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Improved CNN Method for Crop Pest Identification Based on Transfer Learning.

Yiwen Liu^1,2,3, Xian Zhang^1,2,3, Yanxia Gao¹

¹School of Computer Science and Engineering, Huaihua University, Huaihua, Hunan 418000, China.

Computational Intelligence and Neuroscience

|March 28, 2022

Summary

This summary is machine-generated.

This study introduces an advanced crop pest identification method using a multilayer network model. The novel approach achieves 97.71% accuracy, significantly improving upon current crop disease and pest detection techniques.

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Effective crop disease and pest management is crucial for agricultural yield and quality.
Current identification methods lack efficiency and accuracy in research and analysis.
Advanced computational models are needed for precise pest detection.

Purpose of the Study:

To develop an efficient and accurate crop pest identification method.
To enhance the performance of pest recognition models through data enhancement and model fusion.
To address the limitations of existing crop pest identification techniques.

Main Methods:

Image data enhancement to create a reliable sample dataset.
Utilizing VGG16 and Inception-ResNet-v2 transfer learning networks for pest image recognition.
Implementing an integrated algorithm to fuse two improved Convolutional Neural Network (CNN) models.
Simulation analysis using the Integrated Dataset for Agricultural Disease and Pest (IDADP) dataset.

Main Results:

The proposed multilayer network model achieved a pest identification accuracy of 97.71%.
The integrated algorithm effectively fused CNN models, enhancing recognition and classification capabilities.
Significant improvement in identification accuracy compared to existing methods was demonstrated.

Conclusions:

The developed multilayer network model offers a highly accurate solution for crop pest identification.
The method provides a robust framework for improving agricultural pest management strategies.
This research contributes to advancing AI applications in precision agriculture.