A decision support system for herd health management for dairy farms

Jan Saro, Tomáš Šubrt, Helena Brožová, Robert Hlavatý, Jan Rydval, Jaromír Ducháček and Luděk Stádník
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Jan Saro: Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic
Tomáš Šubrt: Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic
Helena Brožová: Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic
Robert Hlavatý: Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic
Jan Rydval: Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic
Jaromír Ducháček: Department of Animal Husbandry, Faculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences Prague, Czech Republic
Luděk Stádník: Department of Animal Husbandry, Faculty of Agrobiology, Food, and Natural Resources, Czech University of Life Sciences Prague, Czech Republic

Czech Journal of Animal Science, 2024, vol. 69, issue 12, 502-515

Abstract: Industrial dairy farms boast highly advanced health monitoring and disease diagnosis systems. But without easily accessible, user-friendly web platforms for real-time decision-making, most dairy farmers cannot proactively manage herd health management and optimize treatments based on disease prediction and prevention. To bridge this gap, we have developed a web application of a Decision support system (DSS) for dairy health management based on machine learning. The system architecture combines a Flask backend with a React frontend and scalable cloud data storage and includes preprocessing, data integration, predictive modelling, and cost analysis. DSS forecasts herd diseases with an accuracy 6.66 mean absolute error and 2.35 median absolute deviation across predictions. Its core predictive capabilities rely on long short-term memory (LSTM) neural networks to forecast disease progression from historical records and on a linear trend model to project cuts in treatment costs. The system calculates medication dosages and cost per disease, streamlines supplier selection, and simulates various treatment scenarios, thereby identifying high-cost diseases with potential savings. In other words, this DSS application processes disease and treatment data by incorporating veterinary records into advanced data analytics and neural networks, thereby predicting diseases, optimizing disease prevention and treatment strategies, and reducing costs. As such, this DSS application provides dairy farmers with a tool for strategic decision-making, veterinary treatment planning, and cost-effective disease management towards improving animal welfare and increasing milk yield.

Keywords: dairy cows; disease monitoring; neural networks; predictive analysis; treatment optimisation; web applications (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjs:v:69:y:2024:i:12:id:178-2024-cjas

DOI: 10.17221/178/2024-CJAS

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