Fusing UAV multiple data and phenology to predict crop biomass

作者：Shuaijie Shen, Wenjie Li, Jun Zou, Matthew Tom Harrison, Shouyang Liu, Ehsan Eyshi Rezaei, Ke Liu, Bahareh Kamali, Zechen Wang, Datong Zhang, Axiang Zheng, Fu Chen, Xiaogang Yin · 发表于：Information Processing in Agriculture · 年份：2025 · DOI：10.1016/j.inpa.2025.09.001 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications

Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle data (UAV) appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above-ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms—Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)—to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha −1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple...