Support Vector Machine for Detection of Tomato Ripeness Using Feature Extraction - HSV, RGB And LBP

Accurate detection of tomato ripeness is crucial for post-harvest processes, reducing waste, and ensuring the consistent quality product delivered which maintaining quality in agricultural production. This study investigates two feature extraction techniques for detection of tomato ripeness using a Support Vector Machine classifier: (1) color-based features combining HSV and RGB, and (2) texture-based features using LBP (Local Binary Patterns). The experimental results show that color based feature approach achieves a high classification accuracy of 98.88%, whereas the texture based method which records an accuracy of 82.3%. The superior performance of color features indicates their strong ability to capture the visual changes in color associated with different ripeness stages. While texture features provide useful information, they are less effective alone in distinguishing ripeness variations. The cross-validation results for color based feature show that the model achieves a high average accuracy of 95.51% with a low standard deviation of ±2.36%, indicating strong and consistent performance across different data splits.