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

Abstract

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.

Plant phenotyping relevance

植物葉の病害状態を画像から検出・分類するYOLO手法を中心に、データセットと性能を比較するレビューであり、植物表現型計測法のレビューとして適格。

Code and data availability

This is a review article. The datasets listed in Table 17 are cited third-party datasets from prior studies, not datasets generated or used for this paper's own measurements. The Data Availability Statement says data are available only from the corresponding author upon request, and no authors' analysis code, models,或补

No evidence-backed public reproduction asset is currently recorded.