GAS-YOLO: a robust soybean seedling detection model trained on single-scene UAV data for complex field environments

Accurate detection of soybean seedlings using unmanned aerial vehicles (UAVs) in complex field environments is crucial for yield estimation and agricultural planning. However, UAV images present challenges such as small, densely clustered, and partially occluded seedlings. Combined with complex field conditions marked by intricate backgrounds and resolution variations, and limited single-scene training data, these factors collectively cause significant detection model performance degradation. To overcome these limitations, we develop GAS-YOLO, an enhanced YOLOv8-based framework for accurate soybean seedling detection in complex field environments. Firstly, we integrated a Global Attention Mechanism (GAM) into the model’s neck to prioritize contextual features and suppress background noise. Secondly, the SIoU loss was employed with an angle term to mitigate bounding box drift and improve localization accuracy. Furthermore, targeted data augmentation strategies, such as defocus blur simulation and soil color variation, were applied to single-scene data to simulate diverse complex field scenarios and enhance model generalization. The experimental results indicate that GAS-YOLO shows significantly better performance than the baseline YOLOv8 model. Crucially, it shows notable improvements in high-density regions, with estimation accuracy increasing by 30.46% for densities of 80–100 seedlings and by 11.91% for densities exceeding 100 seedlings. GAS-YOLO also exhibits better performance in challenging field environments. On test sets featuring defocused images and yellow soil backgrounds, soybean seedling detection accuracy increases by 47.14%, which shows GAS-YOLO’s robustness in real-world agricultural scenarios. This study establishes GAS-YOLO as a robust and reliable solution for soybean seedling detection in complex field environments, offering advantages for practical agricultural applications.