AID-YOLO: A Lightweight Wheat Aphid Detection Model Across Indoor and Field Scenes

Wheat aphids are the primary pests in wheat-producing areas, posing a serious threat to stable, high wheat yields and regional food security. To detect and count wheat aphids under different complex conditions, this study designed an improved model and developed a mini program. Firstly, we constructed a dual-source dataset containing 542 images collected from indoor and field environments, including 117 indoor images and 425 field images. Secondly, we proposed Aphid Identification and Detection YOLO (AID-YOLO), an enhanced YOLO11n-based method for close-range wheat aphid detection and image-level counting. Specifically, the original downsampling structure was replaced with the ADown module to improve feature extraction efficiency while reducing redundant computation, an IEMA multi-scale attention mechanism integrating IRMB and EMA was introduced to strengthen feature learning under complex background interference, and the dynamic upsampling operator DySample was adopted to enhance cross-scale feature fusion. Finally, AID-YOLO achieved a 19.0% reduction in parameter count (2.09 M vs. 2.58 M) and a 19.0% decrease in computational cost (5.1 vs. 6.3 GFLOPs). Across three random seeds, AID-YOLO achieved an average mAP50 of 95.3 ± 0.10%, compared with 93.0 ± 0.39% for the YOLO11n baseline on the combined indoor–field evaluation set. These results suggest that AID-YOLO achieves a favorable balance between detection accuracy and model lightweighting under the tested indoor and field conditions, providing a useful technical reference for intelligent wheat aphid monitoring.