Monitoring milking parameters to improve milking operations through machine learning algorithms
Contributo in Atti di convegno
Data di Pubblicazione:
2022
Citazione:
Monitoring milking parameters to improve milking operations through machine learning algorithms / J. Wang, D. Lovarelli, M. Guarino - In: Precision Livestock Farming 2022 / [a cura di] D Berckmans., M. Oczak, M. Iwersen, K Wagener. - [s.l] : Organizing Committee of the European Conference on Precision Livestock Farming, 2022. - ISBN 978-83-965360-0-6. - pp. 924-931 (( Intervento presentato al 10. convegno European Conference on Precision Livestock Farming : 29 August through 2 September tenutosi a Wien nel 2022.
Abstract:
The operation of milking is one of the most time-consuming in a dairy cattle farm.
Because the management and duration of the whole milking session can be affected by
some cows that need a longer milking time than others, it can be useful to shorten the
milking time of these cows. In this study, a full dataset of milking data was collected and
processed for three months from a dairy cattle farm located in Northern Italy. The aim
was to understand how to reduce the daily milking time by evaluating the effect of a
different pulsation ratio and detachment flow rate on the duration of milking and udder
health. A prediction model for the duration of milking was developed, which was able to
identify the proper pulsation ratio and detachment flow rate based on the first 2 minutes
of data on milking. If implemented on machines, it can lead to an automatization in the
change the pulsation ratio and detachment flow of every cow.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
data analysis; milk production; pulsation ratio; prediction model
Elenco autori:
J. Wang, D. Lovarelli, M. Guarino
Link alla scheda completa:
Titolo del libro:
Precision Livestock Farming 2022