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A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture
Rong Zhao1,2, Fei Deng3, Haohua Que4
1School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces HGS-YOLO, a lightweight tomato leaf disease detection system designed for efficient deployment on low-power agricultural devices. It achieves high accuracy with significantly reduced model complexity, enabling practical field applications.
Area of Science:
- Agricultural Science
- Computer Vision
- Deep Learning
Background:
- Tomato leaf diseases significantly impact crop yield and quality.
- Existing deep learning models for disease detection are often too resource-intensive for practical agricultural deployment.
- There is a need for efficient, deployable solutions for real-time crop monitoring.
Purpose of the Study:
- To develop a lightweight, system-oriented adaptation of YOLOv11 for efficient tomato leaf disease detection.
- To create an end-to-end edge sensing pipeline for low-power agricultural applications.
- To optimize model structure, training, and deployment for resource-constrained environments.
Main Methods:
- Adapted YOLOv11 with an HGNetV2 backbone, an HS-FPN neck with channel attention, and an MPDIoU loss function.
- Developed a complete engineering pipeline including training, optimization, post-training quantization, and hardware deployment on a handheld platform with BPU acceleration.
- Evaluated performance using metrics such as mAP50, mAP@[0.5:0.95], recall, precision, model size, parameter count, latency, FPS, and power consumption.
Main Results:
- HGS-YOLO achieved 93.6% mAP50 and 72.1% mAP@[0.5:0.95] with only 1.3 M parameters and a 3.1 MB model size.
- The model demonstrated superior speed, memory efficiency, and lower power consumption on the RDK X5 platform compared to baselines.
- Field tests in a greenhouse showed successful on-device disease detection under various challenging conditions, with 90.3% mAP50 and 82.3% recall.
Conclusions:
- HGS-YOLO offers a practical solution for tomato leaf disease detection, balancing accuracy, efficiency, and deployability.
- The system-level co-design approach enables effective deployment on resource-constrained edge devices in agriculture.
- Further validation across diverse field conditions is recommended for broader adoption.
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