Classifying Calf Behavior from Data of the Nedap SmartTag Ear with Self-Supervised Learning
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2022-05-13
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en
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Abstract
Calves are the future of a farm. They have to grow up to become dairy cows to make a profit for
the farmer. Unfortunately, health issues in calves can have a long-term effect on the calf’s welfare,
development, and future performance. To detect health issues in adult cattle, artificial intelligence
has been widely used, however, only a few sensors are available to be used for calves. Therefore,
this research will focus on behavior classification in calves, based on data from the Nedap SmartTag
Ear. Three different classes are recognized; Lying to predict diseases in calves, Ruminating to
predict the ideal weaning period, and Other for the remaining behaviors. A convolutional neural
network has been developed with fully-supervised learning to recognize the three classes, reaching
an overall F1 score of 0.826 ± 0.073. Because fully-supervised learning needs a lot of labeled data
and only limited labeled data is available, self-supervised learning (SSL) is used to incorporate
unlabeled data in the training of a model. The pretext task used for SSL is transformation
recognition with six transformations. Four SSL approaches have been compared and the approach
with a single-task learning classifier to recognize the transformations and two-step training with a
lambda value of 0.5 reaches the highest performance, with an overall F1 score of 0.817 ± 0.057,
which is slightly worse than the fully-supervised model. Both model types reach the highest F1
score for the Lying class and the lowest F1 score for the Ruminating class. Further experimentation
shows that with only 1% of the labeled data, the SSL model was still able to reach an overall F1
score of around 0.8, while the performance of the fully-supervised model only reached
approximately 0.6. This shows that there is potential in SSL, however, in the current situation SSL
is not able to outperform fully-supervised learning. Additionally, the models have been developed
based on 2 Hz data, while previous experiments are with 8 Hz data. This results in a performance
drop of 0.036 for the fully-supervised model and 0.107 for the SSL model. Overall, the results seem
promising, but further research on handling the data diversity between calves and increasing the
performance of the SSL model is required before the model can be used on farms.
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Faculteit der Sociale Wetenschappen