Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation

作者：Yi Zhang, Yizhe Yang, Qinwei Zhang, Runqing Duan, Junqi Liu, Yuchu Qin, Xianzhi Wang · 发表于：Remote Sensing · 年份：2022 · DOI：10.3390/rs15010007 · 被引用次数：51 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Leaf Properties and Growth Measurement

Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the floweri...