Plant diseases pose a major threat to agricultural productivity and global food security, underscoring the need for efficient detection and management systems. This review examines a wide spectrum of plant health issues, including fungal infections, bacterial diseases, viral infections, and nutrient deficiencies. Recent advances in Computer Vision (CV) and Deep Learning (DL) have revolutionized crop disease diagnosis by providing non-destructive, rapid, and precise alternatives to traditional manual inspection. We systematically analyzed 265 peer-reviewed studies indexed in PubMed, Scopus, and Web of Science, selected using predefined inclusion and exclusion criteria to ensure methodological rigor and relevance. The review covers RGB, multispectral, and hyperspectral imaging systems, as well as DL architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Vision Transformers (ViTs), and vision-language models. Findings indicate that hyperspectral imaging combined with advanced models such as ViTs achieves superior accuracy for pre-symptomatic detection, while RGB imaging with efficient CNNs remains the most practical and scalable solution for field-level deployment due to its cost-effectiveness and computational efficiency. Persistent challenges include the scarcity of large-scale annotated datasets, domain generalization issues, and limited interpretability of complex models, all of which hinder real-world adoption. By bridging academic research with practical agricultural applications, this review highlights both the scientific effectiveness of emerging technologies and their translational potential in Precision Agriculture. Overall, the findings underscore the transformative role of DL in agricultural monitoring and provide actionable guidance for deploying these technologies in farming environments.
