Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 3; p. 854
• Main Authors: Xu, Sizhe, Xu, Xingang, Blacker, Clive, Gaulton, Rachel, Zhu, Qingzhen, Yang, Meng, Yang, Guijun, Zhang, Jianmin, Yang, Yongan, Yang, Min, Xue, Hanyu, Yang, Xiaodong, Chen, Liping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Adaptive sampling; Algorithms; Background noise; Cameras; color; Correlation analysis; Crop growth; Crops; Data integration; Decision making; Decision trees; Digital imaging; Estimation; Fertilization; image fusion; Imaging techniques; leaf nitrogen content; Leaves; Machine learning; multispectral imagery; Neural networks; Nitrogen; nutrition; Optimization; Regularization; Remote sensing; removal of background noise; Rice; Rice fields; Saturation (color); Sensors; soil; Spatial discrimination; Spatial resolution; UAV remote sensing; Unmanned aerial vehicles; variable optimization; Variables; Vegetation; Vegetation index; Wheat; China

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existin...