SRC-YOLOv8n: a lightweight framework for fine-grained apple leaf disease detection with spatial detail preservation and multi-scale feature enhancement

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Abstract
Apple leaf disease detection is crucial for maintaining crop health and ensuring food security, yet current detection methods face significant challenges in balancing accuracy with computational efficiency. Existing lightweight detection models struggle with spatial detail preservation and multi-scale feature representation when processing complex disease symptoms with subtle visual characteristics. This study presents SRC-YOLOv8n, a lightweight framework that integrates spatial detail preservation and multi-scale feature enhancement for fine-grained apple leaf disease detection. The framework incorporates four key innovations: the Spatial Detail Attention C2f (SDA-C2f) module that preserves critical spatial information through Space-to-Depth Convolution and SpatialGroupEnhance mechanisms, the Reparameterized Generalized Feature Pyramid Network (RepGFPN) that optimizes multi-scale feature fusion through training-inference decoupling, the Cross-Level Local Attention Head (CLLAHead) that enables effective cross-scale feature interaction, and the Inner-IoU loss function that improves bounding box regression accuracy. Comprehensive evaluation on the Plant-Pathology-2021-FGVC8 and AppleLeaf9 datasets demonstrates that SRC-YOLOv8n achieves superior performance with 94.1% precision, 92.3% recall, 96.1% mAP50, and 93.2% F1 score while reducing parameters by 16.6%, computational cost by 19.8%, and model size by 17.7% compared to baseline YOLOv8n. The framework provides an effective solution for real-world agricultural monitoring applications requiring both high accuracy and computational efficiency.

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