Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments
Accurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10749803/ |
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| author | Huinian Li Nannan Li Wenmin Wang Chengcheng Yang Ningxia Chen Fuqin Deng |
| author_facet | Huinian Li Nannan Li Wenmin Wang Chengcheng Yang Ningxia Chen Fuqin Deng |
| author_sort | Huinian Li |
| collection | DOAJ |
| description | Accurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-Guided Transformer (CSGT) model integrates CNN’s local feature extraction with the Self-Guided Transformer’s(SGT) global information processing, enhancing the precision and efficiency of agricultural diagnostics. We selected two datasets with characteristics from Jiangsu and Guangdong provinces to validate our model, respectively representing controlled and uncontrolled agricultural environments. The CSGT model demonstrates exceptional performance in diagnosing crop diseases across diverse backgrounds, overcoming the limitations of current models, especially in complex settings. The CSGT model’s CNN layer, hybrid-scale, and self-guided attention mechanisms ensure accurate diagnoses, even in the presence of background clutter. CSGT has an accuracy of 96.9%(Apple),95.8%(Corn), 96.1%(Grape), and 96.5%(Tomato) when classifying crop diseases in a stable environment, and 95.8%(Rice) when classifying diseases in a complex environment. The research results are anticipated to enhance the effectiveness and stability of natural crop disease recognition applications. |
| format | Article |
| id | doaj-art-b288e321d0b042be811f97da7935e32b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b288e321d0b042be811f97da7935e32b2025-08-20T02:32:45ZengIEEEIEEE Access2169-35362024-01-011216590316591710.1109/ACCESS.2024.349552910749803Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different EnvironmentsHuinian Li0https://orcid.org/0000-0001-7167-0042Nannan Li1https://orcid.org/0000-0003-3688-6766Wenmin Wang2https://orcid.org/0000-0003-2664-4413Chengcheng Yang3Ningxia Chen4Fuqin Deng5https://orcid.org/0000-0001-7585-3081School of Computer Science and Engineering, Macau University of Science and Technology, Macau, ChinaSchool of Computer Science and Engineering, Macau University of Science and Technology, Macau, ChinaSchool of Computer Science and Engineering, Macau University of Science and Technology, Macau, ChinaSchool of Computer Science and Engineering, Macau University of Science and Technology, Macau, ChinaCollege of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, ChinaSchool of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaAccurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-Guided Transformer (CSGT) model integrates CNN’s local feature extraction with the Self-Guided Transformer’s(SGT) global information processing, enhancing the precision and efficiency of agricultural diagnostics. We selected two datasets with characteristics from Jiangsu and Guangdong provinces to validate our model, respectively representing controlled and uncontrolled agricultural environments. The CSGT model demonstrates exceptional performance in diagnosing crop diseases across diverse backgrounds, overcoming the limitations of current models, especially in complex settings. The CSGT model’s CNN layer, hybrid-scale, and self-guided attention mechanisms ensure accurate diagnoses, even in the presence of background clutter. CSGT has an accuracy of 96.9%(Apple),95.8%(Corn), 96.1%(Grape), and 96.5%(Tomato) when classifying crop diseases in a stable environment, and 95.8%(Rice) when classifying diseases in a complex environment. The research results are anticipated to enhance the effectiveness and stability of natural crop disease recognition applications.https://ieeexplore.ieee.org/document/10749803/Vision transformercrop protectioncrop disease diagnosisself-guided attentionAI for agriculture |
| spellingShingle | Huinian Li Nannan Li Wenmin Wang Chengcheng Yang Ningxia Chen Fuqin Deng Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments IEEE Access Vision transformer crop protection crop disease diagnosis self-guided attention AI for agriculture |
| title | Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments |
| title_full | Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments |
| title_fullStr | Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments |
| title_full_unstemmed | Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments |
| title_short | Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments |
| title_sort | convolution self guided transformer for diagnosis and recognition of crop disease in different environments |
| topic | Vision transformer crop protection crop disease diagnosis self-guided attention AI for agriculture |
| url | https://ieeexplore.ieee.org/document/10749803/ |
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