Smart dairy farming for predicting milk production yield based on deep machine learning

The integration of predictive analytics, driven by advancements in Machine Learning and Artificial Intelligence, has significantly transformed the dairy industry. This study utilizes extensive datasets, which include milk production records, environmental data, and genetic profiles, to support the development of Artificial Intelligence and Machine Learning-based decision support systems.These sophisticated tools are capable of forecasting milk production, detecting significant patterns, and identifying key factors that impact dairy output. The insights gained from these systems enable dairy farmers to make well-informed decisions, efficiently allocate resources, improve operational efficiencies, and advance bovine health care practices. Moreover, the use of Artificial Intelligence and Machine Learning in predictive analytics allows farmers to quickly adapt to environmental changes, effectively manage risks, and increase productivity. This paper proposes a novel methodology for predicting milk yield and lactation patterns at various stages, using a comprehensive dataset from one of Jordan's largest dairy farms. The farm tracks approximately 4000 cattle, each outfitted with individual sensors, allowing for continuous and detailed monitoring of milk output. This data underpins the construction of robust, data-driven Artificial Intelligence and Machine Learning decision support models. We have employed a range of machine learning techniques, from traditional models to cutting-edge deep learning algorithms, to predict both short-term daily milk yields and long-term production over extended periods. These predictive models have shown significant potential in enhancing the management of dairy cattle productivity.