Lameness Detection as a Service : Application of Machine Learning to an Internet of Cattle

Byabazaire, John and Olariu, Cristian and Taneja, Mohit and Davy, Alan (2019) Lameness Detection as a Service : Application of Machine Learning to an Internet of Cattle. In: 2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019 :. 2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019 . Institute of Electrical and Electronics Engineers Inc., USA. ISBN 9781538655535

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Abstract

Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g., step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer's mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness.

Item Type:	Book Section
Additional Information:	Funding Information: Our results showed that with a custom model for a small group of animals, we were able to reduce the classification error of the LDA by 8% as opposed to a one-size fits all approach. The solution is also environment and weather agnostic. In our future work we intend to investigate a more robust clustering technique as the current one is only based on threshold. Also evaluate the other cluster models ACKNOWLEDGMENT This work has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/2200/2208
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Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:15
Last Modified:	31 Jul 2023 22:25
URI:	http://repository-testing.wit.ie/id/eprint/4935

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