Agrusti, Miki
(2023)
Big Data approaches as a support for precision livestock farming techniques, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Salute, sicurezza e sistemi del verde, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10623.
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Abstract
With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.
Abstract
With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.
Tipologia del documento
Tesi di dottorato
Autore
Agrusti, Miki
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
35
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Big Data, Machine Learning, Precision Livestock Farming, Random Forest
URN:NBN
DOI
10.48676/unibo/amsdottorato/10623
Data di discussione
27 Marzo 2023
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Agrusti, Miki
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
35
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Big Data, Machine Learning, Precision Livestock Farming, Random Forest
URN:NBN
DOI
10.48676/unibo/amsdottorato/10623
Data di discussione
27 Marzo 2023
URI
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