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Application of Categorical Boosting Model in Classifying Diseases of Tomato Leaves
Abstract
Tomatoes are a strategic horticultural commodity whose productivity is often hampered by leaf diseases, particularly early blight and late blight. Manual identification through visual inspection is often inaccurate due to the similarity of symptoms between diseases. This study aims to improve the performance of tomato leaf disease classification using machine learning by overcoming the limitations of previous research by Ningsih et al., which only focused on disease classes without involving healthy leaf samples, so that the model may fail to recognize normal plant conditions. The proposed methodology integrates the VGG16 architecture as a feature extractor with the Categorical Boosting (CatBoost) algorithm as a classifier. The dataset sourced from Kaggle was processed through data cleaning to produce 3,285 images resized to 224x224 pixels. The experimental results show that the integration of VGG16-CatBoost provides good performance. The accuracy score achieved is 93.1%, while the F1 scores achieved are 90.2% (healthy leaves), 90.3% (early blight), and 98.6% (late blight). Compared to the research by Ningsih et al., this approach not only expands the scope of classification by including the healthy leaf class, but also shows better accuracy in identifying the health conditions of tomato plants
Keywords
Early Blight; Late Blight; Tomato Leaf; VGG16 Architecture; Categorical Boosting;
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DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.38869
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