ALD-YOLO: a lightweight attention detection model for apple leaf diseases.

Abstract

As an important economic crop, apples are significantly affected by disease infestations, which can lead to substantial reductions in apple yield and economic losses. To rapidly and accurately detect apple leaf diseases, we propose a lightweight attention detection model ALD-YOLO based on the YOLOv8 architecture. To improve overall efficiency, we design the Faster_C2F module within the Backbone and Neck by optimizing YOLOv8's primary C2F (Faster Implementation of CSP Bottleneck with 2 convolutions) modules with the more computationally effective FasterNet Block. To strengthen the model's ability to capture multi-scale feature information and focus on smaller disease targets, the EMA (Efficient Multi-Scale Attention) module is introduced at the input end where the Neck connects to the detection module of the Head, forming a new Faster_C2F_EMA module. Two novel C2F modules can achieve the optimal balance of detection accuracy and efficiency. Furthermore, to reduce the model's parameters and retain more image information, most convolution modules in the YOLOv8 architecture are replaced by a lightweight downsampling module ADown. In comparison with YOLOv8n and YOLOv8s, experimental results on the AppleLeaf9 dataset showed that ALD-YOLO increased mAP by 1.4% and 0.6%, and reduced GFLOPs by 29.63% and 79.93%, respectively. The CPU inference testing showed that the improvement of our model in frames per second reached up to 119.23% compared to YOLOv8s. Therefore, our model delivers more stable and efficient detection of apple leaf diseases, even on edge devices.

Plant phenotyping relevance

リンゴ葉の病徴を画像から検出・分類する軽量モデルを開発し、精度・計算量・推論速度を比較評価しているため、植物病害状態の画像ベース表現型抽出が中心です。

Code and data availability

The paper uses the public AppleLeaf9 dataset, but it is cited prior work (Yang et al., 2022), not a paper-specific asset. No author analysis code, trained model checkpoints, or paper-specific data deposit is mentioned; the data availability statement only points to the pre-existing AppleLeaf9 repository.

No evidence-backed public reproduction asset is currently recorded.