Apple Leaf Disease Detection Based on Improved YOLOv11 with DSSA Mechanism.

Abstract

Visual inspection of apple leaf diseases is inefficient and subjective, limiting large-scale orchard applications. To realize rapid and accurate disease identification, this paper proposes an improved YOLOv11 model integrated with a Dual Sparse Selection Attention (DSSA) module. By embedding the DSSA module into the key layers of the YOLOv11 backbone network, the model enhances fine-grained feature extraction for small and complex lesions while suppressing background interference. A tailored training strategy with an optimized learning rate and optimizer is designed to ensure stable convergence. Experiments are conducted on a dataset consisting of 7594 images covering four categories: black rot, rust, scab, and healthy leaves. The proposed model achieves precision of 0.973, recall of 0.978, mAP50 of 0.991, and 0.949 mAP50-95, outperforming YOLOv8, YOLOv9, YOLOv10, and the vanilla YOLOv11. Furthermore, a Qt-based visualization system is developed for practical orchard deployment. This method provides a reliable solution for intelligent apple leaf disease detection and smart orchard management.

Plant phenotyping relevance

リンゴ葉の病斑・健全状態を画像から推定する検出モデルを開発・比較し、実用システムまで構築しており、植物病害表現型の取得手法が中心である。

Code and data availability

The paper describes a custom apple leaf disease dataset (7594 images) and an improved YOLOv11+DSSA model, but provides no public dataset deposit, no author code/model release, and no supplement. The Data Availability Statement only says data are contained within the article, and no repository or URL for assets is given

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