Plant diseases are a major threat to global agricultural productivity and food security. Traditional methods of disease detection, relying on visual inspections and manual scouting, are time-consuming, subjective, and often inadequate for early-stage diagnosis. In response, image sensing technologies have emerged as innovative, non-invasive tools capable of detecting and managing plant diseases with high accuracy and efficiency. This review explores the principles, types, and applications of image sensing in plant disease management, focusing on imaging modalities such as Red, Green, Blue, multispectral, hyperspectral, thermal, and fluorescence imaging. These technologies, especially when integrated with artificial intelligence and machine learning, allow for early detection, disease classification, spatial mapping, and targeted treatment, supporting precision agriculture. Despite their potential, challenges such as high costs, technical complexity, environmental variability, and the need for standardized datasets hinder widespread adoption. Looking forward, advancements in artificial intelligence, edge computing, and mobile-based platforms, along with institutional support and farmer training, are expected to drive the scalable and sustainable use of image sensing in modern plant pathology.