Hyperspectral Remote Sensing for Monitoring Crop Disease: Applications, challenges, and perspectives

Abstract

Crop disease presents significant threats to global food security and agricultural sustainability. Traditional monitoring methods, reliant on visual inspections and laboratory analyses, are labor intensive and unsuitable for large-scale implementation. Hyperspectral remote sensing has emerged as a promising tool for operational crop disease monitoring. Here, we provide a broad review, starting with a hyperspectral-based description of observable symptoms of common crop disease and then examining hyperspectral features, including spectral and textural features, pigment light absorption, solar induced chlorophyll fluorescence (SIF), temporal information, and auxiliary data. We also analyze the algorithms used for disease detection, including traditional statistical methods, machine learning (ML)-based methods, and physically based methods. The review highlights the effectiveness of these methods in distinguishing various stressors, detecting early disease, assessing crop resistance, and monitoring large-scale disease. Additionally, we present two case studies of uncrewed aerial vehicle (UAV)-based hyperspectral imaging for maize leaf spot monitoring. Based on a quantitative literature review, we summarize current research trends. Future research should emphasize integrating physical models with deep learning (DL), ensuring the sensitivity and robustness of spectral features and promoting international data sharing