Leveraging Prediction Strategies to Enhance Milk Quality and Promote Dairy Sustainability

Abstract

In the Dairy industry, ensuring high-quality milk production is vital. This research paper focuses on employing machine learning methods to predict milk quality based on diverse parameters including pH levels, temperature, fat content, and colour. By harnessing data encompassing these factors along with established quality markers, the study aims to construct a predictive model. This model seeks to anticipate variations in milk quality, providing insights into potential fluctuations in fat content, colour deviations, and pH imbalances. Leveraging diverse parameters such as pH, temperature, fat content, and colour as predictors, the study employed several machine learning algorithms including random forests, support vector machines, and neural networks. Through extensive data integration and feature engineering, the model was trained on a comprehensive dataset encompassing these variables alongside established quality metrics. Rigorous evaluation and cross-validation techniques were employed to assess and compare the performance of each algorithm in predicting milk quality attributes. The results showcase the efficacy of the trained model in accurately forecasting variations in fat content, colour deviations, pH imbalances, and overall milk quality. Thus the objective is to develop a comprehensive tool that predicts quality issues, enabling dairy practitioners to take proactive steps to maintain consistent, high-quality milk standards.