Estimation of maize seedling number based on UAV multispectral data
ZHAO Xiaowei1,2(), HUANG Yang1(), WANG Yongqiang1, CHU Ding1
1. Heilongjiang Provincial Research Institute of Surveying and Mapping, Harbin 150081, China 2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
To monitor and evaluate maize seedlings in Northeast China and estimate their number in time, this study provided effective support for the rapid estimation of the maize seedling number using unmanned aerial vehicle (UAV) remote sensing images. Using the multispectral UAV data, the color indexes ExG, GBDI, ExG-ExR, NGRDI, and GLI were compared to segment maize seedlings from the soil background. Then, the optimal threshold was determined using the Otsu algorithm, and ExG was selected as the optimal color index. According to optimization, the best combination of morphological parameters consists of area (A), perimeter (B), rectangle length (D), rectangle perimeter (G), ellipse long axis length (H), and shape factor (Q). Then, the number of maize seedlings was predicted using the support vector regression (SVR) model and the prediction accuracy was assessed. Finally, the spatial distribution map of the local maize seedling number was developed. Tests revealed that the accuracy and the statistical error of the SVR model were 96.54% and 0.6%, respectively. These results allow the number and growth trends of maize seedlings to be predicted quickly and accurately in a short time.
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