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A Lightweight YOLO Model for Rice Panicle Detection in Fields Based on UAV Aerial Images

文献类型： 外文期刊

作者： Song, Zixuan 1 ; Ban, Songtao 2 ; Hu, Dong 2 ; Xu, Mengyuan 2 ; Yuan, Tao 2 ; Zheng, Xiuguo 2 ; Sun, Huifeng 4 ; Zhou, Sheng 4 ; Tian, Minglu 2 ; Li, Linyi 2 ;

作者机构： 1.Shanghai Ocean Univ, Coll Informat Technol, Shanghai 201306, Peoples R China

2.Shanghai Acad Agr Sci, Inst Agr Sci & Technol Informat, Shanghai 201403, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Intelligent Agr Technol Yangtze River Delt, Shanghai 201403, Peoples R China

4.Shanghai Acad Agr Sci, Ecoenvironm Protect Res Inst, Shanghai 201403, Peoples R China

5.Shanghai Engn Res Ctr Low Carbon Agr SERCLA, Shanghai 201415, Peoples R China

6.Minist Agr & Rural Affairs, Key Lab Low Carbon Green Agr Southeastern China, Shanghai 201403, Peoples R China

关键词： rice panicle detection; YOLOv8; FasterNet; lightweight network

期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 9 卷 1 期

页码：

收录情况： SCI

摘要： Accurate counting of the number of rice panicles per unit area is essential for rice yield estimation. However, intensive planting, complex growth environments, and the overlapping of rice panicles and leaves in paddy fields pose significant challenges for precise panicle detection. In this study, we propose YOLO-Rice, a rice panicle detection model based on the You Only Look Once version 8 nano (YOLOv8n). The model employs FasterNet, a lightweight backbone network, and incorporates a two-layer detection head to improve rice panicle detection performance while reducing the overall model size. Additionally, we integrate a Normalization-based Attention Module (NAM) and introduce a Minimum Point Distance-based IoU (MPDIoU) loss function to further improve the detection capability. The results demonstrate that the YOLO-Rice model achieved an object detection accuracy of 93.5% and a mean Average Precision (mAP) of 95.9%, with model parameters reduced to 32.6% of the original YOLOv8n model. When deployed on a Raspberry Pi 5, YOLO-Rice achieved 2.233 frames per second (FPS) on full-sized images, reducing the average detection time per image by 81.7% compared to YOLOv8n. By decreasing the input image size, the FPS increased to 11.36. Overall, the YOLO-Rice model demonstrates enhanced robustness and real-time detection capabilities, achieving higher accuracy and making it well-suited for deployment on low-cost portable devices. This model offers effective support for rice yield estimation, as well as for cultivation and breeding applications.

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