Beyond the hype : using AI, big data, wearable devices, and the Internet of Things for high-throughput livestock phenotyping

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dc.contributor.author Klingström, Tomas
dc.contributor.author König, Emelie Zonabend
dc.contributor.author Zwane, Avhashoni Agnes
dc.date.accessioned 2024-11-29T05:09:12Z
dc.date.available 2024-11-29T05:09:12Z
dc.date.issued 2024
dc.description.abstract Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of ‘Genomics without the genes’ as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as largescale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes. en_US
dc.description.department Biochemistry, Genetics and Microbiology (BGM) en_US
dc.description.sdg SDG-02:Zero Hunger en_US
dc.description.sdg SDG-09: Industry, innovation and infrastructure en_US
dc.description.sponsorship The Livestock Genetics Flagship of the Livestock CGIAR Research Program. Work was performed as part of a South Africa (NRF)/Sweden (STINT) science and technology research collaboration (SA2018-7728) with additional funding from Kunskapsnavet för jordbrukets digitalisering which is partially funded by the European Agricultural Fund for Rural Development. en_US
dc.description.uri https://academic.oup.com/bfg en_US
dc.identifier.citation Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane, Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping, Briefings in Functional Genomics, 2024;, elae032, https://doi.org/10.1093/bfgp/elae032. en_US
dc.identifier.issn 2041-2649 (print)
dc.identifier.issn 2041-2657 (online)
dc.identifier.other 10.1093/bfgp/elae032
dc.identifier.uri http://hdl.handle.net/2263/99681
dc.language.iso en en_US
dc.publisher Oxford University Press en_US
dc.rights © The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). en_US
dc.subject Phenotyping en_US
dc.subject Livestock en_US
dc.subject High-throughput en_US
dc.subject Artificial intelligence en_US
dc.subject Genetics en_US
dc.subject SDG-02: Zero hunger en_US
dc.subject SDG-09: Industry, innovation and infrastructure en_US
dc.subject Internet of Things (IoT) en_US
dc.title Beyond the hype : using AI, big data, wearable devices, and the Internet of Things for high-throughput livestock phenotyping en_US
dc.type Article en_US


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