GAE-YOLO: a lightweight multimodal detection framework for tomato smart agriculture with edge computing
Xiaoke Liu1,2, Wenjie Teng1, Haoran Yu3
1School of Basic Medical Sciences, Shandong Second Medical University, Weifang, Shandong, China.
Frontiers in Plant Science
|December 5, 2025
Summary
A new intelligent tomato management system uses the GAE-YOLO algorithm for efficient crop monitoring. This smart agriculture solution enhances real-time capability and multi-task coordination in tomato cultivation.
Area of Science:
- Computer Vision
- Smart Agriculture
- Edge Computing
Background:
- Computer vision is increasingly used in smart agriculture for crop monitoring.
- Existing methods face challenges in computational complexity, real-time processing, and multi-task coordination for tomatoes.
Purpose of the Study:
- To develop an intelligent tomato management system addressing limitations of current computer vision approaches.
- To improve real-time capability, computational efficiency, and multi-task coordination in tomato cultivation.
Main Methods:
- Proposed a Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm.
- Utilized Ghost Convolution (GhostConv), AReLU activation, and Effective Intersection over Union (E-IoU) loss.
- Implemented on a Jetson TX2 platform with ZED stereo vision and a PyQt6 visualization interface.
Main Results:
- Achieved 93.5% mean Average Precision (mAP@50) at 10.2 FPS on Jetson TX2.
- Optimized to 27 FPS using TensorRT acceleration and 720p resolution.
- Established systems for tomato maturity and yield prediction, disease diagnosis, and LLM consultation.
Conclusions:
- The GAE-YOLO system offers a new paradigm for edge computing in agriculture.
- Provides critical technical support for the advancement of smart farming.
- Demonstrates enhanced performance in real-time tomato management and analysis.
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