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: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/6710865 |
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| _version_ | 1850215651852943360 |
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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 |
| id | doaj-art-86fdfb77cbe14ed9b9e162a6a9309f3f |
| institution | OA Journals |
| issn | 1687-5680 1687-5699 |
| 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 |