Automatic Body Condition Scoring on Dairy Cows of the Swedish red breed
The objective of this MSc thesis was to investigate parameters reported to be associated with dairy cows’ body condition and its changes. A valid reference of a dairy cow’s body condition was to be suggested and investigated for suitability in the development of a 3D imaging based automatic body...
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| Formato: | Second cycle, A2E |
| Lenguaje: | sueco Inglés |
| Publicado: |
2016
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| Acceso en línea: | https://stud.epsilon.slu.se/9038/ |
| Sumario: | The objective of this MSc thesis was to investigate parameters reported to be associated
with dairy cows’ body condition and its changes. A valid reference of a dairy cow’s body
condition was to be suggested and investigated for suitability in the development of a 3D
imaging based automatic body condition scoring model. Furthermore, it was important to
accumulate data to use in training (adjustment of the algorithm mathematics) of the 3D
imaging based automatic body condition scoring model. The study included 21 dairy cows of
the Swedish Red breed from the herd at the Kungsängen Research Centre in Uppsala. The
cows had access to an exercise pen and were fed silage and concentrates indoors according to
the Swedish feeding recommendations, based on individual milk yields. Data was collected
weekly from May to August 2009 and included live weight, manual body condition score,
backfat thickness, 3D images, milk yield, content of fat, protein and lactose in milk and the
plasma metabolites non esterified fatty acids and β-hydroxybutyrate. Data was analysed by
linear correlation and regression analysis. Of all individually investigated collected
parameters, backfat thickness was found to have the highest correlation with manual body
condition scores and this parameter was therefore suggested and used as an alternative true
reference of body condition in the training of the 3D imaging based automatic body condition
scoring model. Results were promising and it was concluded that it is possible to train and
calibrate the 3D imaging based automatic body condition scoring model both with manual
body condition scores and with backfat thickness as reference, to predict the dairy cow’s body
condition. The advantage in using backfat thickness would be that it is a more objective
measure and that it gives continuous data instead of the categorical data obtained from manual
body condition scoring. If sufficient sensitivity is obtained in the automatic body condition
scoring model it could alert the farmer to changes in the cow’s body condition, instead of only
recording the body condition after a change. Future studies should focus on developing this
function in the automatic body condition scoring models since it would add significant value
to dairy management systems. |
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