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An Improved Multi-Scale Feature Extraction Network for Rice Disease and Pest Recognition.

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  • 1Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou 450001, China.

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|November 26, 2024
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Summary

Accurately identifying rice pests and diseases is crucial for crop yield. A new Lightweight Multi-scale Feature Extraction Network (LMN) improves recognition accuracy and reduces model size for effective pest and disease classification.

Keywords:
data augmentationdeep learningmulti-scale feature extractionpest image classificationrice diseases and pests

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Rice production faces significant yield losses due to pests and diseases.
  • Accurate identification of rice pests and diseases is essential for effective management strategies.
  • Current image recognition technologies for rice pest and disease identification are limited by data scarcity and low accuracy.

Purpose of the Study:

  • To address the limitations in current rice pest and disease identification methods.
  • To develop a more accurate and efficient image recognition model for rice pests and diseases.
  • To construct and expand a comprehensive rice pest and disease dataset (RPDD).

Main Methods:

  • Construction of the Rice Pest and Disease Dataset (RPDD) with data augmentation.
  • Proposal of a Lightweight Multi-scale Feature Extraction Network (LMN) based on ResNet and attention mechanisms.
  • Extraction of multi-scale features at a finer granularity for improved classification.

Main Results:

  • The proposed LMN model achieved an average classification accuracy of 95.38% on the RPDD.
  • The LMN model obtained an F1-Score of 94.5%, demonstrating high classification performance.
  • The LMN model has a small parameter size (1.4 M) and low computational cost (1.65 G FLOPs).

Conclusions:

  • The LMN model significantly outperforms the baseline ResNet model in rice pest and disease classification.
  • The LMN model offers a lightweight and accurate solution for agricultural image recognition tasks.
  • This research contributes to advancing automated pest and disease identification in rice production.