ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1650012
This article is part of the Research TopicInnovative Techniques for Precision Agriculture and Big DataView all 3 articles
Real-Time and Resource-Efficient Banana Bunch Detection and Localization with YOLO-BRFB on Edge Devices
Provisionally accepted
Shuo Wang1Lijiao Wei1Danran Zhang2Ling Chen2Weihua Huang1Dongjie Du1
Kangmin Lin2
Zhenhui Zheng1*
Jieli Duan2*- 1Institute of Agricultural Machinery, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, China
- 2South China Agricultural University, Guangzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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, 2 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.
Keywords: Machine Vision, Detection and localization, Banana Bunches, Lightweight model, Edge computing
Received: 19 Jun 2025; Accepted: 22 Jul 2025.
Copyright: © 2025 Wang, Wei, Zhang, Chen, Huang, Du, Lin, Zheng and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhenhui Zheng, Institute of Agricultural Machinery, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, China
Jieli Duan, South China Agricultural University, Guangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.