.png){kind=icon}
返回
Agricultural Pest Image Recognition Algorithm Based on Convolutional Neural Network and Bayesian Method
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3512671.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Bayes methods
Image recognition
Convolutional neural networks
Accuracy
Neural networks
Kernel
Gaussian distribution
Data models
Training
Optimization
neural networks
Bayesian method
probability distribution
approximate function
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Complementing human judgment of essays written by English language learners with e-rater® scoring
PREAI
分享收藏RevGadgets: An R package for visualizing Bayesian phylogenetic analyses from RevBayesRevGadgets: 一个R包，用于从RevBayes可视化贝叶斯系统发育分析
OAAI
OAAI分享收藏Camera, LiDAR, and Radar Sensor Fusion Based on Bayesian Neural Network (CLR-BNN).png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
查看更多内容