Animal Molecular Breeding 2024, Vol.14, No.5, 335-344 http://animalscipublisher.com/index.php/amb 339 analysis. For instance, partitioning around medoids clustering has been used to model groups of farms based on production parameters and bulk tank milk antibody status of internal parasites (Oehm et al., 2022). This approach helps in understanding the complex relationships between parasitic infections and milk production, thereby aiding in better farm management. Anomaly detection techniques are crucial for identifying unusual patterns in feed intake and animal behavior, which can indicate health issues or management problems. These techniques help in monitoring the well-being of dairy cows by detecting deviations from normal behavior. For example, unsupervised clustering frameworks have been developed to study the natural structure of sensor data, which can then be used to guide supervised learning for more accurate behavioral classification (Dutta et al., 2015). This approach ensures that any anomalies in feed intake or behavior are promptly identified and addressed. 4.3 Deep learning approaches Deep learning, particularly neural networks, has shown great promise in image recognition tasks within precision dairy farming. Neural networks are used to analyze images and videos to detect conditions such as lameness in dairy cows. This method provides a non-invasive and efficient way to monitor animal health. For instance, neural network-based algorithms have been increasingly utilized for tasks like lameness detection, leveraging the power of deep learning to analyze complex visual data (Shine and Murphy, 2021). LSTM networks are a type of recurrent neural network (RNN) that are particularly effective for time-series predictions. In dairy farming, LSTM networks are used to predict future trends based on historical data, such as milk yield and cow health metrics. These networks can handle the temporal dependencies in the data, making them ideal for forecasting tasks. For example, time-series data, which is prevalent in animal-based farm data, is often analyzed using LSTM networks to predict future outcomes and improve farm management practices (Lokhorst et al., 2019). Machine learning models and techniques are revolutionizing precision dairy farming by providing advanced tools for prediction, classification, and anomaly detection. Supervised learning methods, including regression and classification algorithms, are widely used for predicting health and yield outcomes. Unsupervised learning techniques, such as clustering and anomaly detection, help in understanding complex patterns in animal behavior and health. Deep learning approaches, particularly neural networks and LSTM networks, offer powerful solutions for image recognition and time-series predictions. As the field continues to evolve, the integration of multiple data sources and advanced machine learning techniques will further enhance the efficiency and productivity of dairy farming. 5 Challenges and Limitations in Applying Machine Learning 5.1 Data quality and availability One of the primary challenges in applying machine learning (ML) in precision dairy farming is the inconsistency in data collection across different farms. Variability in data collection methods, sensor types, and data recording practices can lead to significant discrepancies in the datasets used for training ML models. This inconsistency can hinder the development of robust and generalizable models, as highlighted by the systematic literature review which identified data quality as a critical issue (Gengler et al., 2019; Slob et al., 2020). Additionally, the lack of standardized protocols for data collection exacerbates this problem, making it difficult to compare and integrate data from multiple sources (Cockburn, 2020; Shine and Murphy, 2021). The proliferation of sensors and IoT devices in dairy farming generates vast amounts of data, which poses a significant challenge in terms of data management and analysis. The volume of data can overwhelm traditional data processing systems, necessitating the use of advanced big data analytics and real-time data integration techniques (Lokhorst et al., 2019; Cabrera et al., 2019). Effective data management strategies are essential to handle the high volume, velocity, and variety of data generated, as noted in studies focusing on big data in precision dairy farming (Kolipaka, 2020). Moreover, ensuring data quality and consistency across these large datasets remains a persistent challenge (Gengler, 2019; Liu et al., 2023).
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