基于数码相机的玉米冠层SPAD遥感估算

doi:10.3864/j.issn.0578-1752.2018.15.005

Abstract

Abstract: 【Objective】Chlorophyll is an important pigment in plant photosynthesis. The objective of this study is to investigate the inversion of chlorophyll content using crop spectrum information, so as to provide an important basis for real-time monitoring and diagnosis of crop growth.【Method】Based on the field environment under different nitrogen fertilizer application levels (0, 50% and 100%) of maize, the light and small UAV equipped with consumer level digital camera was used to obtain the RGB image of the test area, and then the soil adjusted vegetation index was used for image segmentation. 15 common visible vegetation indexes were extracted based on images before and after segmentation. Then the correlation between vegetation index and SPAD values were analyzed, besides single variable regression model, multiple regression model and random forest regression model based on visible vegetation indexes were established to estimate the SPAD values. And then, the indicators of accuracy evaluation, coefficient of determination, root mean square error, mean relative error and P<0.01 were used to select the best indicators and the optimal model.【Result】There was a significant correlation between VI_plot and VI_plantvegetation indexes and the SPAD value of maize canopy, for example, the correlation coefficient between normalized redness intensity (NRI), normalized pigment chlorophyll ratio index (NPCI), blue red ratio index (BRRI) and SPAD value of VI_plant was above 0.77. The univariate regression models were built, which took NRI,NPCI, BRRI and DVI as the independent variables and the measured SPAD as dependent variable, including linear, exponential, logarithmic, two degree polynomial and power function models, and among those models, the two polynomial model constructed by the NRI index was the best one with the decision coefficient R² of 0.7976, the RMSE of 4.31, and the MRE of 5.91%; the precision of the model using the random forest regression algorithm was the highest, in which the determining coefficient was 0.8682, the RMSE was 3.92, and the MRE was 4.98%; the multiple regression model had higher accuracy than any single variable regression model, in which the decision coefficient R² was 0.819, RMSE was 4, and MRE was 5.67%. The six inversion models of SPAD were used to make the distribution map of corn canopy SPAD value, and then the map using random forest regression model had the best result which was the closest to real SPAD distribution with R² of 0.8247，RMSE of 4.3，MRE of 5.36％, therefore which could be used as a main method of corn canopy chlorophyll monitoring information.【Conclusion】The results showed that the application of UAV digital imagery in retrieving SPAD of corn was feasible, which also added new means and experience to the application of UAV remote sensing system in agriculture.

Key words: unmanned aerial vehicle, digital camera, SPAD value, random forest regression algorithm

HE Ying, DENG Lei, MAO ZhiHui, SUN Jie. Remote Sensing Estimation of Canopy SPAD Value for Maize Based on Digital Camera[J].Scientia Agricultura Sinica, 2018, 51(15): 2886-2897.

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Remote Sensing Estimation of Canopy SPAD Value for Maize Based on Digital Camera

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