Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 337 IoT-enabled sensors and ML techniques allows for continuous monitoring and timely adjustments to barn conditions (Sharma et al., 2021). Optimizing the use of water and feed resources is another important application of ML in precision dairy farming. By analyzing data on water usage, feed intake, and environmental conditions, ML models can predict the optimal allocation of these resources, reducing waste and improving efficiency (Sharma et al., 2021). These models help in sustainable resource management and contribute to the overall profitability of dairy farms. Machine learning has become an integral part of precision dairy farming, offering innovative solutions for animal health monitoring, milk production optimization, reproduction management, and environmental monitoring. The integration of diverse data sources and advanced ML techniques holds great promise for the future, enabling more accurate predictions and efficient farm management practices. As the technology continues to evolve, the potential for ML in precision dairy farming will only grow, driving further advancements in the industry. 3 Data Sources for Machine Learning in Dairy Farming 3.1 Sensor technology and IoT devices Wearable sensors have become increasingly accessible and affordable, providing valuable data for monitoring cow behavior and health. These sensors can track various activities such as feeding, rumination, and movement, which are crucial for early disease detection and overall herd management. For instance, commercial acceleration measuring tags connected via Bluetooth Low Energy (BLE) have been used to classify feeding behavior with high accuracy using convolutional neural networks (CNNs) and transfer learning techniques (Bloch et al., 2023). Additionally, wearable precision dairy technologies (WPDT) can monitor time spent at the feed bunk, rumination time, eating time, lying time, standing time, walking time, activity, and transitions between lying and standing, providing comprehensive behavioral data (Eckelkamp, 2019). Barn monitoring systems equipped with sensors and cameras generate large amounts of data that can be used for real-time monitoring and predictive analytics. These systems can capture data on environmental conditions, cow positioning, and interactions within the barn. For example, automated systems can measure feed intake and environmental parameters, which are essential for optimizing feeding strategies and improving animal welfare (Koltes et al., 2021). Integrating these data sources with machine learning models can enhance the prediction of disease risk and other critical parameters, thereby improving farm management practices (Lasser et al., 2021). 3.2 Genomic data and animal genetics Genomic data provides a wealth of information that can be integrated into machine learning models to predict various traits and improve breeding programs. By combining genomic data with other data sources such as phenotypic and environmental data, more accurate and robust predictive models can be developed. This integration allows for the identification of genetic markers associated with desirable traits, thereby facilitating more informed breeding decisions (Cockburn, 2020). The use of high-throughput assays and omics data can further enhance the precision of these models, enabling the identification of complex trait relationships and underlying genetics (Koltes, 2021). Predictive analytics can significantly improve breeding programs by identifying animals with the highest genetic potential for specific traits. Machine learning algorithms can analyze large datasets to predict outcomes such as milk yield, disease resistance, and reproductive performance. For example, integrating diverse data sources, including genomic, phenotypic, and environmental data, can improve the prediction of disease risk and other important traits, leading to more effective breeding strategies (Lasser et al., 2021). This approach not only enhances the genetic quality of the herd but also contributes to overall farm productivity and sustainability (Cockburn, 2020). 3.3 Behavioral and environmental data Behavioral data, such as feeding, drinking, and social interactions, provide critical insights into the health and well-being of dairy cows. Machine learning models can analyze these behaviors to detect anomalies that may
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