Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, com...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16142-x |
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| author | Sk Mahmudul Hassan Kumar Sekhar Roy Ruhul Amin Hazarika Mehbub Alam Mithun Mukherjee |
| author_facet | Sk Mahmudul Hassan Kumar Sekhar Roy Ruhul Amin Hazarika Mehbub Alam Mithun Mukherjee |
| author_sort | Sk Mahmudul Hassan |
| collection | DOAJ |
| description | Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset. |
| format | Article |
| id | doaj-art-436a2dbb371c4d8184aa2224a064d3c0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-436a2dbb371c4d8184aa2224a064d3c02025-08-24T11:25:05ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-16142-xMulti-kernel inception-enhanced vision transformer for plant leaf disease recognitionSk Mahmudul Hassan0Kumar Sekhar Roy1Ruhul Amin Hazarika2Mehbub Alam3Mithun Mukherjee4Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationManipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationManipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationDepartment of IT, Indian Institute of Information TechnologyNanjing University of Information Science and TechnologyAbstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.https://doi.org/10.1038/s41598-025-16142-xPlant diseaseMachine learningVision transformerDeep learning |
| spellingShingle | Sk Mahmudul Hassan Kumar Sekhar Roy Ruhul Amin Hazarika Mehbub Alam Mithun Mukherjee Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition Scientific Reports Plant disease Machine learning Vision transformer Deep learning |
| title | Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition |
| title_full | Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition |
| title_fullStr | Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition |
| title_full_unstemmed | Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition |
| title_short | Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition |
| title_sort | multi kernel inception enhanced vision transformer for plant leaf disease recognition |
| topic | Plant disease Machine learning Vision transformer Deep learning |
| url | https://doi.org/10.1038/s41598-025-16142-x |
| work_keys_str_mv | AT skmahmudulhassan multikernelinceptionenhancedvisiontransformerforplantleafdiseaserecognition AT kumarsekharroy multikernelinceptionenhancedvisiontransformerforplantleafdiseaserecognition AT ruhulaminhazarika multikernelinceptionenhancedvisiontransformerforplantleafdiseaserecognition AT mehbubalam multikernelinceptionenhancedvisiontransformerforplantleafdiseaserecognition AT mithunmukherjee multikernelinceptionenhancedvisiontransformerforplantleafdiseaserecognition |