Enhancing Milk Yield Forecasting in Dairy Farming Using an Interpretable Machine Learning Framework

This study employs machine learning techniques and explainable AI (XAI) to enhance milk yield predictions and optimizes dairy farming practices. Split k-means clustering, KNN, SVM combined with k-means, and regression techniques such as binomial, polynomial, and logistic regression are all used in this methodology. KNN in conjunction with SVM improves prediction accuracy even further. Explainable AI techniques, such as SHAP, provide clarity by emphasizing feature significance, while diverse representations improve data scrutiny. These methods offer practical insights into improving dairy farming operations, ensuring efficient resource utilization, and increasing milk output.