Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 342 other prominent areas being milk production and quality. The use of decision tree-based algorithms and artificial neural networks has been prevalent, with sensitivity, specificity, and RMSE being the most common evaluation metrics. Big Data analytics, particularly supervised learning methods, have also been extensively applied, with a notable focus on animal-based farm data. The integration of various data sources, such as sensors and herd management systems, has been crucial in developing predictive models for milk yield and animal health. Despite the progress, challenges such as data integration, feature selection, and handling unbalanced data remain. One of the primary challenges in the application of ML in dairy farming is the integration of diverse data sources to improve the reliability and accuracy of predictive models. The need for larger, integrated datasets that cover longer periods is essential to enhance the performance of ML algorithms. Additionally, addressing issues such as class imbalance, data sparsity, and high dimensionality is crucial for the effective application of ML techniques in agriculture. There is also a growing need for the development of hybrid models that can diagnose and prescribe solutions for animal health issues, thereby providing a more comprehensive approach to precision livestock farming. The potential of Big Data in precision dairy farming is yet to be fully realized, and future research should focus on utilizing multiple Big Data characteristics and sources simultaneously to add value to decision-making processes. Machine learning holds immense potential in promoting sustainable dairy farming by enhancing productivity and animal welfare while reducing resource exploitation and environmental impact. The use of ML algorithms in precision livestock farming can lead to more efficient feeding practices, better health monitoring, and improved management of dairy herds. By leveraging IoT and AI technologies, farmers can monitor animal behavior, health, and feed intake in near real-time, enabling timely interventions and better management practices. The integration of ML with other disruptive technologies such as cloud computing and blockchain can further enhance the sustainability of agricultural supply chains by providing real-time analytic insights for proactive decision-making. As the field continues to evolve, the adoption of ML in dairy farming is expected to play a crucial role in ensuring food security, ecological sustainability, and economic growth. Acknowledgements We would like to express our gratitude to reviewers for their suggested revisions to this study. Conflict of Interest Disclosure Authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akhter R., and Sofi S., 2021, Precision agriculture using IoT data analytics and machine learning, J. King Saud Univ. Comput. Inf. Sci., 34: 5602-5618. https://doi.org/10.1016/J.JKSUCI.2021.05.013 Bloch V., Frondelius L., Arcidiacono C., Mancino M., and Pastell M., 2023, Development and analysis of a CNN-and transfer-learning-based classification model for automated dairy cow feeding behavior recognition from accelerometer data, Sensors (Basel, Switzerland), 23(5): 2611. https://doi.org/10.3390/s23052611 PMid:36904813 PMCid:PMC10006954 Cabrera V., Barrientos-Blanco J., Delgado H., and Fadul-Pacheco L., 2019, Symposium review: real-time continuous decision making using big data on dairy farms, Journal of Dairy Science, 103(4): 3856-3866. https://doi.org/10.3168/jds.2019-17145 PMid:31864744 Cockburn M., 2020, Review: application and prospective discussion of machine learning for the management of dairy farms, Animals, 10(9): 1690. https://doi.org/10.3390/ani10091690 PMid:32962078 PMCid:PMC7552676 Condran S., Bewong M., Islam M., Maphosa L., and Zheng L., 2022, Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades, IEEE Access, 10: 73786-73803. https://doi.org/10.1109/ACCESS.2022.3188649 Dutta R., Smith D., Rawnsley R., Bishop-Hurley G., Hills J., Timms G., and Henry D., 2015, Dynamic cattle behavioural classification using supervised ensemble classifiers, Comput. Electron. Agric., 111: 18-28. https://doi.org/10.1016/j.compag.2014.12.002
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