Research on Classification of Corn Leaf Disease Image by Improved Residual Network

被引：3

作者：

Huang, Yinglai ^{[1
]}

Ai, Xin ^{[1
]}

机构：

[1] College of Information and Computer Engineering, Northeast Forestry University, Harbin

来源：

Computer Engineering and Applications | 2024年 / 57卷 / 23期

关键词：

corn leaf disease; deep learning; image recognition; residual network; transfer learning;

D O I：

10.3778/j.issn.1002-8331.2105-0321

中图分类号：

学科分类号：

摘要：

Aiming at the problems of low accuracy and slow speed of traditional corn leaf disease image recognition methods, a corn leaf image recognition algorithm based on improved deep residual network model is proposed. Here are the proposed improvement strategies：replacing the 7×7 convolution kernel in the first convolution layer of the traditional ResNet-50 model with three 3×3 convolution kernels; using the LeakyReLU activation function to replace the ReLU activation function; changing the order of the batch normalization layer, activation function and convolutional layer in the residual block. Firstly, data preprocessing is carried out, dividing the ratio of training set and test set to 4∶1, using data enhancement to expand the training set. Subsequently, the improved ResNet-50 model is subjected to transfer learning to obtain the weight parameters pre-trained on ImageNet. The experimental results show that the improved network has a 98.3% correct rate in corn leaf disease images classification. Compared with other network models, the accuracy rate is greatly improved, and the robustness is further enhanced, which can provide a reference for the recognition of corn leaf diseases. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：178 / 184

页数：6

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