YOLOv11-Litchi: Efficient Litchi Fruit Detection based on UAV-Captured Agricultural Imagery in Complex Orchard Environments
It addresses the inefficiency of manual selection for high-value litchi fruit production, offering a real-time solution for precision agriculture, though it is incremental as it builds upon the YOLOv11 framework.
This paper tackles the problem of detecting litchi fruits in UAV-captured agricultural imagery in complex orchard environments by introducing YOLOv11-Litchi, a lightweight detection model that achieves a 32.5% reduction in parameter size to 6.35 MB while improving mAP by 2.5% to 90.1% and F1-Score by 1.4% to 85.5%.
Litchi is a high-value fruit, yet traditional manual selection methods are increasingly inadequate for modern production demands. Integrating UAV-based aerial imagery with deep learning offers a promising solution to enhance efficiency and reduce costs. This paper introduces YOLOv11-Litchi, a lightweight and robust detection model specifically designed for UAV-based litchi detection. Built upon the YOLOv11 framework, the proposed model addresses key challenges such as small target size, large model parameters hindering deployment, and frequent target occlusion. To tackle these issues, three major innovations are incorporated: a multi-scale residual module to improve contextual feature extraction across scales, a lightweight feature fusion method to reduce model size and computational costs while maintaining high accuracy, and a litchi occlusion detection head to mitigate occlusion effects by emphasizing target regions and suppressing background interference. Experimental results validate the model's effectiveness. YOLOv11-Litchi achieves a parameter size of 6.35 MB - 32.5% smaller than the YOLOv11 baseline - while improving mAP by 2.5% to 90.1% and F1-Score by 1.4% to 85.5%. Additionally, the model achieves a frame rate of 57.2 FPS, meeting real-time detection requirements. These findings demonstrate the suitability of YOLOv11-Litchi for UAV-based litchi detection in complex orchard environments, showcasing its potential for broader applications in precision agriculture.