YOLO-ACT: an adaptive cross-layer integration method for apple leaf disease detection.

Abstract

Apple is a significant economic crop in China, and leaf diseases represent a major challenge to its growth and yield. To enhance the efficiency of disease detection, this paper proposes an Adaptive Cross-layer Integration Method for apple leaf disease detection. This approach, built upon the YOLOv8s architecture, incorporates three novel modules specifically designed to improve detection accuracy and mitigate the impact of environmental factors. Furthermore, the proposed method addresses challenges arising from large feature discrepancies and similar disease characteristics, ultimately improving the model's overall detection performance. Experimental results show that the proposed method achieves a mean Average Precision (mAP) of 85.1% for apple leaf disease detection, outperforming the latest state-of-the-art YOLOv10s model by 2.2%. Compared to the baseline, the method yields a 2.8% increase in mAP, with improvements of 5.1%, 3.3%, and 2% in Average Precision, Recall, and mAP50-95, respectively. This method demonstrates superiority over other classic detection algorithms. Notably, the model exhibits optimal performance in detecting Alternaria leaf spot, frog eye leaf spot, gray spot, powdery mildew, and rust, achieving mAPs of 84.3%, 90.4%, 80.8%, 75.7%, and 92.0%, respectively. These results highlight the model's ability to significantly reduce false negatives and false positives, thereby enhancing both detection and localization of diseases. This research offers a new theoretical foundation and direction for future advancements in apple leaf disease detection.

Plant phenotyping relevance

リンゴ葉の病害状態を画像から検出・局在化するYOLOベース手法の開発と性能比較が中心であり、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 ALDD dataset is a manually curated fusion of two pre-existing public datasets (PlantDoc and AppleLeaf9), both cited as prior work rather than released by this paper's authors. The data availability statement only points to those external dataset repositories; no authors' code, trained models, or paper-curtd

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