Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 341 of potential issues. The use of IoT devices ensures that data is collected consistently and accurately, providing a robust foundation for the machine learning algorithms to operate effectively (Cabrera et al., 2019; Lokhorst et al., 2019; Lasser et al., 2021). The implementation of predictive health monitoring using machine learning has led to significant improvements in both animal health and milk production on the farm. Early detection of diseases such as mastitis and ketosis has allowed for timely interventions, reducing the severity and duration of these conditions. This proactive approach has not only improved the overall well-being of the cows but also enhanced milk yield and quality. The farm has reported a reduction in disease incidence rates and an increase in milk production efficiency, demonstrating the effectiveness of integrating machine learning with precision dairy farming practices (Cabrera et al., 2019; Lasser et al., 2021). 7 Future Prospects of Machine Learning in Precision Dairy Farming 7.1 Advancements in ml algorithms and their potential in dairy The future of machine learning (ML) in precision dairy farming is promising, with advancements in algorithms playing a crucial role. Decision tree-based algorithms and artificial neural networks have been widely used, but there is a growing interest in hybrid models that combine multiple techniques to enhance prediction accuracy and reliability (Slob et al., 2020; Cockburn, 2020). For instance, hybrid models like the K-medoids, random forest, and support vector regression (K-R-S) approach have shown significant improvements in predicting lactation curves, demonstrating the potential of advanced ML algorithms in dairy farming (Zhang et al., 2022). Additionally, the development of label-efficient learning methods, which require fewer labeled data for training, can address the challenges of data scarcity and high labeling costs, further enhancing the applicability of ML in dairy farming (Li et al., 2023). 7.2 Integration of ML with robotics and automated systems The integration of ML with robotics and automated systems is set to revolutionize precision dairy farming. Robotic milking systems, automated feeding systems, and behavioral sensors generate vast amounts of data that can be analyzed using ML algorithms to optimize farm operations (Cockburn, 2020). For example, the Dairy Brain project aims to create a real-time, data-driven decision-making engine by integrating data from various sources, including sensors and robotic systems, to improve whole-farm management (Cabrera et al., 2019). This integration can lead to more efficient resource utilization, better animal health monitoring, and enhanced productivity, ultimately transforming dairy farming practices. 7.3 Role of cloud computing and big data analytics in enhancing dairy productivity Cloud computing and big data analytics are essential for handling the large volumes of data generated in precision dairy farming. These technologies enable the storage, processing, and analysis of data from multiple sources, facilitating real-time decision-making and predictive analytics. The use of big data analytics can help identify patterns and trends in dairy farm data, leading to improved disease detection, milk production, and overall farm management. The integration of cloud computing with ML algorithms allows for scalable and efficient data processing, making it possible to leverage the full potential of big data in dairy farming (Lokhorst et al., 2019). 7.4 Policy and regulatory support for ml adoption in agriculture The successful adoption of ML in precision dairy farming requires supportive policies and regulations. Governments and regulatory bodies need to create frameworks that encourage the use of advanced technologies in agriculture while ensuring data privacy and security. Policy support can include funding for research and development, incentives for technology adoption, and the establishment of standards for data interoperability and integration. By fostering a conducive environment for ML adoption, policymakers can help accelerate the transformation of dairy farming, leading to increased productivity, sustainability, and profitability (Sharma et al., 2020). 8 Concluding Remarks The application of machine learning (ML) in precision dairy farming has seen significant advancements over recent years. A systematic review identified that more than half of the studies focused on disease detection, with
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