科研进展

【论文题目】 Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop

基于无人机成像的马铃薯作物叶绿素含量多时相制图

【作者】 Hang Yin, Weili Huang, Fei Li*, Haibo Yang, Yuan Li, Yuncai Hu, Kang Yu

尹航，黄卫丽， 李斐，杨海波，李渊，胡云才，于康

【摘要】

【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 estima tion 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 R2 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.

【Keywords】Chlorophyll content, Machine learning, Multispectral images, Potato, Unmanned aerial vehicle (UAV)