YOLO-PINE: A Lightweight Pineapple Object Detection Method Based on an Improved YOLO11n

Abstract

Pineapple detection in complex field environments faces significant challenges, including severe background interference, frequent occlusion by sword leaves, and difficulties in identifying dense fruits, which demand an optimal balance between detection accuracy and computational efficiency for practical deployment on resource-constrained edge devices. We propose YOLO-PINE, a lightweight detection model based on an improved YOLO11n architecture incorporating three key modules: the Channel Grouping semi-convolution module (CGHalfConv) for efficient shallow feature extraction, the Circular Attention mechanism (CA) for global context modeling in the frequency domain, and the Spatial Attention Multi-scale Convolution module (SAMC) for enhanced multi-scale feature fusion. The model was trained and evaluated on a self-constructed pineapple dataset. YOLO-PINE achieved a precision of 97.2%, a recall of 92.6%, mAP@50 of 95.9%, and mAP@50–95 of 70.1%, with only 5.0 GFLOPs and 2.1 M parameters, representing a 21.9% reduction in computational load and a 16.0% decrease in parameters compared to YOLO11n. YOLO-PINE achieves a competitive balance between detection accuracy and efficiency, offering a viable visual perception solution for automated pineapple harvesting on edge devices.