Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 335 Review and Progress Open Access Optimizing Dairy Farm Operations through IoT and Machine Learning: A Case Study Approach Hui Liu, Shiqiang Huang Tropical Animal Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572000, Hainan, China Corresponding author: shiqiang.huang@hitar.org Animal Molecular Breeding, 2024, Vol.14, No.5 doi: 10.5376/amb.2024.14.0035 Received: 27 Aug., 2024 Accepted: 06 Oct., 2024 Published: 20 Oct., 2024 Copyright © 2024 Liu and Huang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Liu H., and Huang S.Q., 2024, Optimizing dairy farm operations through iot and machine learning: a case study approach, Animal Molecular Breeding, 14(5): 335-344 (doi: 10.5376/amb.2024.14.0035) Abstract This study explores the integration of machine learning (ML) within precision dairy farming, providing a comprehensive analysis of its applications, including animal health monitoring, milk production optimization, reproduction management, and environmental monitoring. It highlights the role of various data sources, such as IoT devices, genomic data, and behavioral patterns, in training effective ML models. The study delves into key ML techniques, including supervised, unsupervised, and deep learning methods, while addressing the challenges of data quality, integration, and ethical concerns. A case study on predictive health monitoring in a large-scale dairy farm demonstrates the practical benefits of ML, emphasizing improved disease management and production outcomes. The study concludes by discussing the future potential of ML, focusing on advancements in robotics, cloud computing, and policy frameworks to foster sustainable dairy farming. This study offers valuable insights for researchers and practitioners, envisioning a data-driven future for precision agriculture. Keywords Precision dairy farming; Machine learning; Animal health monitoring; Predictive analytics; Sustainable agriculture 1 Introduction Precision dairy farming represents a transformative approach in the agricultural sector, leveraging advanced technologies to enhance the efficiency and productivity of dairy farms. This method integrates various data-driven tools and techniques to monitor and manage dairy cattle, aiming to optimize milk production, improve animal health, and reduce environmental impact. The advent of precision farming has been driven by the need to meet the growing demand for dairy products while ensuring sustainable farming practices (García et al., 2020; Slob et al., 2020; Sharma et al., 2021). Modern dairy farming has seen a significant shift with the incorporation of technologies such as the Internet of Things (IoT), big data analytics, and machine learning (ML). These technologies enable real-time monitoring and data collection from various sensors placed on animals and farm equipment. For instance, IoT devices can track cow behavior, health parameters, and environmental conditions, providing valuable insights for farm management (Akhter and Sofi, 2021). Big data analytics further processes this information to identify patterns and trends, facilitating informed decision-making (García et al., 2020). The integration of these technologies has revolutionized dairy farming, making it more precise, efficient, and sustainable (Lokhorst et al., 2019; Slob et al., 2020). Machine learning, a subset of artificial intelligence, involves the development of algorithms that can learn from and make predictions based on data. In the context of dairy farming, ML algorithms analyze vast amounts of data collected from various sources to predict outcomes such as milk yield, disease outbreaks, and optimal feeding times (Slob et al., 2020; Cockburn, 2020). These algorithms can handle complex and high-dimensional data, making them ideal for applications in precision dairy farming where multiple variables need to be considered simultaneously (Condran et al., 2022). The use of ML in dairy farming not only enhances productivity but also improves animal welfare by enabling early detection of health issues and optimizing resource use (Shine and Murphy, 2021). This study provides a comprehensive review of current trends and future prospects of machine learning in precision dairy farming, exploring how ML techniques are utilized to address various challenges in dairy
RkJQdWJsaXNoZXIy MjQ4ODYzNA==