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TSSC: a new deep learning model for accurate pea leaf disease identification.

Laixiang Xu1, Yibu Chang1, Chenyang Li1

  • 1School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, China.

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Summary

This study introduces a deep learning model for accurate pea leaf disease recognition, achieving 99.61% accuracy. The intelligent system aids in early detection, improving crop yield and food safety.

Keywords:
convolutional neural networkdeep learningpea leafplant pathologysplit attention

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate plant disease diagnosis is vital for crop yield and food safety.
  • Automatic recognition of pea leaf diseases presents a significant challenge.

Purpose of the Study:

  • To develop a deep learning-based intelligent recognition method for various pea leaf diseases.
  • To address the automatic recognition problem of plant leaf diseases.

Main Methods:

  • A novel deep learning framework, TSSC, was proposed.
  • Incorporated a three-neighbor channel attention mechanism for enhanced feature extraction.
  • Utilized a complementary squeeze and excitation mechanism and a split attention module to improve key feature extraction and reduce model complexity.

Main Results:

  • The TSSC model achieved an outstanding overall classification accuracy of 99.61%.
  • The proposed model demonstrated superior performance compared to other leading deep learning models.

Conclusions:

  • The developed system offers an effective solution for the image recognition of complex plant diseases.
  • The findings have valuable implications for the development of mobile disease detection equipment.