Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-06, Vol.20 (12), p.3535
Main Authors: Yan, Qian, Yang, Baohua, Wang, Wenyan, Wang, Bing, Chen, Peng, Zhang, Jun
Format: Article
Language:English
Subjects:
Accuracy
apple leaf diseases
Classification
convolutional neural networks
Datasets
deep learning
Deoxidizing
Identification
Machine learning
Malus
Neural networks
Neural Networks, Computer
Plant Diseases
Plant Leaves
Recognition
Support vector machines
transfer learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20123535