Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop

作者：Hang Yin, Weili Huang, Fei Li, Haibo Yang, Yuan Li, Yuncai Hu, Kang Yu · 发表于：PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · 年份：2022 · DOI：10.1007/s41064-022-00218-8 · 被引用次数：31 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Smart Agriculture and AI

Abstract Spectral indices based on unmanned aerial vehicle (UAV) multispectral images combined with machine learning algorithms can more effectively assess chlorophyll content in plants, which plays a crucial role in plant nutrition diagnosis, yield estimation and a better understanding of plant and environment interactions. Therefore, the aim of this study was to use UAV-based spectral indices deriving from UAV-based multispectral images as inputs in different machine learning models to predict canopy chlorophyll content of potato crops. The relative chlorophyll content was obtained using a SPAD chlorophyll meter. Random Forest (RF), support vector regression (SVR), partial least squares regression (PLSR) and ridge regression (RR) were employed to predict the chlorophyll content. The results showed that RF model was the best performing algorithm with an R 2 of 0.76, Root Mean Square Error (RMSE) of 1.97. Both RF and SVR models showed much better accuracy than PLSR and RR models. This study suggests that the best models, RF model, allow to map the spatial variation in chlorophyll content of plant canopy using the UAV multispectral images at different growth stages.