Spectral imaging is a technique that captures and analyzes the spectral information of an object, such as its reflectance, transmittance, or fluorescence. It has been widely used in various fields, such as remote sensing, and food quality assessment. In recent years, spectral imaging has also emerged as a promising tool for crop disease diagnosis, as it can provide rapid, non-destructive, and accurate detection of plant pathogens and symptoms. This review aims to provide a concise overview of the principles, methods, applications, and challenges of spectral imaging in crop disease diagnosis. First, we introduce the basic remote sensing concepts and types of spectral imaging, such as hyperspectral, and multispectral imaging. Second, we discuss the main steps and techniques involved in spectral imaging analysis, such as image acquisition, processing, feature extraction, and classification. Third, we present some representative examples and applications of spectral imaging in crop disease diagnosis, such as fungal, bacterial, viral, and nematode infections. Finally, we highlight the importance of artificial intelligence integration alongside the current limitations and future directions of spectral imaging in crop disease diagnosis.
