Can Deep Learning Identify Tomato Leaf Disease?
Joint Authors
Zhang, Keke
Wu, Qiufeng
Liu, Anwang
Meng, Xiangyan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-26
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zhang, Keke& Wu, Qiufeng& Liu, Anwang& Meng, Xiangyan. 2018. Can Deep Learning Identify Tomato Leaf Disease?. Advances in Multimedia،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1118466
Modern Language Association (MLA)
Zhang, Keke…[et al.]. Can Deep Learning Identify Tomato Leaf Disease?. Advances in Multimedia No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1118466
American Medical Association (AMA)
Zhang, Keke& Wu, Qiufeng& Liu, Anwang& Meng, Xiangyan. Can Deep Learning Identify Tomato Leaf Disease?. Advances in Multimedia. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1118466
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118466