Classification of Oil Palm Fruit Ripeness Levels Based on Digital Image Feature Extraction Using the Catboost Algorithm
DOI:
https://doi.org/10.35335/jict.v17i1.327Keywords:
Oil Palm, CatBoost, GLCM, Feature Extraction, Web-Based SystemAbstract
Determining the ripeness level of oil palm fruit is essential for improving palm oil production quality. Manual assessment methods are often subjective and inconsistent because they rely on workers’ experience and environmental conditions. Therefore, this study proposes an automatic image-based classification system using the CatBoost algorithm. The novelty of this research lies in the integration of CatBoost with RGB color and Gray Level Co-occurrence Matrix (GLCM) texture feature extraction for multiclass oil palm fruit ripeness classification. The dataset consisted of 1000 images categorized into four classes: unripe, under-ripe, ripe, and overripe. The research stages included image preprocessing, feature extraction, classification, and web-based implementation using the Flask framework. Experimental results showed that the proposed system achieved high performance based on accuracy, precision, recall, and F1-score metrics, demonstrating the effectiveness of CatBoost in classifying oil palm fruit ripeness while reducing overfitting. The developed web-based system can assist plantation workers in determining fruit ripeness automatically, objectively, and efficiently, thereb
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