CPS: MEDIUM: INTEGRATED REAL-TIME MONITORING, DIAGNOSIS, AND PREDICTIVE DATA ANALYTICS FOR EARLY DECISION-MAKING AND TREATMENT OF PREVALENT DISEASES IN PRECISION DAIRY FARMING

This project employs a multidisciplinary methodology combining real-time data analytics, AI and machine learning (ML) techniques, farm-wide social interaction analysis, and digital twin technology to enhance dairy cattle health by monitoring, diagnosis, and predictions. We list and describe the major methods below.1. ML and AI Model Development and ImplementationThe project involves the development of AI/ML models to predict dairy cattle health outcomes based on biological sensor data. These datasets collected will also include health records, production statistics, and environmental factors. The models will be developed using a combination of supervised and unsupervised learning techniques. They will be validated during deployment to ensure their reliability and accuracy.2. Real-time Data Analytical MethodsTo achieve the goals of real-time monitoring, we develop data analytical methods that involve integrating AI/ML models with sensor-based data collection systems on the dairy farm. The data analytical methods will analyze real-time data on cattle health, social interactions, and environmental conditions to predict potential health issues and provide actionable insights.3. Development of Digital Twin Methodology for the Farm EnvironmentA digital twin methodology of the dairy farm will be developed, integrating real-time health analytics with a virtual representation of the dairy farm. The digital twin will provide a comprehensive and dynamic view of the farm operations, allowing farmers to simulate different what-if scenarios and evaluate their potential impacts on cattle health and productivity.4. Evaluation of Project MethodsThe project methods will be evaluated regarding their impacts on dairy cattle health, farm productivity, and potential profitability. Key performance indicators (KPIs), such as the effectiveness of the developed AI/ML algorithms, the farm-wide analytics, and the biosensors, will be monitored and evaluated to demonstrate the improvements made feasible by the developed methods.Moreover, surveys and interviews will be conducted with the end-users of the project methods to assess their satisfaction and the perceived usefulness of these methods. Feedback will be collected and used to improve the developed methodology.5. EffortsEfforts to develop, evaluate, and disseminate the developed methodology will include workshops and training sessions to educate on the project outputs and their potential benefits. Efforts will also be carried out through classroom and laboratory instruction to educate graduate and undergraduate students, the development of curriculum for course needs, and farm visits for experiential learning opportunities.In summary, the methods of the project employ a combination of advanced AI/ML techniques, real-time data analytics, and hardware-software co-design, such as the digital twin platform, to enhance dairy cattle health and productivity. The success of the project will be evaluated based on the improvements in metrics after the methods are deployed and the feedback from the end-users of the project outputs. This innovative approach has the potential to make significant contributions to dairy farming practices and outcomes.