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SSM-based detection of rice seedling deficiency
Youran Xia1, Zhengtao Zhu2, Xiaobin Liu3
1School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
Scientific Reports
|July 2, 2025
Summary
This study introduces an automated method for detecting missing rice seedlings using a state space model. The approach significantly improves detection accuracy, offering a practical solution for large-scale rice cultivation.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Machine Learning
Background:
- Rice seedling deficiency is a major challenge in large-scale cultivation, impacting replanting schedules.
- Manual inspection methods are labor-intensive and inefficient for timely detection.
Purpose of the Study:
- To develop an automated and accurate method for detecting rice seedling deficiency.
- To improve detection precision for small seedlings in rice fields.
Main Methods:
- A novel state space model (Mamba) with a dual-branch feature extraction module was employed.
- Wavelet convolution transform was integrated to enhance feature detection.
- The method was evaluated on a self-constructed rice seedling deficiency dataset.
Main Results:
- The proposed optimized model achieved a mean Average Precision at 50% (mAP50) of 78%.
- The approach demonstrated superior performance compared to existing baseline models.
Conclusions:
- The developed method offers an effective and practical solution for automated rice seedling deficiency detection.
- This innovation addresses the need for efficient monitoring in large-scale rice farming.
