Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Taneja, Mohit and Byabazaire, John and Jalodia, Nikita and Davy, Alan and Olariu, Cristian and Malone, Paul (2020) Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171. ISSN 0168-1699

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Abstract

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect 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. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach.

Item Type:	Article
Additional Information:	Funding Information: Nikita Jalodia is a PhD Researcher in the Department of Computing and Mathematics at the Emerging Networks Lab Research Unit in Telecommunications Software and Systems Group, Waterford Institute of Technology, Ireland. She is working as a part of the Science Foundation Ireland funded CONNECT Research Centre for Future Networks and Communications, and her research is based in Deep Learning and Neural Networks, NFV, Fog Computing and IoT. She received her Bachelor's Degree in Computer Science and Engineering from The LNM Institute of Information Technology, Jaipur, India in 2017, with an additional diploma specialization in Big Data and Analytics with IBM. Funding Information: The future work (MELD) is funded through the IoF2020 which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 731884 . Funding Information: 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 . Mohit Taneja is also supported by CISCO Research Gift Fund . Funding Information: 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. Mohit Taneja is also supported by CISCO Research Gift Fund. The future work (MELD) is funded through the IoF2020 which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 731884. Publisher Copyright: © 2020 The Authors
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/1100/1107
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Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:01
Last Modified:	15 Aug 2023 19:50
URI:	http://repository-testing.wit.ie/id/eprint/3607

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