FIG.

The data memory 30 stores data necessary for the wet / dry prediction process and data obtained by the process. As data necessary for the wet and dry prediction process,
(1) The visible image 42 immediately after the rain of the farm field to be processed, obtained by the aerial imaging helicopter 40, the light amount data 43 at the time of imaging, the visible image 44 at the time of drying, the light amount data 45 at the time of imaging,
(2) Volume moisture content 52 and gas phase rate 54, which are analysis results of soil 50 sampled at several locations in the field.
(3) Position coordinate data 62 of the soil sampling point and the four corner points of the field acquired from the GPS (Global Positioning System) 60,
There is.

In addition, as data obtained by the wet and dry prediction process,
(1) A corrected image 70 obtained by geometrically correcting the visible image on the GIS (Geographic Information System) using the position coordinate data 62 by the GPS 60,
(2) Color separation value (luminance value of each color component) data 72 that is a luminance value of each color component of red (R), green (G), and blue (B) extracted from the corrected image 70,
(3) The light quantity corrected color separation value 73 corrected based on the color separation value data 72 and the light quantity data 43 and 45 obtained from the light quantity sensor.
(4) difference data 74 indicating the difference between the color separation value data 72 when wet and when dry;
(5) Volume moisture content map 76 as an analysis result of the soil sampling,
(6) Gas phase rate map 78 as an analysis result of the soil sampling,
(7) Regression equation 80 obtained from the difference data 72, the volume moisture content map 76, and the gas phase rate map 78,
(8) A wet and dry map 82 created based on the visible images 42 and 44 and the regression equation 80,
There is.

  Next, the operation of this embodiment will be described with reference to FIGS. FIG. 2A shows a processing procedure in the present embodiment as a flowchart. First, using the helicopter 40 for aerial photography, the visible images 42 and 44 at the time of rain (when wet) and when dry are taken in the target field. Further, the light amount data 43 immediately after the rain and the light amount data 45 during drying are obtained using the light amount sensor. The aerial helicopter 40 may be manned, but an unmanned helicopter is used in this embodiment.

  FIGS. 3A and 3B show examples of the visible images 42 and 44. Although the illustrated example is black and white, it is actually a color image. Immediately after the rain, the soil 50 is sampled at several points in the field and the volumetric moisture content 52 and the gas phase rate 54 are analyzed in accordance with the image capturing. Further, the four corners of the soil sampling point and the field are measured using the GPS 60, and the position coordinate data 62 is obtained (step SA). These data are stored in the data memory 30 of the wet / dry predictor 10.

  Next, in the dry / wet prediction apparatus 10, the dry / wet prediction program 22 stored in the program memory 20 is executed by the CPU 12, and the following processing is performed. First, the visible images 42 and 44 are geometrically corrected on the GIS based on the position coordinate data 62 of the four corners of the field to obtain a corrected image 70 of the visible images 42 and 44 (step SB).

Next, the light quantity corrected color separation value 73 is calculated using the blue, green, and red color separation value data 72 and the light quantity data 43 and 45 obtained from the light quantity sensor in both corrected images 70 after geometric correction. As a result, the light amount of the color separation value data 72 is corrected and the luminance value is normalized (step SC). An example of a calculation formula for the light quantity corrected color separation value 73 is as follows.

  Next, the blue, green, and red light intensity corrected color separation values 73 at the soil sampling point are taken out from both images after geometric correction, and are exported onto spreadsheet software (for example, Microsoft Excel) (step SD). Next, a difference between the light intensity corrected color separation value 73 of each color is calculated on a spreadsheet software, and the difference data 74 and a map of the volume moisture content 52 and the gas phase rate 54 at each sampling point obtained by analysis (Scatter chart) 76 and 78 are created (step SE). Then, on the maps 76 and 78, the regression equation having the highest correlation coefficient is adopted as an estimation equation for the wet and dry (volume moisture content or gas phase rate), and set as a regression equation 80 (step SF).

  For example, looking at the visible images in FIGS. 3A and 3B, the image of the field is trapezoidal for aerial photography, and this is corrected. Next, as shown in FIGS. 3C and 3D, among the pixels PW1, PW2, PW3,..., PD1, PD2, PD3,. , Red, green, and blue color separation value data (luminance value data) 72 are respectively extracted. The color separation value data at the position P1 of the visible image 42 at the time of rainfall are RW1, GW1, and BW1. On the other hand, the color separation value data at the position P1 of the visible image 44 at the time of drying are RD1, GD1, and BD1. The same applies to pixels at other sampling points. Next, the light quantity corrected color separation value 73 is calculated for these data using the light quantity data 43 and 45. Thereafter, the light quantity corrected color separation value 73 is accumulated on the spreadsheet software, and the difference is calculated. For example, in the pixel P1, if the color separation value of the red data RD1 at the time of drying is “150” and the color separation value of the red data RW1 immediately after the rain is “70”, the difference data is 150−70 = 80. It becomes. The same applies to green and blue. Such calculation is performed for each sampling point.

  FIG. 4 shows an example of a map 78 of the difference data 74 at each sampling point obtained as described above and the gas phase rate 54 at each sampling point obtained by analysis. FIGS. 9A to 9C show the relationship between the difference in luminance values of blue, green, and red and the gas phase rate, respectively. The horizontal axis of the graph is the difference in luminance value between immediately after rain (May 8) and when dry (May 19), and the vertical axis is the gas phase rate (%) on May 8. Both are shown for the A field and the B field, respectively. From these maps, the regression equation with the highest correlation coefficient is adopted as the estimation equation for the gas phase rate, and is set as the regression equation 80. For example, in the case of blue in FIG. 4A, assuming that the gas phase rate is y, the difference value is x, and the correlation coefficient is r, the regression equation is “y = −2.2738x + 47.868, r = −0.77”. Become. The green and red colors in FIGS. 4B and 4C are also as shown. Although FIG. 4 shows the case of the gas phase rate, the regression equation 80 can be obtained by creating a map for the volume moisture content in the same manner.

  Next, the regression equation 80 obtained as described above is programmed on the GIS, and a wet-dry map (volume moisture content or gas phase) based on the corrected image 70 of the two visible images 42 and 44 and the regression equation 80 is used. A distribution chart of absolute values of rates) 82 is created (step SG). That is, the relationship between the difference data 74 and the absolute values of the volumetric water content and the gas phase rate is determined from the regression equation 80. On the other hand, since the difference data 74 can be obtained for all the pixels included in the corrected image 70 of the visible images 42 and 44, the dry / wet map 82 for the entire field is obtained by using the regression equation 80. be able to.

  FIG. 5 shows, as an example of the dry / wet map 82 obtained as described above, a map of the gas phase rate in the case of green in FIG. In this example, the shade of the color corresponds to the gas phase rate, the darkest part is 1 to 10%, the darkest part is 11 to 15%, and so on. That is, the dense portion has a low gas phase rate (high moisture or high moisture), and the thin portion has a high gas phase rate (low moisture). The same applies to the volumetric moisture content.

Thus, according to the first embodiment, there are the following effects.
(1) Since the field is photographed using a helicopter for aerial photography with a high degree of freedom, it is possible to obtain image information both immediately after raining and during drying without being affected by clouds.
(2) Since the regression equation for predicting dryness and wetness is obtained using the difference between the brightness values of the field images when dry and wet as explanatory variables, dryness and wetness prediction is performed without the need to analyze the soil humus content. be able to. In particular, since the data immediately after the rain is used, the wet and dry map immediately after the rain which is strongly demanded at the farming site can be created with high accuracy.

  Next, a second embodiment of the present invention will be described. In the above embodiment, the absolute value of the volumetric water content and the gas phase rate immediately after the rain is necessary, but the absolute value of the volumetric water content and the gas phase rate immediately after the rain is unnecessary, and relative dry and wet is acceptable. The processing of steps SP to SR shown in FIG.

  First, the aerial imaging helicopter 40 is used to capture visible images 42 and 44 immediately after the rain of the target field and during drying, respectively. Further, the four corners of the soil sampling point and the field are measured using the GPS 60, and the position coordinate data 62 is obtained (step SP). These data are stored in the data memory 30 of the wet / dry predictor 10. The analysis of the volumetric moisture content 52 and the gas phase rate 54 based on the soil analysis is the same as in the above embodiment.

  Next, in the dry / wet predicting apparatus 10, the dry / wet predicting program 22 stored in the program memory 20 is executed by the CPU 12, and the following processing is performed. First, the visible images 42 and 44 are geometrically corrected on the GIS based on the position coordinate data 62 of the four corners of the field to obtain a corrected image 70 of the visible images 42 and 44 (step SQ). Next, the light quantity corrected color separation value 73 is calculated using the blue, green, and red color separation value data 72 and the light quantity data 43 and 45 obtained from the light quantity sensor in both images 70 after geometric correction. Thereby, the light intensity correction of the color separation value data 72 is performed, and the luminance value is normalized (step SR). This is the same as step SC in FIG.

  Next, the red, green, and blue color separation value data 72 of the soil sampling point is extracted from both images after geometric correction, and the difference is calculated. The difference data 74 and the volume at each sampling point obtained by the analysis are calculated. Maps 76 and 78 of the moisture content 52 and the gas phase rate 54 are created (step SR). The map may use any difference data 74 of blue, red, and green. Thereby, the map shown in FIG. 4 is obtained. From this map, the relative relationship between the value of the difference data and the gas phase rate can be known.

  In the above-described first embodiment, a regression equation creation work using the absolute values of the volumetric water content 52 and the gas phase ratio 54 obtained by soil analysis is required, but relative wet and dry conditions are estimated as in this embodiment. If only, soil sampling and analysis are not required.

In addition, this invention is not limited to the Example mentioned above, A various change can be added in the range which does not deviate from the summary of this invention. For example, the following are also included.
(1) In the above-described embodiment, the case where the processing is performed in units of pixels of the captured image has been described. However, the field may be divided into meshes of a predetermined size and the processing may be performed in units of meshes.
(2) The apparatus configuration, various numerical values and mathematical formulas shown in the above embodiments are merely examples, and the present invention is not limited to them.
(3) The present invention is effective for extracting poorly drained areas, but can be used for extracting local irrigation areas and the like, such as grasping a place where it is easy to dry. Moreover, although farmland is the main application target, if it is bare land conditions, it does not prevent applying wet / dry prediction to places other than farmland.

  According to the present invention, since the regression equation for dry / wet prediction is obtained using the difference between the light intensity corrected luminance values of the field image when dry and wet as an explanatory variable, a dry / wet map of soil can be obtained with high accuracy. It is suitable for extraction of poor drainage areas.

10: Dry / wet prediction device 12: CPU
14: Input device 16: Display device 18: Output device 20: Program memory 22: Dry / wet prediction program 30: Data memory 40: Aerial helicopter 42, 44: Visible image 43, 45: Light quantity data 50: Soil 52: Volume moisture Rate 54: Gas phase rate 60: GPS
62: Position coordinate data 70: Corrected image 72: Color separation value data 73: Light amount corrected color separation value 74: Difference data 76: Volume moisture content map 78: Gas phase rate map 80: Regression equation 82: Dry / wet map P1: Position BD1, BW1, GD1, GW1, RD1, RW1: Color separation value data PW1, PW2, PW3, PD1, PD2, PD3: Pixel

Claims (4)

Obtaining a visible image of the land when wet and dry, and the amount of light when each visible image is captured, by an aerial helicopter;
Collecting the soil of the land at a plurality of sampling points to obtain a volume moisture content or a gas phase rate;
Using the visible image and the light amount to obtain light amount corrected color separation value data when the visible image is wet and dry at the sampling point, and calculating difference data thereof;
Obtaining a correlation between the volumetric water content or gas phase rate at the sampling point and the difference data;
A method for predicting soil wetness and dryness. Obtaining a visible image of the land when wet and dry, and the amount of light when each visible image is captured, by an aerial helicopter;
Collecting the soil of the land at a plurality of sampling points to obtain a volume moisture content or a gas phase rate;
Using the visible image and the light amount to obtain light amount corrected color separation value data when the visible image is wet and dry at the sampling point, and calculating difference data thereof;
Obtaining a correlation between the volumetric water content or gas phase rate at the sampling point and the difference data;
A step of obtaining a regression equation of the volumetric water content or gas phase rate and difference data from the obtained correlation;
Creating a wet and dry map for the entire land using the obtained regression equation;
A method for predicting soil wetness and dryness. An apparatus for performing the soil wet / dry prediction method according to claim 1,
Wet and dry of the visible image at a plurality of sampling points by using the data of the visible image of the land when wet and dry obtained by the helicopter for aerial photography and the amount of light at the time of photographing each visible image Means for obtaining the light intensity corrected color separation value data and calculating the difference data thereof,
Means for obtaining a correlation between the difference data and the volume moisture content or the gas phase rate using the data of the volume moisture content or the gas phase rate obtained by sampling the soil of the land at the sampling point;
A soil dry / wet pre-equipment comprising a dry / wet predicting means. An apparatus for performing the soil wet / dry prediction method according to claim 2,
Wet and dry of the visible image at a plurality of sampling points by using the data of the visible image of the land when wet and dry obtained by the helicopter for aerial photography and the amount of light at the time of photographing each visible image Means for obtaining the light intensity corrected color separation value data and calculating the difference data thereof,
Means for obtaining a correlation between the difference data and the volume moisture content or the gas phase rate using the data of the volume moisture content or the gas phase rate obtained by sampling the soil of the land at the sampling point;
Means for obtaining a regression equation between the volumetric moisture content or the gas phase rate and difference data from the obtained correlation;
Means for creating a wet and dry map for the entire land using the obtained regression equation;
A soil dry / wet pre-equipment comprising a dry / wet predicting means.