Accurate and timely detection of crop diseases is essential for global food security and sustainable agriculture. This review provides a comprehensive analysis of recent advancements in leaf image-based crop disease detection using machine learning, deep learning (DL), convolutional neural networks, vision transformers, and emerging state-space models. Following PRISMA guidelines, the review systematically examines peer-reviewed studies (2021–2025) and evaluates datasets, model architectures, validation protocols, and deployment constraints. Unlike previous surveys, this review uniquely integrates explainable artificial intelligence (XAI) with emphasis on physiology-based interpretability and real-world field usability. Key findings reveal that while DL models achieve > 95% accuracy on controlled datasets like PlantVillage, performance degrades significantly under field conditions due to dataset bias, domain shifts, and multi-disease complexity. Critical research gaps are identified, including insufficient annotation standards, limited cross-domain validation, and computational constraints for edge deployment. Emerging directions include agriculture-specific foundation models, domain-generalizable vision systems, lightweight mobile strategies, and next-generation XAI frameworks aligned with plant physiology. By bridging technical advancements with agronomic interpretability, this work provides researchers and practitioners with a roadmap for developing trustworthy, field-deployable AI systems for sustainable crop health management. Graphical abstract The alternative text for this image may have been generated using AI.