An enhanced lightweight model for apple leaf disease detection in complex orchard environments.

Abstract

Automated detection of apple leaf diseases is crucial for predicting and preventing losses and for enhancing apple yields. However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. To address the challenges of detecting small-target diseases on apple leaves in complex backgrounds and difficulty in mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high consumption of computational resources in real-time deployment of existing models, we integrate the Fasternet Block into the C2f of the backbone network and neck network, effectively reducing the parameter count and the computational load of the model. In order to enhance the network's anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. Finally, we employ the NWD loss function to replace the CIoU loss function, allowing the model to locate and identify small targets more accurately and further enhance its robustness, thereby facilitating rapid and precise identification of apple leaf diseases. Experimental results demonstrate ELM-YOLOv8n's effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50 value-a significant improvement over YOLOv8n. Furthermore, the parameter count and computational load are reduced by 44.8% and 39.5%, respectively. The ELM-YOLOv8n model is better suited for deployment on mobile devices while maintaining high accuracy.

Plant phenotyping relevance

リンゴ葉の病徴を画像から検出する軽量モデルを開発し、精度・計算量・モバイル展開性を評価しているため、植物病害状態の表現型取得手法が中心である。

Code and data availability

The paper builds ELM-YOLOv8n on images drawn from two pre-existing public datasets (plant-pathology-2021-fgvc8 and AppleLeaf9) and its own augmented Apple-Leaf1 dataset, but no block contains an authors' public deposit URL, code release, trained model checkpoint, or data availability statement with an actionable link.

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