Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (23): 178-184.DOI: 10.3778/j.issn.1002-8331.2105-0321

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

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

HUANG Yinglai, AI Xin   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
  • Online:2021-12-01 Published:2021-12-02



  1. 东北林业大学 信息与计算机工程学院,哈尔滨 150040


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.

Key words: corn leaf disease, image recognition, deep learning, residual network, transfer learning



关键词: 玉米叶片病害, 图像识别, 深度学习, 残差网络, 迁移学习