A machine learning framework to predict the next month's daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. (April 2022)

• Record Type: Journal Article
• Authors: Ji, Boyu; Banhazi, Thomas; Phillips, Clive J.C.; Wang, Chaoyuan; Li, Baoming
• Is Part Of: Biosystems engineering. Volume 216(2022)
• Page Start: 186
• Page End: 197
• Publication Date: 2022-04
• Subjects: Robotic dairy farm -- Dairy cow -- Machine learning -- Thermal comfort index; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2022.02.013
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Robotic milking systems (RMS) are increasingly utilised by modern livestock farmers because they can reduce labour costs, and they have the potential to collect data that will improve animal welfare and animal productivity through better monitoring. Sensors and devices installed in RMS enable farmers to routinely collect data on environment conditions, individual animal's behaviours, health, productivity, and milk quality. This dataset can be used to train artificial intelligence algorithms to predict trends in these variables. This study developed a machine learning framework using 5 years' behaviour, heath and productivity data from 80 cows in a robotic dairy farm. Here we demonstrate the development of a framework to automatically train models with up-to-date farm data and predict daily milk yield, composition (fat and protein content) and frequency of individual cow milking during the subsequent 28 days. A time series cross-validation was applied to simulate the application of this framework under commercial conditions and to evaluate the performance. A high accuracy of prediction (R 2 > 0.90 and overall accuracy > 80%) was achieved with the models created by this framework. The practical potential of using such frameworks to enhance the management efficiency and animal welfare in robotic dairy farms is discussed. Highlights: A machine learning framework was developed to predict cow production performance. Models were trained using 5 years' data collected from robotic milking system. Prediction of individual cow production performance got an accuracy >80% (R 2 > 0.90).