Lightweight visual detection approach for apple ripeness perception in intelligent orchard harvesting scenarios

Apple ripeness detection in complex orchard scenarios is plagued by fruit occlusion, overlapping, intense illumination interference, and subtle maturity feature differences among similar fruits. Conventional lightweight detection models also suffer from low accuracy and severe feature loss in field environments. To address these problems, this paper proposes a real-time lightweight method for apple ripeness detection with optimized multi-dimensional network structures, which balances lightweight deployment and feature perception capability for complex orchard backgrounds. Specifically, depthwise convolutions replace standard convolutions at the backbone tail to reduce redundant computation. A parameter-free SimAM attention mechanism is integrated into the C2PSA module to enhance valid feature extraction of apples under complex backgrounds. Additionally, adaptive downsampling (ADown) is adopted in the neck network to replace traditional downsampling structures, preserving fruit texture and color details and mitigating feature degradation. A multi-category orchard dataset containing red, green, and red-green striped apples is constructed, covering variable illumination, fruit occlusion, and dense stacking conditions. Ablation, comparative, and generalization experiments are conducted to verify model performance. The results show that compared with YOLOv11n, the proposed method achieves 8.6%, 9.5%, 8.9%, 10.9%, and 24.4% improvements in precision, recall, F1-score, mAP50, and mAP50-95, respectively. Its parameters and computational complexity are reduced by 12.8% (2.25 M) and 25.4% (4.7 GFLOPs), with an inference speed of 109 FPS. As a zero-cost feature optimization module, SimAM effectively eliminates classification confusion caused by apples with similar color and appearance. The optimized model presents superior illumination robustness, generalization ability, and convergence performance. It achieves a favorable trade-off between detection accuracy, model size, and inference speed, enabling real-time edge deployment in field orchards. This study provides technical support for intelligent ripeness detection and automatic grading of orchard fruits.