Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 2; p. 453
• Main Authors: Liu, Shiyuan, Zhang, Bin, Yang, Weiguang, Chen, Tingting, Zhang, Hui, Lin, Yongda, Tan, Jiangtao, Li, Xi, Gao, Yu, Yao, Suzhe, Lan, Yubin, Zhang, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: absorption; Absorptivity; Agricultural production; Algorithms; altitude; Artificial intelligence; Chlorophyll; Data acquisition; Dry matter; dry matter accumulation; Experiments; Indicators; Interdisciplinary subjects; Leaf area; Learning algorithms; Leaves; Linear algebra; Low altitude; Machine learning; Model accuracy; Nitrogen; Physiological effects; physiological parameters; Physiology; Plant growth; Plants; Plants (botany); Radiation; reflectance; Remote sensing; Rice; Root-mean-square errors; soil; Soil analysis; Spatial discrimination; Spatial resolution; Spectral correlation; Support vector machines; UAV; Unmanned aerial vehicles; Vegetation; Vegetation index

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vege...