An enhanced deep learning-based framework for diagnosing apple leaf diseases.

Abstract

Timely and correct identification of diseases in the apple leaf is also important in protecting crop production and sustaining agriculture. This paper introduces E-YOLOv8, a lightweight improved version of YOLOv8, that can be implemented in real-time and with a limited resource base. The model has three key contributions: (1) GhostConv and C3 fusion to reduce redundant feature extraction and computational cost, (2) CBAM attention and a specifically designed FPN to maximize multi-scale feature fusion and small-lesion detections, and (3) large-scale evaluation on datasets of apple leaf disease, as well as ablation experiments and operational testing on edge devices to verify the accuracy and viability of this model. In experiments, E-YOLOv8 reaches 93.9mAP0.5 using 5.3 GFLOPs and 1.8 M parameters, a 33.9x factor smaller than that of YOLOv8l. These results indicate that E-YOLOv8 has achieved better performance than recent state-of-the-art detectors and is still applicable to practical real-world agricultural tasks.

Code and data availability

The paper uses the public AppleLeaf9 dataset (fused with PlantVillage and other external datasets) and proposes E-YOLOv8, but the supplied blocks contain no authors' public code, trained model checkpoints, or paper-specific data deposit. The datasets mentioned are cited third-party resources, not assets released with,

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