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Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction
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  • Journal title : Journal of Biosystems Engineering
  • Volume 41, Issue 2,  2016, pp.126-137
  • Publisher : Korean Society for Agricultural Machinery
  • DOI : 10.5307/JBE.2016.41.2.126
 Title & Authors
Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction
Yun, Hee Sup; Park, Soo Hyun; Kim, Hak-Jin; Lee, Wonsuk Daniel; Lee, Kyung Do; Hong, Suk Young; Jung, Gun Ho;
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Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of < 60 days after sowing (DAS). With the M-statistic method, the test plots under different treatments could be discriminated in the early seasons of <41 DAS, showing a value of M > 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.
Barley cover cropping;Excess green;Image processing;M-statistic method;UAV;Vegetation index;
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