Predictive Modeling of Milk Production Using Artificial Intelligence and Machine Learning Techniques

Abstract The precise prediction of milk production is essential for optimizing dairy farm management, enhancing resource allocation, and improving economic returns. In this study, we present a predictive modeling framework designed to estimate milk yield through the integration of artificial intelligence (AI) and machine learning (ML) techniques. Utilizing real-world datasets from dairy farms, we analyzed key features such as animal breed, age, feed intake, lactation stage, and environmental conditions to construct robust predictive models. Various machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Gradient Boosting, were implemented and compared, alongside deep learning approaches such as Artificial Neural Networks (ANN). Model performance was assessed using standard metrics, including R², RMSE, and MAE, to evaluate accuracy and reliability. The findings indicate that ensemble and deep learning models surpass traditional methods, offering significant potential for practical application in precision dairy farming. This research underscores the value of intelligent data-driven approaches for informed decision-making within the dairy industry. keywords : Dairy Alchemy,Intelligent Herding, Milk Oracle, Farming Synergy,Data-driven Pastures