MG-YOLO: A Lightweight Deep Learning Model for Detecting Blueberry Fruits at Different Maturities

The yield and quality of blueberry cultivation depend heavily on effective fruit harvesting. Accurate and real-time identification of blueberries at various maturity stages is a prerequisite for precise harvesting. Accordingly, a lightweight network named MG-YOLO is developed for blueberry fruit detection in this study, through multiple enhancements on YOLO11-nano. First, the GBC block integrating gated convolutions and Bottleneck convolutions is adopted to improve adaptive feature discrimination ability, especially for densely occluded blueberries. Second, a novel MG-C3k2 module incorporating multi-scale Ghost convolution is proposed to strengthen feature representation while maintaining model efficiency. Finally, IF-CIoU loss, focusing on inner overlapping areas of bounding boxes and the balance between hard and easy samples, is designed to mitigate the class imbalance issue and accelerate bounding box regression, improving localisation precision. Experimental results demonstrate that the proposed MG-YOLO attains 85.7% mAP@0.5 and 60.6% mAP@0.5:0.95, representing remarkable improvements of 4.0% and 5.1% over the baseline, respectively. Furthermore, the parameter count is reduced by 12%, and the model exhibits a detection speed of 51 FPS measured on an NVIDIA RTX 3060 GPU, indicating its potential for for real-time automated harvesting applications.