ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
GAE-YOLO: A Lightweight Multimodal Detection Framework for Tomato Smart Agriculture with Edge Computing
Provisionally accepted
- Shandong Second Medical University, Weifang, China
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The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm, which achieves end-to-end optimization through integrated technological solutions. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. When implemented on embedded computing platforms (Jetson TX2), the system demonstrates a configurable balance between accuracy and speed, achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, a three-dimensional localization framework is constructed through ZED stereo vision integration, enabling precise robotic navigation. Standardized assessment systems for tomato maturity and yield prediction are established to support scientific crop management. Additionally, a PyQt6-based visualization platform is developed, incorporating modules for maturity analysis, disease diagnosis, yield estimation, and agricultural large language model consultation, facilitating data-driven pest control strategies. This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Keywords: Tomato Smart Agriculture, Lightweight YOLO, Edge computing, Multimodal detection, plant phenotyping
Received: 24 Sep 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Liu, Teng, Yu, Yao, Wang, Peng, Han and Liu. 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:
Xiaoqing Han, hanxiaoqing@sdsmu.edu.cn
Jianming Liu, liujianming@sdsmu.edu.cn
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