Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs)

Plant leaf diseases (PLDs) can continue to be a significant problem in the agricultural sector, leading to significant losses in production and jeopardizing food security. Early detection is essential, and recent achievements in the domain of deep learning (DL) have made automated high-accuracy solutions possible. The most popular and commonly used of these is the You Only Look Once (YOLO) family of object detection models, which have been proposed to detect plant diseases in real time. This review presents a new and in-depth synthesis of YOLO-based methods, including YOLOv1 to YOLOv10 and the domain-specific variants, including CTB-YOLO (coriander), BED-YOLO (YOLOv10n), and RAG-augmented YOLOv8 (coffee). This work compares to previous surveys in that (i) it presents a structured dataset catalog containing information on size, resolution, disease classes, and limitations (such as imbalance and annotation problems); (ii) it provides comparative benchmarking analysis of performance measures (accuracy, precision, recall, F1-score, mean Average Precision, and frames per second) across versions of YOLO to illustrate trade-offs between speed and accuracy; and (iii) it gives forward-looking discussion on how (ii) open challenges and (iii) future research directions, including lightweight YOLO models to run on mobile. This review presents a summative reference and a new contribution to the progress of the YOLO-based PLD detection approach to sustainable agriculture.