GLNet: global-local feature network for wheat leaf disease image classification
Addressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1471705/full |
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| author | Shangze Li Shen Liu Mingyu Ji Yuhao Cao Bai Yun Bai Yun |
| author_facet | Shangze Li Shen Liu Mingyu Ji Yuhao Cao Bai Yun Bai Yun |
| author_sort | Shangze Li |
| collection | DOAJ |
| description | Addressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block. By processing global and local feature blocks in parallel and integrating the information of both effectively with the help of feature fusion blocks, the model realizes the comprehensive capture of multi-scale features in images. This innovative design significantly enhances the model ability to understand the features of wheat leaf disease images, and thus demonstrates excellent performance and accuracy in the task of classifying wheat leaf disease images in real-world scenarios. The successful application of GLNet provides new ideas and effective tools for solving complex image classification problems. |
| format | Article |
| id | doaj-art-680f4c57dc9c4d99bf823c9b26fd553d |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-680f4c57dc9c4d99bf823c9b26fd553d2024-12-20T04:22:27ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14717051471705GLNet: global-local feature network for wheat leaf disease image classificationShangze Li0Shen Liu1Mingyu Ji2Yuhao Cao3Bai Yun4Bai Yun5Aulin College, Northeast Forestry University, Harbin, ChinaAulin College, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, ChinaCollege of International Studies, National University of Defense Technology, Nanjing, ChinaBasic Education College, National University of Defense Technology, Changsha, ChinaAddressing the issues with insufficient multi-scale feature perception and incomplete understanding of global information in traditional convolutional neural networks for image classification of wheat leaf disease, this paper proposes a global local feature network, i.e. GLNet, which adopts a unique global-local convolutional neural network architecture, realizes the comprehensive capturing of multi-scale features in an image by processing the global feature block and local feature block in parallel and integrating the information of both of them with the help of a feature fusion block. By processing global and local feature blocks in parallel and integrating the information of both effectively with the help of feature fusion blocks, the model realizes the comprehensive capture of multi-scale features in images. This innovative design significantly enhances the model ability to understand the features of wheat leaf disease images, and thus demonstrates excellent performance and accuracy in the task of classifying wheat leaf disease images in real-world scenarios. The successful application of GLNet provides new ideas and effective tools for solving complex image classification problems.https://www.frontiersin.org/articles/10.3389/fpls.2024.1471705/fullconvolutional neural networkwheat leaf diseaseimage classificationmultiscale featuresGLNet model |
| spellingShingle | Shangze Li Shen Liu Mingyu Ji Yuhao Cao Bai Yun Bai Yun GLNet: global-local feature network for wheat leaf disease image classification Frontiers in Plant Science convolutional neural network wheat leaf disease image classification multiscale features GLNet model |
| title | GLNet: global-local feature network for wheat leaf disease image classification |
| title_full | GLNet: global-local feature network for wheat leaf disease image classification |
| title_fullStr | GLNet: global-local feature network for wheat leaf disease image classification |
| title_full_unstemmed | GLNet: global-local feature network for wheat leaf disease image classification |
| title_short | GLNet: global-local feature network for wheat leaf disease image classification |
| title_sort | glnet global local feature network for wheat leaf disease image classification |
| topic | convolutional neural network wheat leaf disease image classification multiscale features GLNet model |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1471705/full |
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