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

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 imaging spectral measurement (∼10 % improvements in both OA and Macro F1); (3) the integration of an attention mechanism with FCN or CNN, which considers the varied importance of multi-view spectral features, further improved the accuracy of SCR monitoring. The highest accuracy in SCR monitoring (OA: 82.1 %, Macro F1: 0.82) was achieved by the attention-based FCN, with a ∼7 % improvement in both OA and Macro F1 relative to the RF. Overall, this study provides a promising method for efficiently monitoring SCR in field conditions, which could assist in making smart agriculture decisions and reducing pesticide inputs.