Predicting Tea Quality Based on Antioxidant and Polyphenol Content Using Ensemble Machine Learning Model

Made from Camellia sinensis, tea is highly valued for numerous health benefits and rich phytochemical makeup. This research aims to determine whether antioxidant and polyphenol levels may be used to predict the quality of tea using machine learning techniques. The main objective is to improve prediction accuracy using ensemble models that integrate the Random Forest and Decision Tree methods. The results showed the effectiveness of the ensemble model, with up to 99.89% accuracy in polyphenol and antioxidant dataset tea quality prediction. The proposed research highlights the potential of machine learning to enhance the efficiency of tea production procedures, guarantee uniformity in product quality, and propel scientific comprehension of tea's nutritional and health advantages. By utilizing cutting-edge procedures, the tea industry may improve manufacturing processes and offer consumers high-quality goods enhanced with health-promoting phytochemicals.