Improved monitoring of southern corn rust using UAV-based multi-view imagery and an attention-based deep learning method

作者：Zhengang Lv, Binyuan Xu, Liheng Zhong, Gengshen Chen, Zehua Huang, Rui Sun, Wei Huang, Feng Zhao, Ran Meng · 发表于：Computers and Electronics in Agriculture · 年份：2024 · DOI：10.1016/j.compag.2024.109232 · 被引用次数：21 · 研究领域：Smart Agriculture and AI、Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications

Southern corn rust (SCR) is a significant foliar disease, which can result in substantial corn yield losses. Unmanned aerial vehicle (UAV)-based optical remote sensing presents a promising method for efficiently monitoring SCR in field conditions. Nevertheless, its performance can be challenged by SCR’s bottom-up pathogenesis and canopy occlusion in field conditions. In contrast, multi-view imaging spectral measurements (MISM) can mitigate the effects of canopy occlusion by collecting complementary information from different corn layers. However, a method to optimize the fusion of MISM for precise and efficient monitoring of SCR remains absent. In this study, an attention-based deep learning method was introduced for the automated extraction of critical spectral and spatial information from MISM to monitor SCR. First, the optimal spectral features for SCR were explored and identified. Second, the improvements in SCR monitoring were evaluated by comparing MISM with the sole use of nadir-view. Finally, the performances of two attention-based deep learning algorithms (i.e., attention-based Fully Connected Network (FCN) and attention-based Convolutional Neural Network (CNN)) were assessed against the baseline Random Forest (RF) algorithm. These results indicated that: (1) spectral indices based on green–red–red edge bands were most effective for SCR monitoring across various views; (2) MISM significantly improved the accuracy of SCR monitoring compared to that of nadir-view imagi...