Machine Learning Models for Early Detection of Plant Diseases Using Multispectral Imaging: A Comprehensive Review

Abstract

Plant diseases significantly affect crop productivity and global food security, making early detection essential for sustainable agriculture. Traditional visual diagnosis methods are often slow, subjective, and ineffective during early infection stages. Recent advances in multispectral and hyperspectral imaging combined with machine learning and deep learning techniques enable rapid, non-destructive, and accurate detection of plant diseases before visible symptoms appear. Spectral imaging captures physiological and biochemical changes in plants across visible, near-infrared, red-edge, and thermal bands, while algorithms such as SVM, Random Forest, CNN, RNN, and Vision Transformers effectively classify healthy and diseased plants. This review discusses imaging techniques, preprocessing methods, machine learning models, UAV-based sensing, explainable AI, and edge computing applications in precision agriculture, along with challenges and future directions for smart disease detection systems.