YOLO-Based Leaf Disease Detection in Smart Agriculture: Evolution, Task Adaptation, and Deployment Challenges

Computer vision-based leaf disease detection has become an important technical approach in smart agriculture and agricultural inspection services. Recent advances in artificial intelligence (AI) and deep learning have provided effective solutions for automated leaf disease monitoring, and You Only Look Once (YOLO)-based detectors have attracted increasing attention for balancing detection accuracy and inference speed. However, leaf disease detection remains challenging because lesions often occupy small regions, exhibit subtle visual features, have blurred boundaries, and appear against complex backgrounds, with considerable variation under field conditions. To address these challenges, numerous studies have improved lightweight backbones, feature fusion mechanisms, attention modules, detection heads, loss functions, and training strategies. This critical narrative review examines YOLO-based leaf disease detection, organizing reported adaptations by task characteristics and interpreting their outcomes in relation to datasets, annotations, and evaluation conditions. Representative studies report gains in detection performance, model compactness, or measured inference speed, together with trade-offs that depend on the task and configuration. Nevertheless, several challenges remain, including limited dataset diversity, insufficient cross-crop and cross-environment generalization, inadequate consideration of annotation uncertainty, biased evaluation of lightweight models based only on parameter reduction, and the gap between benchmark performance and real-world deployment. Future research should focus on task-oriented optimization, robust visual perception, improved model generalization, and efficient deployment toward intelligent agricultural monitoring systems.