Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine.
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| Title: | Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine. |
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| Authors: | Abbigeri, Vijayalakshmi S.1 vijayalaxmisa@gmail.com, Devanagavi, Geetha D.1 dgeetha@reva.edu.in |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1399-1406. 8p. |
| Subjects: | Transformer models, Principal components analysis, Foliar diagnosis, Precision farming, Classification algorithms, Support vector machines, Agricultural technology, Feature extraction |
| Abstract: | Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT-PCA-SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194285627 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Abbigeri%2C+Vijayalakshmi+S%2E%22">Abbigeri, Vijayalakshmi S.</searchLink><relatesTo>1</relatesTo><i> vijayalaxmisa@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Devanagavi%2C+Geetha+D%2E%22">Devanagavi, Geetha D.</searchLink><relatesTo>1</relatesTo><i> dgeetha@reva.edu.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1399-1406. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Foliar+diagnosis%22">Foliar diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+technology%22">Agricultural technology</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT-PCA-SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i3.pp1399-1406 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1399 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Foliar diagnosis Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Agricultural technology Type: general – SubjectFull: Feature extraction Type: general Titles: – TitleFull: Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Abbigeri, Vijayalakshmi S. – PersonEntity: Name: NameFull: Devanagavi, Geetha D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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