JP2008175537A - Method for forming calibration curve in remote sensing for calculating crop data - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To form a calibration curve more enhanced in measuring precision than before while a calibration curve maintains general properties capable of corresponding to various photographing conditions in the measurement in remote sensing for calculating crop data. <P>SOLUTION: This method for forming calibration curve includes: the process of measuring the reflected lights from the crops grown in a plurality of sections in a field at every section by the photographing due to a camera; the process of calculating the crop data of the crops in the field at every section by chemical analysis or the like; and the process of calculating the coefficient of multiple regression of a multiple regression formula predetermined using the data of the reflected light obtained by the photographing as an explanation variable, the crop data as an object variable and the dummy variable in the factor related to the photographing condition at the time of photographing. The dummy variable is used as the factor with respect to at least one of the altitude of the sun, the quantity and wavelength balance of solar rays, the positional relation between the sun and the camera and the kind or growing stage of crops to form the calibration curve. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a calibration curve creation method for improving measurement accuracy in measurement by remote sensing.

  Conventionally, as described in Patent Document 1, Patent Document 2 or Patent Document 3, the crop in the field is photographed in a state of looking down obliquely from the side of the field, and the image data in the field is obtained from the photographed image data. Remote sensing for crop information of crops is known. The calculation formula (hereinafter referred to as “calibration curve”) used to calculate crop information in these remote sensing is a multivariate analysis with reflected light of the crop by sunlight as the explanatory variable and the chemical analysis value of the crop as the objective variable. Has been created.

  When creating a calibration curve, various shooting methods are used to create a versatile calibration curve that can respond to various shooting conditions by reducing the influence of factors related to the light characteristics of the crop to be measured and the shooting conditions of sunlight. Sample data is collected by measuring on conditions or crops of different varieties, and multivariate analysis is performed using these sample data. In general, in multivariate analysis such as multiple regression analysis, it is considered that the measurement accuracy can be increased as the number of explanatory variables and objective variables increases, and Patent Document 1, Patent Document 2 or Patent Document 3 The calibration curve created by the described creation method can cope with various imaging conditions.

  However, when the measurement is performed under limited imaging conditions such as measuring only a specific variety, the calibration curve is created using only the sample data measured under the limited imaging conditions. There was a problem that the measurement accuracy was lower than the standard curve. For this reason, even though the calibration curve is versatile and can be used for various imaging conditions, even when measurement is performed only for a specific product type or imaging condition, It is desired to create a calibration curve that can maintain the same measurement accuracy.

JP 2000-350517 A JP 2001-045867 A JP 2006-320240 A

  In view of the above problems, the present invention is a versatile calibration curve that can cope with various imaging conditions in measurement by remote sensing, but also when measurement is performed only for a specific product type or imaging condition. It is a technical problem to enable creation of a calibration curve that can maintain the same measurement accuracy as a calibration curve created exclusively for limited conditions.

  In order to solve the above problems, the present invention, in remote sensing for calculating crop information in a field, measures reflected light from crops growing in a plurality of areas in the field for each of the areas by photographing with a camera. A process, a step of obtaining crop information of the crop in the field by chemical analysis or the like for each area, information of reflected light obtained by the photographing, explanatory variables, the crop information as objective variables, and the time of photographing And calculating a multiple regression coefficient of a predetermined multiple regression equation using a dummy variable as a factor relating to the shooting conditions of the above, including the solar altitude, the amount of sunlight and the spectral balance, the sun and the camera The method of creating a calibration curve using a dummy variable for at least one of the positional relationship, crop varieties or growth stages was taken.

  Moreover, in the remote sensing which calculates the crop information of the crop with the calibration curve created by the creation method according to claim 1, the step of measuring the reflected light from the crop growing in the field by photographing with a camera; By applying the reflected light information obtained by the imaging to the temporary calibration curve with the constant term of the calibration curve and the coefficient of the dummy variable set to 0, the crops growing in the area of a certain area in the field Calculating first crop information; measuring second crop information of a crop growing in the area by a method of directly irradiating the leaf blades of the crop; and the first crop information; And a step of calculating a difference from the second crop information, and a calibration curve creation method was adopted in which the difference is defined as a constant term value of the provisional calibration curve.

  Further, a remote sensing technical method for calculating crop information using a calibration curve created by the creation method according to claim 2 was taken.

  According to the present invention, since the dummy variable is used in the multivariate analysis at the time of creating the calibration curve, it is possible to reduce the influence on the measurement value due to the factors related to the imaging conditions. Factors related to the shooting conditions include the altitude of the sun, the intensity and spectrum balance of sunlight, the shooting position of the camera relative to the sun position, the variety of the crop or the growth stage, etc. Because it is replaced with a dummy variable as a factor, it is a versatile calibration curve that can handle various imaging conditions, but it also has excellent measurement accuracy even when measurements are limited to specific varieties and imaging conditions. Can be created.

  Further, in the present invention, a value corresponding to the bias component (constant term) of the calibration curve is obtained using a measurement value obtained by directly measuring a crop so as not to be affected by sunlight or the like. Difficult measurement is possible.

  The best mode for carrying out the present invention will be described below with reference to the drawings. In creating a calibration curve, first, a method for photographing a field by remote sensing will be described. Here, the case where the crop grown in the field is paddy rice and the measurement target is the nitrogen content of the paddy rice (rice body) will be described as an example. The present invention is not limited to paddy rice, and can be used in the case of crops such as upland rice and wheat, and the measurement target is not limited to the nitrogen content of the crop, but the nitrogen content of the leaf blades It can also be used to create a calibration curve such as the predicted value of protein contained in the grain after rate and harvest.

  Since there is a detailed description in Patent Document 1, Patent Document 2 or Patent Document 3 regarding remote sensing in which a field is photographed from above with a camera installed on the ground, only the outline will be described here. FIG. 1 shows an example of photographing a field 1, and a camera 3 is installed at a predetermined position toward the field 1 where paddy rice is grown. The field 1 is exposed to sunlight. The entire field 1 is included in the shooting range of the camera 3. The camera 3 is connected to the data processing device 20. The data processing device 20 may be any device as long as it can process image data captured by the camera 3. For example, a general laptop computer can be used. Reference numeral 21 denotes a portable measuring device, which will be described later, that measures crop information of the crop by a method of directly irradiating the leaf blades of the crop. Here, the farm field 1 may be a single farm field divided by “edges” or a “farm field group” including a plurality of farm fields.

  FIG. 2 is a simplified block diagram showing functions of the camera 3. The camera 3 includes a CCD 5. Further, a filter wheel 7 having a plurality of filters 6 is attached to the camera 3. By rotating the filter wheel 7 and switching the filter 6, it is possible to photograph with each filter 6. The light transmitted through the filter 6 is received by the CCD 5 through the condenser lens 8. The filter wheel 7 is rotated by a stepping motor 10 that is driven and controlled by the control circuit 9. Further, the control circuit 9 sends image data (reflected light) obtained by receiving light by the CCD 5 to the data processing device 20. The filter 6 may be appropriately selected from the wavelength in the visible light region and the wavelength in the near infrared region, corresponding to the measurement target of the crop to be measured. Although four filters 6 are shown in FIG. 2, they can be changed at any time according to the purpose, and the RGB R (red) signal, G (green) signal, and B (blue) signal can be changed by the filter 6. You may enable it to photograph each. The camera used in the present invention is not particularly limited as long as the photographed information can be digitized into the same state as the image data photographed by a digital camera equipped with a CCD. Note that two cameras may be used to acquire the R signal, the G signal, and the B signal with one camera, and the NIR signal in the near infrared region (NIR) with the other camera.

  What is received by the CCD 5 when photographing with the camera 3 is reflected light of the rice in the field 1 due to sunlight. The reflected light is received by the CCD 5 for each reflected light transmitted through each filter 6. Information on the reflected light obtained by receiving the light is sent to the data processing device 20. The incident light to the camera 3 is different between the reflected light from the field to the camera 3 and the reflected light from the field near the camera 3 and the reflected light from the field far from the camera 3. Therefore, it is preferable to correct a difference in incident light due to different incident angles.

  In the present invention, the calibration curve is created using the information (image data) of the reflected light of the rice plant sent to the data processing device 20. The information of the reflected light obtained by receiving the reflected light of the rice plant in the field 1 is divided into a plurality of areas of a certain area, and the average of the reflected light of the rice plants growing in the area of the certain area Value is obtained, and rice leaves are collected from a plurality of rice plants growing in the area of the predetermined area, and the nitrogen content of the collected rice leaves is directly obtained by chemical analysis such as Kjeldahl method, The average value is calculated. Then, the average value of the reflected light is an explanatory variable, the average value of the nitrogen content is a target variable, and a factor related to the imaging conditions is a dummy variable. A plurality of sample data is prepared using the reflected light information of the area, and a calibration curve is created by performing multivariate analysis on the sample data.

  A method for creating a calibration curve will be specifically described. In the calibration curve creation method of the present invention, for example, a calibration curve is created by the multiple regression equation (Formula 1) shown below.

In Equation 1, N is the nitrogen content of the measurement object, F0, F1, F2, F3, F4, E1, E2,..., _Em-1 are multiple regression coefficients, and R1, R2, R3, and R4 are the respective filters. , D1, D2,..., _Dm−1 represent dummy variables.

  The nitrogen content N is a value obtained by collecting rice leaves from a plurality of rice plants growing in a certain area in the field, and obtaining and averaging the nitrogen contents of the plurality of rice leaves by chemical analysis. The nitrogen content becomes an objective variable of sample data in the area.

  Information of each reflected light represented by R1, R2, R3, and R4 is an average value of the reflected light of the rice plants grown in the area. In the present invention, as reflected light information, R signal of RGB signal, G signal, B signal, and reflected light of four wavelengths in the NIR signal, which is a wavelength in the near infrared region, are measured. It is used as an explanatory variable for sample data in the area.

D1, D2,..., _Dm−1 are dummy variables that take “1” or “0” depending on m shooting conditions. In this embodiment, a dummy variable is provided for each image obtained by photographing a farm field. Factors relating to shooting conditions such as the altitude of the sun, the amount of sunlight, and the spectral balance change with time, so the factors change depending on the shooting time. In the present invention, since the reflected light of sunlight is measured, the photographing condition changes depending on the positional relationship between the sun and the camera. That is, in actual shooting by remote sensing, shooting conditions differ for each shot image. Therefore, in this embodiment, dummy variables (D1, D2,..., D _m−1 ) are provided for each photographed image, and combinations of dummy variables as shown in Table 1 are used.

  Since the altitude of the sun, the amount of sunlight, and the spectral balance change over time, if the shooting time is different, the altitude of the sun, the amount of sunlight, and the spectral balance that cause errors during measurement Will be different. For this reason, in the present invention, a dummy variable is set for each error factor such as the altitude of the sun, the amount of sunlight, and the spectral balance by setting one dummy variable for each captured image at each capturing time.

  In one piece of image data obtained by photographing a field, the field is divided into a plurality of areas having a certain area on the image data, and the reflected light information (R1) of rice grown in each area for each area. , R2, R3 and R4) and the nitrogen content N, a plurality of sample data can be obtained from one image. In addition, when the said image data image | photographs the agricultural field group containing a several agricultural field, each agricultural field can be made into one area.

  In addition, if sample data is obtained from each of a plurality of image data, a large number of sample data can be prepared. By performing multivariate analysis using these sample data in Equation 1, the multiple regression coefficient of Equation 1 can be obtained and a calibration curve can be created. In general, in multivariate analysis, it is considered that the accuracy of the calibration curve can be increased as the number of samples increases.

  The information (R1, R2, R3, and R4) of each reflected light in Equation 1 is preferably logarithmic, and it is desirable to perform multivariate analysis using a multiple regression equation as shown in Equation 2.

  As shown in Equation 2, logarithmically reflected light at each wavelength from the crop, and using the numerical value as an explanatory variable, the error factor mainly due to the state of sunlight includes a dummy variable as a bias. Can be replaced.

By the way, in this embodiment, a calibration curve is created by multivariate analysis using dummy variables in order to reduce the influence of factors relating to the photographing conditions, but dummy variables are set for each photographed image. Therefore, it is difficult to determine which dummy variable should be input with “1”. For this reason, the sum of the constant terms represented by the multiple regression coefficients F0 and the multiple regression coefficients E1 to Em ₋₁ (hereinafter referred to as “bias component”) of the calibration curve represented by the formula 1 or 2. "Corresponding to" intercept "as a linear function). For this reason, it is not possible to calculate an accurate amount of bias, and it is necessary to obtain a value corresponding to the amount of bias separately.

  In the present invention, this bias amount is obtained using, for example, a portable leaf component measuring device (hereinafter referred to as “measuring device 21”) as described in JP-A-10-96692. According to the measuring device 21, it is possible to easily measure the nitrogen content of the rice growing in the field, and to directly measure the rice leaves and to avoid the influence of sunlight etc. The content rate can be determined. In addition, about how to obtain | require a bias component, you may obtain | require by the method generally known other than using an apparatus like the measuring apparatus 21. FIG.

  Next, how to obtain the bias amount using the measuring device 21 will be described. In this case, Formula 3 shown below obtained by removing the term for calculating the bias from Formula 2 created by the above-described calibration curve creation method is used as the provisional calibration curve. Naturally, instead of the formula 2, a formula obtained by removing the term for calculating the bias amount from the formula 1 may be used.

  Here, it is assumed that the field X is captured by the camera 3 in a state where the provisional calibration curve (Equation 3) is stored in the data processing device 20. In this case, as shown in FIG. 3, the nitrogen content of the rice growing in the area Y of a certain area in the field X is the reflected light of the area Y among the information of each reflected light obtained by the photographing. Can be calculated by substituting the above information into the provisional calibration curve. It is assumed that this value is 4.0% (hereinafter referred to as “first crop information”). The shape and width of the area are not particularly limited, but considering that image data is processed by a personal computer, the shape is preferably a square, and particularly a square. When the shape is a quadrangle, the length of one side is preferably about 8 m.

  Next, it is assumed that the nitrogen content of the rice growing in the area Y is 3.1% (hereinafter referred to as “second crop information”) as measured by the measuring device 21. The measured value by the measuring device 21 is an average value obtained by measuring a plurality of rice leaves growing in the area Y. The measurement with the measuring device 21 may be performed by measuring one leaf per rice body, and the number of the leaves is preferably 20 or more.

  Since the first crop information can be stored in the data processing device 20 and the second crop information is stored in the measuring device 21, the second crop information is input to the data processing device 20 to obtain data. Store in the processing device 20. The input method may be either automatic or manual. In the data processing device 20, the difference between the first crop information and the second crop information is calculated. Here, since the first crop information is 4.0% and the second crop information is 3.1%, the first crop information is 0.9% higher than the second crop information. . Therefore, the difference is −0.9%, and in this example, this difference −0.9% is used as the bias (constant term) of the provisional calibration curve. Therefore, the provisional calibration curve is expressed by the following Equation 4,

It becomes. The calibration curve expressed by Equation 4 can be used to determine the nitrogen content of the rice plant in the field X taken to determine the bias. For example, by substituting the information (R1, R2, R3, and R4) of the reflected light in the zone Z into the calibration curve (Formula 4), the nitrogen content of the rice plant in the zone Z can be easily obtained. Although the calibration curve can be used for image data obtained by photographing a field different from the field X, there is a risk that the measurement accuracy may be reduced. The field X is not limited to a single field, and may be a field group including a plurality of fields. In this way, it is possible to easily measure the crop information of the crops cultivated in the field (here, the nitrogen content of rice) over a wide range. In general, the average value of the nitrogen content is obtained for each field, and this average value is used as the nitrogen content of each field.

  In the method for creating a calibration curve according to the present invention, dummy variables can be freely used for crop varieties, growth stages, and the like. Table 2 below shows the case where dummy variables are set for the crop varieties.

  Dummy variables can also be used for a plurality of imaging conditions, and Table 3 below shows combinations when dummy variables are set for each image and crop variety.

  As shown in Table 3, when two or more crop varieties are growing in one image, if the place where each variety is growing can be classified, one image More than one dummy variable can be set. In addition, when setting a dummy variable as shown in Table 3, when the variety is unknown and the growth stage is known, the dummy variable can be taken as shown in Table 4.

  Furthermore, when the crop varieties and the growth stages of the varieties can be confirmed, dummy variables can be taken as shown in Table 5.

Even when dummy variables are provided as shown in Tables 2 to 5, a calibration curve can be created by the calibration curve creation method described above.

  In the method of creating a calibration curve of the present invention, as shown in Equation 5 below, the reflected light information of the R (red) signal, the reflected light information of the G (green) signal, and the reflected light of the B (blue) signal And a multiple regression equation that takes the logarithm of the ratio of the information of the reflected light of the NIR signal.

  Even when a multiple regression equation is provided as in Equation 5, a calibration curve can be created by the calibration curve creation method described above.

  The method for creating a calibration curve using dummy variables according to the present invention is a remote in which a camera is mounted on a balloon, a radio-controlled flying device (airplane, helicopter), a manned airplane, an artificial satellite or the like in addition to photographing from the ground. It can also be used in sensing. The photographing from the ground means photographing with a camera so that the field is looked down from a height of 1 m to 30 m from the ground, and it is desirable to use a telephoto lens when photographing far away. By using a telephoto lens, it becomes possible to secure resolution at the time of analysis up to an image of a far image, and a wider range can be measured at a time.

It is explanatory drawing which showed the imaging | photography state. It is a schematic block diagram of the camera used for imaging | photography of a farm field. It is the figure which showed the imaging range of the agricultural field.

Explanation of symbols

1 Farm 3 Camera 5 CCD
6 Filter 7 Filter wheel 8 Lens 9 Control circuit 10 Stepping motor 20 Data processing device 21 Measuring device

Claims (3)

In remote sensing to calculate crop information in the field,
Measuring reflected light from crops growing in a plurality of areas in the field for each of the areas by photographing with a camera;
Obtaining the crop information of the crop in the field by chemical analysis or the like for each of the areas;
The multiple regression coefficient of a predetermined multiple regression equation is calculated using the reflected light information obtained by the photographing as an explanatory variable, the crop information as an objective variable, and a dummy variable as a factor relating to the photographing condition at the time of photographing. Process,
Creation of a calibration curve characterized in that dummy variables are used as at least one of the factors such as the altitude of the sun, the light intensity and spectral balance of the sun, the positional relationship between the sun and the camera, the variety of the crop or the growth stage. Method. In remote sensing that calculates crop information of a crop with a calibration curve created by the creation method according to claim 1,
Measuring the reflected light from the crop growing in the field by photographing with a camera;
By applying the reflected light information obtained by the imaging to a tentative calibration curve in which the constant term of the calibration curve and the coefficient of the dummy variable are 0, the number of crops growing in a certain area in the field Calculating one crop information;
Measuring second crop information of a crop growing in the area by a method of directly irradiating the leaf blades of the crop;
Calculating the difference between the first crop information and the second crop information,
A calibration curve creation method characterized in that a calibration curve is obtained by setting the difference as a value of a constant term of the provisional calibration curve. A method for measuring a crop by remote sensing, wherein the calibration curve created by the calibration curve creating method according to claim 2 is used.

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