Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 336 agriculture, such as disease detection, milk production optimization, and animal behavior monitoring. It also discusses the potential benefits and limitations of these technologies, offers insights into future research directions and practical applications, and aims to contribute to the expanding body of knowledge in this field, supporting the adoption of ML technologies in dairy farming practices. 2 Current Applications of Machine Learning in Precision Dairy Farming 2.1 Animal health monitoring Machine learning (ML) has been extensively applied to the early detection of diseases in dairy farming. Various algorithms, including decision trees, artificial neural networks, and regression-based models, have been utilized to predict health issues such as mastitis, ketosis, lameness, and metritis. These models analyze data from sensors monitoring milk yield, physical activity, rumination time, and milk conductivity to identify cows at risk of developing health disorders (Halachmi and Guarino, 2016; Slob et al., 2020; Zhou et al., 2022). The integration of diverse data sources, such as housing, nutrition, and climate, further enhances the prediction accuracy, enabling timely interventions to prevent disease outbreaks (Lasser et al., 2021). Predictive models leveraging ML algorithms are also used to forecast the health and longevity of dairy cows. These models utilize historical data on cow characteristics, lactation, and farm conditions to predict future health outcomes and longevity. The use of supervised learning techniques, particularly classification methods, has shown promising results in predicting health-related events and improving decision support systems for farmers (Lokhorst et al., 2019; Cockburn, 2020; Shine and Murphy, 2021). The integration of large datasets from multiple farms can further improve the reliability and accuracy of these predictive models (Cockburn, 2020). 2.2 Milk production optimization ML algorithms are employed to optimize milk production by predicting milk yield based on various factors such as cow characteristics, lactation stage, and milking parameters. Decision tree-based algorithms and artificial neural networks are commonly used to analyze these variables and provide accurate predictions of milk yield (Cockburn et al., 2020; Slob et al., 2020; Sharma et al., 2021). The use of time-series data and supervised learning methods has been particularly effective in enhancing the precision of milk yield predictions (Lokhorst etal., 2019). Optimizing feed efficiency and nutrient balance is another critical application of ML in dairy farming. By analyzing data from feeding lists, behavioral sensors, and health records, ML models can predict the optimal feed composition and quantity for individual cows, thereby improving feed efficiency and nutrient utilization (Halachmi and Guarino, 2016; Cockburn, 2020; Sharma et al., 2021). These models help in reducing feed costs and enhancing milk production while maintaining the health and well-being of the cows. 2.3 Reproduction and fertility management ML-based heat detection systems have revolutionized reproduction management in dairy farming. These systems use data from behavioral sensors and activity monitors to accurately detect estrus in cows, enabling timely artificial insemination and improving reproductive efficiency (Halachmi and Guarino, 2016; Cockburn, 2020). The use of supervised learning algorithms, particularly classification methods, has been effective in identifying heat events with high accuracy (Lokhorst et al., 2019). Artificial intelligence (AI) techniques, including ML algorithms, are also used to predict fertility outcomes in dairy cows. By analyzing data on cow characteristics, health records, and environmental conditions, these models can forecast the likelihood of successful conception and calving (Sharma et al., 2021; Cockburn, 2020). The integration of diverse data sources and advanced ML techniques, such as deep learning, further enhances the accuracy of fertility predictions (Yousefi et al., 2022). 2.4 Environmental monitoring and management ML algorithms are employed to analyze real-time sensor data to monitor and manage barn conditions, such as temperature, humidity, and air quality. These models help in maintaining optimal environmental conditions for the cows, thereby improving their health and productivity (Halachmi and Guarino, 2016; Cockburn, 2020). The use of
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