Agricultural Pest Image Recognition Algorithm Based on Convolutional Neural Network and Bayesian Method

Abstract

Aiming at the limitations of existing agricultural pest image recognition technology, a novel agricultural pest recognition algorithm based on convolutional neural network and Bayesian method is proposed. During the process, convolutional neural networks are chosen as the basic model for image recognition algorithms, and Bayesian methods are used to optimize the neural network structure. At the same time, approximate variational methods are applied to construct corresponding approximate functions. Finally, the Bayesian neural network sampling optimization is completed through the method of random variable reparameterization. According to simulation experiments, the accuracy of the proposed method in classifying and recognizing beetles is 92.1%, and the accuracy in classifying and recognizing grasshoppers is 92.4%. The proposed method has an image recognition accuracy of over 90% for agricultural pests, and has the highest accuracy among the 5 image recognition methods compared. Except for the accuracy of cricket recognition, the convolutional neural network image recognition method has the lowest accuracy of agricultural pest image recognition among the five image recognition methods. The experimental results show that the proposed method can effectively recognize agricultural pest images and has good operational performance. The contribution of the research lies in proposing a novel agricultural pest image recognition algorithm and innovatively optimizing the structure of the convolutional neural network model. The Bayesian method is used to improve the estimation of weights and biases, making the model more accurate in processing agricultural pest images.

Keywords

• Image recognition
• neural networks
• Bayesian method
• probability distribution
• approximate function