<![CDATA[Postharvest tomato deterioration remains a major challenge due to manual and subjective quality assessment, which may lead to inconsistent sorting results and inaccurate shelf-life estimation. This study aims to develop a tomato quality classification system and predict potential shelf life based on digital image processing using the Random Forest algorithm. The study employed 936 tomato images and 450 non-tomato images collected independently. The extracted features consisted of Red Green Blue (RGB) and Hue Saturation Value (HSV) color features, as well as Gray Level Co-occurrence Matrix (GLCM) texture features. Tomato quality was classified into three categories, namely Poor, Medium, and Good, using a Random Forest Classifier, while shelf-life prediction was performed using a Random Forest Regressor. The classification model achieved an accuracy of 96.81%, precision of 96.82%, recall of 96.81%, and an F1-score of 96.81%. The regression model produced a Mean Absolute Error (MAE) of 0.0621, a Root Mean Square Error (RMSE) of 0.1152, and an R² value of 0.8752, while cross-validation yielded an average accuracy of 95.83% ± 1.24%, indicating stable model performance. Feature importance analysis revealed that color features contributed the most to both models, with g_mean identified as the most influential feature for tomato quality classification and shelf-life prediction. This study contributes to the development of a tomato quality assessment system capable of simultaneously classifying tomato quality and predicting shelf-life potential based on digital image processing using the Random Forest algorithm. In addition, feature importance analysis is employed to identify the visual characteristics that have the greatest influence on model performance. The results demonstrate that the proposed approach has the potential to support tomato sorting and postharvest management processes in a more objective and efficient manner.]]>
