Research on Blueberry Maturity Detection Based on Receptive Field Attention Convolution and Adaptive Spatial Feature Fusion

Detecting small objects in complex outdoor conditions remains challenging. This paper proposes an improved version of YOLOv8n for the detection of blueberry in challenging outdoor scenarios. In this context, this article addresses feature extraction, small-target detection, and multi-scale feature fusion. Specifically, the C2F-RFAConv module is introduced to enhance spatial receptive field learning and a P2-level detection layer is introduced for small and distant targets and fused by a four-head adaptive spatial feature fusion detection head (Detect-FASFF). Additionally, the Focaler-CIoU loss is chosen to mitigate sample imbalance, accelerate convergence, and improve overall model performance. Experiments on our blueberry maturity dataset show that the proposed model outperforms YOLOv8n, achieving 2.8% higher precision, 4% higher recall, and a 4.5% increase in mAP@0.5, with an FPS of 80. It achieves 89.1%, 91.0%, and 85.5% AP for ripe, semi-ripe, and unripe blueberries, demonstrating robustness under varying lighting, occlusion, and distance conditions. Compared to other lightweight networks, the model offers superior accuracy and efficiency. Future work will focus on model compression for real-world deployment.