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WMC-RTDETR: a lightweight tea disease detection model.

Youcheng Zhang¹, Jun Song¹, Xinjian Yu²

¹College of Information Science and Technology, Nanjing Forestry University, Nanjing, China.

Frontiers in Plant Science

|May 21, 2025

Summary

This study introduces WMC-RTDETR, an improved lightweight model for detecting tea pests and diseases. It enhances accuracy and efficiency in complex environments, offering a practical solution for agricultural monitoring.

Keywords:

RT-DETR contextual feature reconstruction embedded deployment multiscale multihead self-attention tea pest and disease detection wavelet transform

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Area of Science:

Agricultural technology
Computer vision
Machine learning

Background:

Accurate detection of tea pests and diseases is vital for plantation management.
Challenges like occlusion and complex backgrounds hinder traditional detection methods.

Purpose of the Study:

To develop an improved lightweight model (WMC-RTDETR) for efficient and accurate tea pest and disease detection.
To enhance feature extraction and target detection in complex agricultural scenes.

Main Methods:

Introduced wavelet transform convolution for multi-scale feature capture.
Integrated multi-scale multi-head self-attention for global feature fusion.
Designed a context-guided spatial feature reconstruction pyramid network.

Main Results:

Achieved 97.7% mAP50 and 83.1% mAP50:95, outperforming the original model.
Reduced model parameters by 35.48% and FLOPs by 40.42%.
Successfully deployed on Raspberry Pi for real-time performance.

Conclusions:

WMC-RTDETR offers a highly efficient and accurate solution for pest and disease detection in complex tea plantations.
The model's lightweight nature and real-time performance make it suitable for low-cost, embedded agricultural monitoring systems.