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Data management challenges for artificial intelligence in plant and agricultural research [version 2; peer review: 2 approved]
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Williamson, Hugh F., Brettschneider, Julia, Caccamo, Mario, Davey, Robert P., Goble, Carole, Kersey, Paul J., May, Sean, Morris, Richard J., Ostler, Richard, Pridmore, Tony, Rawlings, Chris, Studholme, David, Tsaftaris, Sotirios A. and Leonelli, Sabina (2023) Data management challenges for artificial intelligence in plant and agricultural research [version 2; peer review: 2 approved]. F1000Research, 10 . p. 324. doi:10.12688/f1000research.52204.2 ISSN 2046-1402.
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aca16cf3-f810-4074-979a-bb72995322dd_52204_-_sabina_leonelli.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1259Kb) | Preview |
Official URL: http://doi.org/10.12688/f1000research.52204.2
Abstract
Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.
Item Type: | Journal Article | ||||||||||||||||||||||||
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Subjects: | S Agriculture > S Agriculture (General) | ||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Artificial intelligence -- Agricultural applications, Agricultural innovations, Machine learning, Big data | ||||||||||||||||||||||||
Journal or Publication Title: | F1000Research | ||||||||||||||||||||||||
Publisher: | F1000 Research Ltd | ||||||||||||||||||||||||
ISSN: | 2046-1402 | ||||||||||||||||||||||||
Official Date: | 17 January 2023 | ||||||||||||||||||||||||
Dates: |
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Volume: | 10 | ||||||||||||||||||||||||
Page Range: | p. 324 | ||||||||||||||||||||||||
DOI: | 10.12688/f1000research.52204.2 | ||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||
Copyright Holders: | Copyright: © 2023 Williamson HF et al. | ||||||||||||||||||||||||
Date of first compliant deposit: | 14 December 2023 | ||||||||||||||||||||||||
Date of first compliant Open Access: | 15 December 2023 | ||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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