guo tianxia

Tropical Animal Resources Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572000, Hainan, China
Author    Correspondence author
Animal Molecular Breeding, 2024, Vol. 14, No. 5   doi: 10.5376/amb.2024.14.0035
Received: 27 Aug., 2024    Accepted: 02 Oct., 2024    Published: 20 Nov., 2024

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.

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): 130-140 (doi: 10.5376/amb.2024.14.0035)

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.

Precision dairy farming; Machine learning; Animal health monitoring; Predictive analytics; Sustainable agriculture