Machine Learning Techniques for Improving and Predicting Milk Yield in Dairy Cows

Abstract

As the global demand for dairy products grows, there is a pressing need for smarter solutions to maximize milk production efficiency. This study investigates the use of machine learning (ML) techniques to both predict and enhance milk yield in dairy cows. Utilizing comprehensive farm data—such as lactation history, dietary intake, environmental variables, and cow health indicators—various ML models are trained to recognize trends and deliver accurate yield forecasts. The paper compares the effectiveness of different algorithms, including linear regression, decision trees, support vector machines, and deep learning models. Additionally, it discusses how predictive analytic can be integrated with real-time monitoring to aid in strategic herd management and decision-making. Findings show that ML-driven approaches not only improve forecasting accuracy but also contribute to operational efficiency and sustainability in dairy farming. This research underscores the role of intelligent technologies in transforming traditional dairy operations into data-informed, high-performing systems...