Can Deep Learning Identify Tomato Leaf Disease?

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 fin...

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Main Authors: Keke Zhang, Qiufeng Wu, Anwang Liu, Xiangyan Meng
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2018/6710865
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author Keke Zhang
Qiufeng Wu
Anwang Liu
Xiangyan Meng
author_facet Keke Zhang
Qiufeng Wu
Anwang Liu
Xiangyan Meng
author_sort Keke Zhang
collection DOAJ
description 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.
format Article
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institution OA Journals
issn 1687-5680
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language English
publishDate 2018-01-01
publisher Wiley
record_format Article
series Advances in Multimedia
spelling doaj-art-86fdfb77cbe14ed9b9e162a6a9309f3f2025-08-20T02:08:32ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/67108656710865Can Deep Learning Identify Tomato Leaf Disease?Keke Zhang0Qiufeng Wu1Anwang Liu2Xiangyan Meng3College of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Science, Northeast Agricultural University, Harbin 150030, ChinaCollege of Engineering, Northeast Agricultural University, Harbin 150030, ChinaCollege of Science, Northeast Agricultural University, Harbin 150030, ChinaThis 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.http://dx.doi.org/10.1155/2018/6710865
spellingShingle Keke Zhang
Qiufeng Wu
Anwang Liu
Xiangyan Meng
Can Deep Learning Identify Tomato Leaf Disease?
Advances in Multimedia
title Can Deep Learning Identify Tomato Leaf Disease?
title_full Can Deep Learning Identify Tomato Leaf Disease?
title_fullStr Can Deep Learning Identify Tomato Leaf Disease?
title_full_unstemmed Can Deep Learning Identify Tomato Leaf Disease?
title_short Can Deep Learning Identify Tomato Leaf Disease?
title_sort can deep learning identify tomato leaf disease
url http://dx.doi.org/10.1155/2018/6710865
work_keys_str_mv AT kekezhang candeeplearningidentifytomatoleafdisease
AT qiufengwu candeeplearningidentifytomatoleafdisease
AT anwangliu candeeplearningidentifytomatoleafdisease
AT xiangyanmeng candeeplearningidentifytomatoleafdisease