A comprehensive review on AI-based crop disease detection using leaf image classification and explainable AI

Abstract

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.

Plant phenotyping relevance

葉画像から植物の病害状態を推定する手法を体系的にレビューし、モデル、データセット、検証、実運用上の課題を中心に扱っているため。

Code and data availability

This is a PRISMA-style review of AI-based crop disease detection. It contains no authors' own phenotyping measurements, image datasets, analysis code, or trained models. Table 1 lists public datasets (PlantVillage, PlantCity, MangoLeafBD, etc.), but these are cited prior-work resources, not paper-specific assets, and a

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