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Advisor(s)
Abstract(s)
The autonomous identification of animal births has a significant added value, since it
enables for a prompt timely human intervention in the process, protecting the young and the mothers’
health, without requiring continuous human surveillance. Wearable inertial sensors have been
employed for a variety of animal monitoring applications, thanks to their low cost and the fact that
they allow less invasive monitoring process. Alarms triggered by the occurrence of events must
be generated close to the events to avoid delays caused by communication latency, which is why
this type of mechanism is typically implemented at the network’s edge and integrated with existing
auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried
out commercially for some years, there is no solution for small ruminants, especially goats, where
the literature does not even report any attempts. The current work consisted of a first attempt at
developing an automatic birth monitor using inertial sensing, as well as detection techniques based on
Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus,
two concept drift detection techniques and seven kidding detection mechanisms were developed
using data classification models. The work also includes the testing and comparison of learning
results, both in terms of accuracy and of computational costs of the detection module, for algorithms
implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow
kidding detection, whereas classification-algorithm-based static learning models do, despite the
unbalanced character of the dataset and its reduced size. The learning findings are quite promising
in terms of computational cost and its suitability for deployment on edge devices. The algorithm
demonstrates behavior changes four hours before kidding and allows for the identification of the
kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process
with a larger dataset
Description
Keywords
goat kidding detection inertial sensors stream learning concept drift edge computing precision livestock farming