RESEARCH ON A DETECTION ALGORITHM FOR DRY-DIRECT SEEDED RICE BASED ON YOLOv11N-DF

Identifying dry-direct seeded rice seedlings provides valuable information for field management. To address the challenges of seedling detection in cold-region dry-direct seeded rice fields, this study proposes an enhanced YOLOv11n-DF model. Key innovations include: 1) integrating DSConv into the C3k2 module to optimize phenotypic feature extraction, and 2) employing the FASFF strategy to improve scale invariance in the convolutional head. Experimental results show that the improved model achieves an mAP50 of 96%, with high recall, precision, and a processing speed of 251.5 FPS, outperforming the original YOLOv11n by 5 percentage points in mAP50, and surpassing YOLOv7–YOLOv10 in detection accuracy. The proposed algorithm effectively addresses challenges such as seedling occlusion and non-uniform distribution, offering a robust solution for automated seedling monitoring in precision agriculture.