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TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free
Tianjiao Chen1,2, Hongbo Chen1,2, Jianming Du1
1Intelligent Agriculture Engineering Laboratory of Anhui Province, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Pest Management Science
|May 29, 2024
Summary
A new rotated detection method effectively identifies Cnaphalocrocis medinalis (C. medinalis) damage in fields. This automated approach improves pest detection accuracy and efficiency for agricultural surveys.
Area of Science:
- Agricultural Entomology
- Computer Vision
- Machine Learning
Background:
- Cnaphalocrocis medinalis (C. medinalis) is a significant agricultural pest causing recurrent outbreaks.
- Automated pest and disease detection is crucial for efficient in-field surveys.
- Existing detection methods struggle with the variable orientations and aspect ratios of C. medinalis damage symptoms.
Purpose of the Study:
- To develop an automated, rotated two-stage detection method for in-field C. medinalis surveys.
- To address the limitations of generic detection methods in handling arbitrary orientations and aspect ratios of pest damage.
- To validate the proposed method using a custom in-field C. medinalis dataset.
Main Methods:
- Implementation of an anchor-free rotated region proposal network (AF-R2PN).
- Development of a rotated two-stage detection framework to handle arbitrary orientations.
- Construction of a dedicated in-field C. medinalis dataset for training and validation.
Main Results:
- The proposed method achieved 80% average precision (AP), outperforming horizontal detectors by 2.3%.
- Demonstrated superior localization capabilities compared to generic detection methods.
- Outperformed other state-of-the-art rotated detection algorithms, balancing speed and performance.
Conclusions:
- The developed method shows significant superiority in detecting C. medinalis damage under complex field conditions.
- Enhances the efficiency and coverage of in-field surveys, offering practical applicability.
- Provides crucial technical support for agricultural pest and disease monitoring.
