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Digital Phenotyping in Livestock Farming
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Version 1
: Received: 14 May 2021 / Approved: 14 May 2021 / Online: 14 May 2021 (14:08:40 CEST)
A peer-reviewed article of this Preprint also exists.
Neethirajan, S.; Kemp, B. Digital Phenotyping in Livestock Farming. Animals 2021, 11, 2009. https://doi.org/10.3390/ani11072009 Neethirajan, S.; Kemp, B. Digital Phenotyping in Livestock Farming. Animals 2021, 11, 2009. https://doi.org/10.3390/ani11072009
Abstract
Currently, large volumes of data are being collected on farms using multimodal sensor technol-ogies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored ef-ficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly ad-dressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future re-search is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.
Keywords
Digital Biomarkers; Digital Phenotyping; Wearables; Sensors; Livestock
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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