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Advances in Multimedia
Volume 2018, Article ID 6710865, 10 pages
https://doi.org/10.1155/2018/6710865
Research Article

Can Deep Learning Identify Tomato Leaf Disease?

1College of Engineering, Northeast Agricultural University, Harbin 150030, China
2College of Science, Northeast Agricultural University, Harbin 150030, China

Correspondence should be addressed to Qiufeng Wu; nc.ude.uaen@uwfq

Received 9 June 2018; Accepted 30 August 2018; Published 26 September 2018

Academic Editor: Alexander Loui

Copyright © 2018 Keke Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.