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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
PubMed
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.

Keywords:
anchor‐freein‐field surveypest damage detectionrotated detectorrotated region proposal network

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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.