YOLO-ODD: an improved YOLOv8s model for onion foliar disease detection.

Abstract

Onion crops are affected by many diseases at different stages of growth, resulting in significant yield loss. The early detection of diseases helps in the timely incorporation of management practices, thereby reducing yield losses. However, the manual identification of plant diseases requires considerable effort and is prone to mistakes. Thus, adopting cutting-edge technologies such as machine learning (ML) and deep learning (DL) can help overcome these difficulties by enabling the early detection of plant diseases. This study presents a cross layer integration of YOLOv8 architecture for detection of onion leaf diseases viz .anthracnose, Stemphylium blight, purple blotch (PB), and Twister disease. The experimental results demonstrate that customized YOLOv8 model YOLO-ODD integrated with CABM and DTAH attentions outperform YOLOv5 and YOLO v8 base models in most disease categories, particularly in detecting Anthracnose, Purple Blotch, and Twister disease. Proposed YOLOv8 model achieved the highest overall 77.30% accuracy, 81.50% precession and Recall of 72.10% and thus YOLOv8-based deep learning approach will detect and classify major onion foliar diseases while optimizing for accuracy, real-time application, and adaptability in diverse field conditions.

Plant phenotyping relevance

タマネギ葉の病害状態を画像から検出・分類するYOLOv8改良手法の開発と比較評価が中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper's onion foliar disease image dataset (1000 field images, annotated in Roboflow) and trained YOLOv8 model are paper-specific assets, but no public repository or URL is provided; the data availability statement only promises access upon request. The Roboflow format link and Google Play link are generic tools, i

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