Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network
The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In toda...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6683255 |
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| author | Li Ma Xueliang Guo Shuke Zhao Doudou Yin Yiyi Fu Peiqi Duan Bingbing Wang Li Zhang |
| author_facet | Li Ma Xueliang Guo Shuke Zhao Doudou Yin Yiyi Fu Peiqi Duan Bingbing Wang Li Zhang |
| author_sort | Li Ma |
| collection | DOAJ |
| description | The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification. |
| format | Article |
| id | doaj-art-821d9ed2851d44c3a38cda4861a9e006 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-821d9ed2851d44c3a38cda4861a9e0062025-02-03T06:07:36ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66832556683255Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural NetworkLi Ma0Xueliang Guo1Shuke Zhao2Doudou Yin3Yiyi Fu4Peiqi Duan5Bingbing Wang6Li Zhang7Henan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaHenan Provincial Key University Laboratory for Plant-Microbe Interactions, College of Biology and Food, Shangqiu Normal University, Shangqiu, Henan 476000, ChinaCollege of Biological Engineering, Henan University of Technology, Zhengzhou, Henan 450000, ChinaThe growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.http://dx.doi.org/10.1155/2021/6683255 |
| spellingShingle | Li Ma Xueliang Guo Shuke Zhao Doudou Yin Yiyi Fu Peiqi Duan Bingbing Wang Li Zhang Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network Complexity |
| title | Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network |
| title_full | Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network |
| title_fullStr | Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network |
| title_full_unstemmed | Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network |
| title_short | Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network |
| title_sort | algorithm of strawberry disease recognition based on deep convolutional neural network |
| url | http://dx.doi.org/10.1155/2021/6683255 |
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