Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine.

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Bibliographic Details
Title:	Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine.
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]
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Database:	Engineering Source

Description
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]
ISSN:	20888708
DOI:	10.11591/ijece.v16i3.pp1399-1406