Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming
The identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, sm...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/2479172 |
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| author | Yan Guo Jin Zhang Chengxin Yin Xiaonan Hu Yu Zou Zhipeng Xue Wei Wang |
| author_facet | Yan Guo Jin Zhang Chengxin Yin Xiaonan Hu Yu Zou Zhipeng Xue Wei Wang |
| author_sort | Yan Guo |
| collection | DOAJ |
| description | The identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. The results show that the accuracy of the method is 83.57%, which is better than the traditional method, thus reducing the influence of disease on agricultural production and being favorable to sustainable development of agriculture. Therefore, the deep learning algorithm proposed in the paper is of great significance in intelligent agriculture, ecological protection, and agricultural production. |
| format | Article |
| id | doaj-art-e748da9ff9094205859e4579febf9f44 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-e748da9ff9094205859e4579febf9f442025-02-03T01:05:23ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/24791722479172Plant Disease Identification Based on Deep Learning Algorithm in Smart FarmingYan Guo0Jin Zhang1Chengxin Yin2Xiaonan Hu3Yu Zou4Zhipeng Xue5Wei Wang6College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, ChinaCollege of Management, Sichuan Agricultural University, Ya’an, Sichuan, ChinaCollege of Management, Chengdu Aeronautic Polytechnic, Chengdu, Sichuan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, ChinaCollege of Management, Sichuan Agricultural University, Ya’an, Sichuan, ChinaThe identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. The results show that the accuracy of the method is 83.57%, which is better than the traditional method, thus reducing the influence of disease on agricultural production and being favorable to sustainable development of agriculture. Therefore, the deep learning algorithm proposed in the paper is of great significance in intelligent agriculture, ecological protection, and agricultural production.http://dx.doi.org/10.1155/2020/2479172 |
| spellingShingle | Yan Guo Jin Zhang Chengxin Yin Xiaonan Hu Yu Zou Zhipeng Xue Wei Wang Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming Discrete Dynamics in Nature and Society |
| title | Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming |
| title_full | Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming |
| title_fullStr | Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming |
| title_full_unstemmed | Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming |
| title_short | Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming |
| title_sort | plant disease identification based on deep learning algorithm in smart farming |
| url | http://dx.doi.org/10.1155/2020/2479172 |
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