Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM)

Abstract

Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.

Plant phenotyping relevance

植物葉の病害状態を画像から分類する手法の開発・評価が研究の中心であり、GLCM特徴量とRF/SVMの性能を交差検証しているため、植物フェノタイピング手法として収録する。

Code and data availability

The paper describes a banana/ornamental leaf image dataset partly sourced from Kaggle, but the reference is a placeholder ('Dataset Link') with no actual URL, repository name, or identifier. No author analysis code, trained models, or supplementary data deposits are mentioned anywhere in the supplied blocks.

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