Precision agriculture with YOLO-Leaf: advanced methods for detecting apple leaf diseases.

Abstract

The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection methods face significant challenges. To address these issues, we propose a new model called YOLO-Leaf. Specifically, YOLO-Leaf utilizes Dynamic Snake Convolution (DSConv) for robust feature extraction, employs BiFormer to enhance the attention mechanism, and introduces IF-CIoU to improve bounding box regression for increased detection accuracy and generalization ability. Experimental results on the FGVC7 and FGVC8 datasets show that YOLO-Leaf significantly outperforms existing models in terms of detection accuracy, achieving mAP50 scores of 93.88% and 95.69%, respectively. This advancement not only validates the effectiveness of our approach but also highlights its practical application potential in agricultural disease detection.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から検出するYOLO-Leafモデルを開発・評価しており、植物病害フェノタイピング手法が研究の中心である。

Code and data availability

The paper uses two public Kaggle competition image datasets (Plant Pathology 2020-FGVC7 and 2021-FGVC8) for apple leaf disease detection, but the supplied blocks contain no authors' public code, model checkpoint, or data deposit; there is no data availability statement with an authors' URL, and the only allowed URL is,

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