Machine Learning Driven Approach for Modelling Milk Production in India

Milk production in India is integral to its food and nutritional security and is instrumental in achieving SDGs 1, 2, and 5. Accurate early prediction of milk production is crucial for data-driven policymaking, with implications for resource allocation and supply chain optimization. However, modelling milk production remains complex due to its dependence on multiple factors and the significant regional heterogeneity within the country. While the application of Artificial Intelligence (AI) driven approaches has expanded modelling capabilities in the realm of livestock production, their application in modelling milk production remains underexplored within the Indian context. Therefore, the present study aimed to develop state-level predictive models for milk production, employing a suite of Machine Learning (ML) algorithms. Time-series data encompassing fifteen variables from 2000-01 to 2022-23 across twenty-seven Indian states were systematically collected and analyzed. Seven machine learning techniques, including Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LightGBM), CatBoost (CB), and Ensemble techniques, were tested and compared using an extensive array of performance metrics. Notably, the ensemble technique combining DT, RF, and XGB demonstrated superior predictive accuracy, surpassing individual models. Conversely, DT exhibited the weakest performance relatively. The findings clearly point to genetic improvement, remunerative pricing for producers, and assured irrigation as strategic priorities to boost India’s milk production.