A field rice panicle detection model based on improved YOLOv11x

Yuzhu Luo¹Xinyu Li¹Bing Bai¹Xiaoyu Yu¹Yu Wang¹Zuobin Ma²Liying Zhang^2*Xiuyuan Peng^1*

• ¹Institute of Information, Liaoning Academy of Agricultural Sciences, Shenyang, China
• ²Liaoning Rice Research Institute, Shenyang, China

Rice serves as the staple food for over 50% of the world's population, making its yield prediction crucial for food security. The number of panicles per unit area is a core parameter for estimating rice yield. However, traditional manual counting methods suffer from low efficiency and significant subjective bias. To address challenges in unmanned aerial vehicle (UAV) images such as densely distributed panicles, large scale variations, and severe occlusion, this paper proposes a rice panicle detection model based on an improved You Only Look Once version 11x (YOLOv11x) architecture. The main improvements include: 1) Introducing a Bi-level Routing Attention (BRA) mechanism into the backbone network to improve the feature representation capability for small objects; 2) Adopting a Transformer-based detection head (TransHead) to capture long-term spatial dependencies; 3) Integrating a Selective Kernel (SK) Attention module to achieve dynamic multi-scale feature fusion; 4) Designing a multi-level feature fusion architecture to enhance multi-scale adaptability. Experimental results demonstrate that the improved model achieves an mAP@0.5 of 89.4% on our self-built dataset, representing a 3% improvement over the baseline YOLOv11x model. It also achieves a Precision of 87.3% and an F1-score of 84.1%, significantly outperforming mainstream algorithms such as YOLOv8 and Faster R-CNN. Panicle counting tests conducted on 300 rice panicle images show that the improved model achieves R 2 =0.85, RMSE=2.33, and rRMSE=0.13, indicating a good fitting effect. The proposed model provides a reliable solution for intelligent in-field rice panicle detection using UAV images and holds significant importance for precise rice yield estimation.