Identification of winter wheat pests and diseases based on improved convolutional neural network
Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-07-01
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| Series: | Open Life Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/biol-2022-0632 |
| _version_ | 1797784155143012352 |
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| author | Yao Jianbin Liu Jianhua Zhang Yingna Wang Hansheng |
| author_facet | Yao Jianbin Liu Jianhua Zhang Yingna Wang Hansheng |
| author_sort | Yao Jianbin |
| collection | DOAJ |
| description | Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field. Data expansion and transfer learning technology are introduced to improve the training mode, and then attention mechanism is introduced for further improvement. The experimental results show that the transfer learning scheme of fine-tuning source model is better than that of freezing source model, and the VGGNet16 based on fine-tuning all layers has the best recognition effect, with an accuracy of 96.02%. The CBAM-VGGNet16 and NLCBAM-VGGNet16 models are designed and implemented. The experimental results show that the recognition accuracy of the test set of CBAM-VGGNet16 and NLCBAM-VGGNet16 is higher than that of VGGNet16. The recognition accuracy of CBAM-VGGNet16 and NLCBAM-VGGNet16 is 96.60 and 97.57%, respectively, achieving high precision recognition of common pests and diseases of winter wheat. |
| first_indexed | 2024-03-13T00:35:55Z |
| format | Article |
| id | doaj.art-8a7a8eff4da14b4db98268acd1c97de0 |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-03-13T00:35:55Z |
| publishDate | 2023-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-8a7a8eff4da14b4db98268acd1c97de02023-07-10T06:03:14ZengDe GruyterOpen Life Sciences2391-54122023-07-011814710.1515/biol-2022-0632Identification of winter wheat pests and diseases based on improved convolutional neural networkYao Jianbin0Liu Jianhua1Zhang Yingna2Wang Hansheng3School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, ChinaWheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field. Data expansion and transfer learning technology are introduced to improve the training mode, and then attention mechanism is introduced for further improvement. The experimental results show that the transfer learning scheme of fine-tuning source model is better than that of freezing source model, and the VGGNet16 based on fine-tuning all layers has the best recognition effect, with an accuracy of 96.02%. The CBAM-VGGNet16 and NLCBAM-VGGNet16 models are designed and implemented. The experimental results show that the recognition accuracy of the test set of CBAM-VGGNet16 and NLCBAM-VGGNet16 is higher than that of VGGNet16. The recognition accuracy of CBAM-VGGNet16 and NLCBAM-VGGNet16 is 96.60 and 97.57%, respectively, achieving high precision recognition of common pests and diseases of winter wheat.https://doi.org/10.1515/biol-2022-0632wheat diseases and pestsdata expansiontransfer learningmixed attention mechanism |
| spellingShingle | Yao Jianbin Liu Jianhua Zhang Yingna Wang Hansheng Identification of winter wheat pests and diseases based on improved convolutional neural network Open Life Sciences wheat diseases and pests data expansion transfer learning mixed attention mechanism |
| title | Identification of winter wheat pests and diseases based on improved convolutional neural network |
| title_full | Identification of winter wheat pests and diseases based on improved convolutional neural network |
| title_fullStr | Identification of winter wheat pests and diseases based on improved convolutional neural network |
| title_full_unstemmed | Identification of winter wheat pests and diseases based on improved convolutional neural network |
| title_short | Identification of winter wheat pests and diseases based on improved convolutional neural network |
| title_sort | identification of winter wheat pests and diseases based on improved convolutional neural network |
| topic | wheat diseases and pests data expansion transfer learning mixed attention mechanism |
| url | https://doi.org/10.1515/biol-2022-0632 |
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