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SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch

Keyuan Qiu1, Yingjie Zhang1, Zekai Ren1

  • 1College of Information Science and Technology, Shihezi University, Shihezi 832003, China.

Insects
|September 28, 2024
PubMed
Summary

We developed SpemNet, a new method for cotton pest and disease recognition. This efficient model improves upon traditional methods, offering superior performance in identifying cotton pests and diseases.

Keywords:
attention mechanismcotton pest recognitiondeep learningefficient multi-scale attentionfeature fusionimage classificationtransformer

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Traditional Vision Transformer models struggle with local feature learning and multi-scale feature integration in pest recognition.
  • Accurate identification of cotton pests and diseases is crucial for crop management and yield optimization.

Purpose of the Study:

  • To propose SpemNet, an efficient cotton pest and disease recognition method.
  • To enhance the performance and efficiency of pest and disease identification models.

Main Methods:

  • Developed SpemNet incorporating efficient multi-scale attention and stacking patch embedding.
  • Introduced the SPE (Self-supervised Patch Embedding) and EMA (Efficient Multi-scale Attention) modules.
  • Validated SpemNet on the CottonInsect dataset.

Main Results:

  • SpemNet effectively addresses local feature learning difficulties and improves multi-scale feature integration.
  • The model demonstrates significant improvements in performance and efficiency for cotton pest recognition.
  • SpemNet achieved high precision and F1 scores, indicating superiority in the task.

Conclusions:

  • SpemNet offers an efficient and reliable solution for cotton pest and disease identification.
  • The model shows significant theoretical and applied potential in agricultural applications.
  • This research contributes to advancing automated pest and disease detection in cotton farming.