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.
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