Recognition of rice seedling counts in UAV remote sensing images via the YOLO algorithm

• Published in: Smart agricultural technology Vol. 12; p. 101107
• Main Authors: Chen, Shengxi, Li, Wenli, Chen, Du, Xie, Zhao, Zhang, Song, Cen, Fulang, Huang, Xiaoyun, Tu, Lei, Gao, Zhenran
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2025
• Subjects: Deep learning; Remote sensing; Rice; Seedling recognition; UAV; YOLO; YOLO; Deep learning; UAV; Seedling recognition; Remote sensing; Rice
• ISSN: 2772-3755; 2772-3755
• DOI: 10.1016/j.atech.2025.101107

Accurate identification of rice seedling numbers is essential for breeding, replanting, and yield prediction. Traditional manual counting methods are inefficient and prone to error. The integration of high-resolution drone imagery with the feature extraction capabilities of deep learning offers a novel approach for identifying rice seedlings using advanced computational techniques. This study employed drone-captured images of rice seedlings taken at heights of 12 m and 15 m from two locations—Anshun City and Qianxinan Prefecture in Guizhou Province—to construct datasets containing 100, 150, and 200 images, and compared the performance of YOLOv8n, YOLOv9t, and YOLOv10n in recognizing rice seedling numbers. The results show that at a flight height of 12 m and using a dataset of 200 images, model performance was optimal, achieving mAP@50 values of 0.964, 0.936, and 0.944 for YOLOv8n, YOLOv9t, and YOLOv10n, respectively. Among these, YOLOv8n demonstrated the highest prediction accuracy for rice seedlings, with an R2 value of 0.889, RMSE of 3.225, and rRMSE of 0.032. This research demonstrates that the combination of drone imagery and deep learning models enables effective large-scale counting of rice seedlings, presenting an innovative approach to rice phenotypic analysis.