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Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices

文献类型： 外文期刊

作者： Wang, Shuo 1 ; Wei, Lijiao 1 ; Zhang, Danran 1 ; Chen, Ling 1 ; Huang, Weihua 1 ; Du, Dongjie 1 ; Lin, Kangmin 4 ; Zheng, Zhenhui 1 ; Duan, Jieli 4 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Agr Machinery Res Inst, Zhanjiang, Guangdong, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Equipment Trop Crops, Zhanjiang, Guangdong, Peoples R China

3.Chinese Acad Trop Agr Sci, Inst Agr Machinery, Zhanjiang, Guangdong, Peoples R China

4.South China Agr Univ, Coll Engn, Guangzhou, Guangdong, Peoples R China

5.South China Agr Univ, Coll Math Informat, Guangdong, Peoples R China

关键词： machine vision; detection and localization; banana bunches; lightweight model; edge computing

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Reliable detection and spatial localization of banana bunches are essential prerequisites for the development of autonomous harvesting technologies. Current methods face challenges in achieving high detection accuracy and efficient deployment due to their structural complexity and significant computational demands. This study proposes YOLO-BRFB, a lightweight and precise system designed for detection and 3D localization of bananas in orchard environments. First, the YOLOv8 framework is improved by integrating the BasicRFB module, enhancing feature extraction for small targets and cluttered backgrounds while reducing model complexity. Then, a binocular vision system is used for localization, estimating 3D spatial coordinates with high accuracy and ensuring robust performance under diverse lighting and occlusion conditions. Finally, the system is optimized for edge-device deployment, achieving real-time processing with minimal computational resources. Experimental results demonstrate that YOLO-BRFB achieves a precision of 0.957, recall of 0.922, mAP of 0.961, and F1-score of 0.939, surpassing YOLOv8 in both recall and mAP. The average positioning error of the system along the X-axis is 12.33 mm, the average positioning error along the Y-axis is 11.11 mm, and the average positioning error along the Z-axis is 16.33 mm. The system has an inference time of 8.6 milliseconds on an Nvidia Orin NX with a GPU memory requirement of 1.7 GB. This study is among the first to focus on a lightweight approach optimized for deployment on edge computing devices. These results highlight the practical applicability of YOLO-BRFB in real-world agricultural scenarios, providing a cost-effective solution for precision harvesting.

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