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Crop pest detection by three-scale convolutional neural network with attention.

Xuqi Wang1, Shanwen Zhang1, Xianfeng Wang1

  • 1College of Information Engineering, Xijing University, Xi'An, 710123, China.

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Summary

A new three-scale convolutional neural network with attention (TSCNNA) model improves crop pest detection accuracy. This AI approach enhances identification of small pests in complex field conditions for better agricultural security.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Crop pests significantly impact agricultural yield and quality, necessitating accurate detection for effective management.
  • Existing convolutional neural network (CNN) methods struggle with recognizing small, diverse pests in complex field environments.

Purpose of the Study:

  • To develop an advanced CNN model for improved crop pest detection, particularly for small and varied pest species.
  • To enhance the accuracy and efficiency of real-time pest identification in agricultural fields.

Main Methods:

  • A novel three-scale CNN with attention (TSCNNA) model was developed, incorporating channel attention and spatial mechanisms.
  • The model was designed to improve CNN's focus on pests of varying sizes against complicated backgrounds and enlarge its receptive field.

Main Results:

  • The TSCNNA model achieved a precision of 93.16% in crop pest detection.
  • This represents a significant improvement of 5.1% and 3.7% over existing ICNN and VGG16 methods, respectively.

Conclusions:

  • The TSCNNA model demonstrates high speed and accuracy for crop pest detection.
  • This method holds practical significance for real-time, in-field pest control and safeguarding crop production.