CN111695723B - Ku-waveband-based prediction method for rice yield in dual phenological period based on unmanned airborne SAR - Google Patents
Ku-waveband-based prediction method for rice yield in dual phenological period based on unmanned airborne SAR Download PDFInfo
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Abstract
A method for predicting the yield of rice in a two-phenological period based on a Ku-waveband unmanned aerial vehicle-mounted SAR comprises a paddy field, an unmanned aerial vehicle, a Ku-waveband Synthetic Aperture Radar (SAR) and RTK equipment, and is characterized in that: acquiring a paddy field image of a Ku waveband unmanned aerial vehicle SAR after rice transplanting; randomly extracting 20 transplanting points, recording the positions of the samples in the images, and simultaneously marking corresponding geographic coordinates by using RTK equipment; when the rice in the paddy field is mature, finding a rice sample at a corresponding position through the geographic coordinates recorded by RTK equipment, randomly extracting 3 rice ears at the pier where the sample point is located, and counting the number of particles y1, y2 and y3 of each rice ear; and finally, extracting the backscattering coefficient of each transplanting point, and predicting the rice grains of each pier through the fitting relation between the number of rice spikes of each pier and the transplanting points after tillering. The invention has the advantages that: the technology adopts a novel unmanned aerial vehicle technology and is matched with the Ku waveband airborne SAR, so that the remote and accurate prediction is realized, the prediction quality and efficiency are greatly improved, and the technology is simple and reliable and has good market prospect.
Description
Technical Field
The invention relates to a prediction method of dual-phenological-period rice yield based on a Ku-band unmanned airborne SAR, belonging to the technical field of aerospace remote sensing, in particular to the field of agricultural microwave remote sensing.
Background
In life, some yields often need to be predicted, especially for rice that we eat daily. China is the world with the highest rice yield, the total yield is more than 2 hundred million tons, the country with large rice yield is a world with the rice planting area of about 3 million hectares and the second place in the world. The rice yield can be accurately predicted, and the national production plan can be formulated. The previous rice yield prediction is usually determined manually or according to experience, and cannot be predicted accurately, so that the problem is needed to be solved urgently.
Disclosure of Invention
The invention aims to provide a method for predicting the yield of rice in a dual phenological period based on a Ku-band unmanned airborne SAR.
The invention aims to solve the problem of inaccurate prediction of the yield of the existing rice.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
a method for predicting the yield of rice in a two-phenological period based on a Ku-waveband unmanned aerial vehicle-mounted SAR comprises a paddy field, an unmanned aerial vehicle, a Ku-waveband Synthetic Aperture Radar (SAR) and RTK equipment, and is characterized in that: acquiring a paddy field image of a Ku waveband unmanned aerial vehicle SAR after transplanting rice, wherein the paddy field image comprises a flat water surface and sparse rice seedlings serving as backgrounds; randomly extracting 20 transplanting points, counting the seedling plant number n1, n2... n20 of each sample point, and calculating the backscattering coefficient sigma0 1、σ0 2…σ0 20Recording the position of the sample in the image and simultaneously marking the corresponding geographic coordinate by using RTK equipment; when the rice in the paddy field is mature, finding a rice sample at a corresponding position through the geographic coordinates recorded by RTK equipment, collecting the number x1 and x2.. x20 of rice plants of a pier where a sample point is located (a rice sample point after tillering of a rice transplanting point), randomly extracting 3 rice ears from the pier where the sample point is located, and counting the number y1, y2 and y3 of particles of each rice ear; firstly, according to the number n of seedlings at a sample point and the backscattering coefficient sigma of the seedlings0Linear fit relationship of (1): n = k σ0+ b, k, b are constants; then establishing a linear relation between the number n of the sample point seedlings and the number x of the tillered rice plants: x = l n + c, l, c being a constant; rice of each sample of mature riceThe statistic model of the number of the ear particles comprises Num = (y1+ y2+ y3)/3 x of the number of rice ears per pier of rice; num = (y1+ y2+ y3)/3 [ l ] (k × [ sigma ])0+b)+c](ii) a The expected value ζ of (y1+ y2+ y3) was estimated from the statistical analysis, and the number of grains per mound Num = ζ/3 [ (. k. sigma.) ]0+b)+c]And rounding up. And finally, extracting the backscattering coefficient of each transplanting point, and predicting the rice grains of each pier through the fitting relation between the number of rice spikes of each pier and the transplanting points after tillering.
The invention has the advantages that: the technology adopts a novel unmanned aerial vehicle technology and is matched with the Ku waveband airborne SAR, so that the remote and accurate prediction is realized, the prediction quality and efficiency are greatly improved, and the technology is simple and reliable and has good market prospect.
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FIG. 1 is a front view of a prediction method of rice yield in a dual-phenological period based on a Ku-band unmanned airborne SAR;
in the figure: 1. paddy field 2, unmanned aerial vehicle 3, Ku wave band Synthetic Aperture Radar (SAR) 4, RTK equipment.
Detailed Description
The invention is further described with reference to the following figures and examples.
A prediction method for rice yield in dual-phenological period based on Ku-waveband unmanned aerial vehicle-mounted SAR comprises a paddy field 1, an unmanned aerial vehicle 2, a Ku-waveband Synthetic Aperture Radar (SAR) 3 and RTK equipment 4, wherein the paddy field 1 after 50 mu of rice transplanting is selected in a certain grain production base in Zhejiang province, the unmanned aerial vehicle 2 flies in the paddy field 1, the Ku-waveband Synthetic Aperture Radar (SAR) 3 is carried on the unmanned aerial vehicle 2, a paddy field image of the Ku-waveband unmanned aerial vehicle-mounted SAR is obtained, 20 rice transplanting points are randomly extracted from the paddy field image, the number n1, n n2... n20 of each sample point is counted, and the backscattering coefficient sigma is0 1、σ0 2…σ0 20Recording the position of the sample in the image and simultaneously marking the corresponding geographic coordinate by using the RTK equipment 4; when the rice in the paddy field 1 is mature, the corresponding position of the rice sample is found through the geographic coordinates recorded by the RTK equipment 4, the number of the rice plants of the pier where the sample point is located (the rice sample point after the seedling transplanting point is tillered) is collected, and the number of the rice plants is x1, x2Randomly extracting 3 rice ears by mounds, and counting the particle number y1, y2 and y3 of each rice ear; firstly, according to the number n of seedlings at a sample point and the backscattering coefficient sigma of the seedlings0Linear fit relationship of (1): n = k σ0+ b, k, b are constants; then establishing a linear relation between the number n of the sample point seedlings and the number x of the tillered rice plants: x = l n + c, l, c being a constant; the rice ear particle number statistical model of each sample of the rice in the mature period comprises the following steps: num = (y1+ y2+ y3)/3 x for each mound of rice spike; num = (y1+ y2+ y3)/3 [ l ] (k × [ sigma ])0+b)+c](ii) a The expected value ζ of (y1+ y2+ y3) was estimated from statistical analysis, and the number of grains per mound Num = ζ/3 [ (. k. sigma.) ] per mound0+b)+c]And rounding up. And finally, extracting the backscattering coefficient of each transplanting point, and predicting the rice grains of each pier through the fitting relation between the number of rice spikes of each pier and the transplanting points after tillering.
The using method comprises the following steps: selecting a 50-mu rice field 1 after transplanting rice seedlings in a certain grain production base in Zhejiang province, after transplanting rice seedlings on the rice field 1, using an unmanned aerial vehicle 2 carrying a Ku-band Synthetic Aperture Radar (SAR) 3 to fly on the rice field 1, acquiring a paddy field image of a Ku-band unmanned aerial vehicle carrying the SAR, randomly extracting 20 rice transplanting points on the paddy field image, wherein the number of the rice seedlings of each rice transplanting point is respectively: 4. 6, 4, 5, 4, 5, 4, 6, 4, 3, 5, 4, backscattering coefficient: -12.36, -11.57, -12.28, -12.13, -12.23, -12.92, -12.16, -13.16, -12.68, -11.97, -12.34, -11.62, -12.71, -11.82, -12.63, -13.21, -12.14, -12.47, -12.23, -12.56, recording the position of each rice transplanting point in the image of the paddy field, while marking the corresponding geographical coordinates with the RTK device 4; when the rice in the paddy field 1 is mature, finding a rice sample point at a corresponding position through a geographic coordinate recorded by an RTK device 4, collecting the number of rice plants 13, 19, 14, 17, 16, 12, 14, 13, 11, 16, 14, 16, 12, 19, 12, 11, 15 and 13 of a pier where the rice sample point is located (the rice sample point after tillering of a rice transplanting point), randomly extracting 3 rice ears as samples from the pier where each rice sample point is located, and counting the number of particles of each ear, wherein the number of the particles is 1: 154. sample nos. 175, 155, 2: 137. sample nos. 150, 147, 3: 150. sample number 147, 187, 4: 175. sample nos. 138, 147, 5: 157. sample nos. 135, 160, 6: 141. sample nos. 120, 110, 7: 114. sample nos. 135, 128, 8: 138. sample No. 128, 135, 9: 119. 155, 159, sample No. 10: 144. sample nos. 151, 147, 11: 146. sample nos. 152, 131, 12: 143. sample nos. 151, 126, 13: 136. sample numbers 142, 153, 14: 123. sample nos. 154, 131, 15: 166. sample nos. 134, 142, 16: 155. sample nos. 137, 161, 17: 127. 166, 152, sample No. 18: 144. sample nos. 132, 158, 19: 166. sample No. 141, 127, 20: 134. 148, 131. The average per ear particle (y1+ y2+ y3)/3 for 20 samples were: 161. 145, 162, 154, 151, 124, 126, 134, 145, 148, 143, 140, 144, 136, 148, 151, 149, 145, 138.
According to the linear fitting relation of the number of seedlings at the sample point and the backscattering coefficient:
n=1.4503*σ0+ 22.425; then establishing a linear relation between the number of the sample point seedling plants and the number of the tillered rice plants: x =3.61n-2, substituting n into: x =5.24 σ ·0+ 79; the statistic model of the number of rice ear particles of each sample of the rice in the mature period comprises the number of rice ears per mound, Num = (y1+ y2+ y3)/3 x; the mean value was 435 particles from 20 point survey samples, and the expected value ζ =400 × 0.05+410 × 0.15+430 × 0.45+450 × 0.2+470 × 0.05+480 × 0.1=437 particles was estimated after simplified processing (y1+ y2+ y3), and ζ was substituted into the model as the value (y1+ y2+ y 3): num =437/3 (5.24 σ:)0+79) to get the real number rounded up to get the number of rice grains per mound. For 20 mound of rice grains sampled, the following were: 2074. 2677, 2135, 2249, 2173, 1646, 2227, 1463, 1830, 2372, 2089, 2639, 1807, 2485, 1868, 1425, 2242, 1990, 2173, 1921; the measured values are 1658, 2141, 2763, 2877, 2765, 2111, 1699, 1901, 2392, 1875, 1677, 2100, 1441, 1963, 1451, 1816, 1711, 2490, 1734 and 2401, and compared with the positive and negative values of the 20 mound rice grains, the accuracy is 75-80%, and the feasibility is high.
Claims (1)
1. A dual phenological period rice yield prediction method based on a Ku-band unmanned aerial vehicle-mounted SAR comprises a rice field (1), an unmanned aerial vehicle (2), a Ku-band Synthetic Aperture Radar (SAR) (3) and an RTK device (4), selecting a 50 mu rice transplanting paddy field (1) in a certain grain production base in Zhejiang province, flying an unmanned aerial vehicle (2) on the paddy field (1), installing a Ku-band Synthetic Aperture Radar (SAR) (3) on the unmanned aerial vehicle (2), acquiring a paddy field image of a Ku-band unmanned aerial vehicle SAR after the Ku-band Synthetic Aperture Radar (SAR) (3) shoots a picture of the paddy field (1), randomly extracting 20 seedling transplanting points on a paddy field image, counting the seedling plant number n1, n2... n20 and the backscattering coefficient sigma 01, sigma 02.. sigma 020 of each sample point, recording the position of the sample in the image and simultaneously marking corresponding geographic coordinates by using RTK equipment (4); when the rice in the rice field (1) is mature, finding a rice sample at a corresponding position through the geographic coordinates recorded by the RTK equipment (4), acquiring the number x1 and x2.. x20 of rice plants of a pier where a sample point is located, wherein the pier represents the rice sample point after a seedling transplanting point is tillered, randomly extracting 3 rice ears from the pier where the sample point is located, and counting the number y1, y2 and y3 of particles of each rice ear; firstly, according to a linear fitting relation of the number n of seedlings at a sample point and a backscattering coefficient sigma 0 of the seedlings: n = k σ 0+ b, k, b being a constant; then establishing a linear relation between the number n of the sample point seedlings and the number x of the tillered rice plants: x = l n + c, l, c being a constant; the rice ear particle number statistical model of each sample of the rice in the mature period comprises the following steps: num = (y1+ y2+ y3)/3 x for each mound of rice spike; num = (y1+ y2+ y3)/3 [/(k × σ 0+ b) + c ]; estimate the expected value ζ of (y1+ y2+ y3) from statistical analysis, the number of grains per mound Num = ζ/3 [ | (k × σ 0+ b) + c ], rounded up; and finally, extracting the backscattering coefficient of each transplanting point, and predicting the rice grains of each pier through the fitting relation between the number of rice spikes of each pier and the transplanting points after tillering.
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