Evaluating the contribution of behavioral, milking system, and environmental data to short-term milk yield prediction in commercial dairy cows using machine learning
Year: 2025
Type: article
Abstract: in DMY-informed models, they significantly reduced prediction error. These findings highlight the importance of behavioral and environmental data in milk yield prediction, particularly when historical milk yield records are unavailable. Integrating sensor-based monitoring can enhance precision dairy strategies and improve forecasting accuracy.
Source: Journal of Dairy Science
Authors: J. Hooker, B.B. de Medeiros, C. Saha, T. Abdulrahman, A.A.C. Alves
Institution: University of Georgia
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,500
Funder: University of Georgia
OpenAlex ID: W4413270759