Fast-YOLO11n: A Lightweight and Efficient Apple Detection Model for Complex Orchard Environments

Abstract

Accurate and real-time apple detection in complex orchard environments is essential for robotic harvesting but remains challenging because of illumination variation, foliage occlusion, and limited computational resources. This study proposes Fast-YOLO11n, a lightweight detector derived from the nano variant of You Only Look Once 11 (YOLO11n) and integrating three complementary components. A Fast-C3k2 module based on partial convolution (PConv) reduces redundant computation while preserving cross-layer feature transmission. A focal modulation (FM) mechanism enhances target-related responses and suppresses background interference under occlusion and uneven illumination. In addition, a parallel downsampling module, termed ADown, retains local geometric details and multi-scale semantic information during downsampling. Experiments were conducted on a field-collected orchard dataset comprising 2240 images and 22,673 annotated apple instances under diverse lighting, scale, and occlusion conditions. Fast-YOLO11n achieved mean average precision values of 75.76% across intersection-over-union (IoU) thresholds of 0.50–0.95 (mAP@50–95) and 91.29% at an IoU threshold of 0.50 (mAP@50), while operating at 366.19 frames per second (FPS) with 2.51 million parameters and 6.00 billion floating-point operations (FLOPs). Compared with the YOLO11n baseline, it improved mAP@50–95 and mAP@50 by 2.39 and 1.39 percentage points, respectively, while reducing the parameter count and FLOPs by 2.71% and 5.36%. Ablation experiments demonstrated the individual and combined effects of the three modules on detection performance and computational efficiency. The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms.