Multiclass Classification of Tomato Leaf Diseases Using GLCM, Color, and Shape Feature Extraction with Optimized XGBoost
DOI:
https://doi.org/10.30871/jaic.v9i6.11273Keywords:
Feature Extraction, Gray-Level Co-occurrence Matrix (GLCM), Principal Component Analysis (PCA), Smote, XGBoostAbstract
Automatic classification of tomato leaf diseases is an essential component in advancing precision agriculture based on artificial intelligence. This study aims to develop a multiclass classification model for tomato leaf diseases by utilizing texture, color, and shape features, and employing an optimized XGBoost algorithm. The public PlantVillage dataset was used, with preprocessing stages including feature extraction, normalization, dimensionality reduction using PCA, and class balancing using SMOTE. The experimental results showed that the model successfully classified ten disease classes with a high accuracy of 97.63%, and both macro and weighted f1-scores of 0.98. These findings indicate that the combination of handcrafted features and XGBoost offers an effective, efficient, and applicable solution for plant disease diagnostic systems.
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