YOLOv12n-SCA: A Hybrid Attention-Based Model for UAV-Based Assessment of Rice Transplanting Quality

Mechanized rice transplanting is a key method for large-scale cultivation. To enable rapid, accurate seedling detection and evaluate rice transplanting quality, this paper proposes an improved YOLOv12n model, YOLOv12n-SCA. First, this study embeds SCA hybrid attention modules into the YOLOv12n backbone and neck networks, integrating the advantages of SCSA (Spatial and Channel Synergistic Attention) and CA (Coordinate Attention) modules to enhance the model’s ability to extract and locate seedling features against complex paddy field backgrounds. Second, clustering analysis and a plant-spacing discrimination rule, based on seedling detection coordinates, automatically identify areas with missed transplanted seedlings. Experimental results show that the YOLOv12n-SCA model achieved precision, recall, [email protected], and [email protected]:0.95 of 93.6%, 90.3%, 95.3%, and 79.8%, respectively, with overall performance superior to that of the original YOLOv12n and single-attention-based improved models. In experiments on an independent dataset, the detection algorithm for rice seedlings in missed transplants achieved 82.34% accuracy and a counting error of 2.06%, indicating that it can effectively identify rice seedling locations and provide quality assessment results. These results show that YOLOv12n-SCA can support intelligent rice seedling detection, rice transplant quality evaluation, and smart field management in complex rice paddy environments.