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Data management challenges for artificial intelligence in plant and agricultural research.
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; Leonelli, Sabina.
Afiliação
  • Williamson HF; Exeter Centre for the Study of the Life Sciences & Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, UK.
  • Brettschneider J; Department of Statistics, University of Warwick, Coventry, UK.
  • Caccamo M; NIAB, National Research Institute of Brewing, East Malling, UK.
  • Davey RP; Earlham Institute, Norwich, UK.
  • Goble C; Department of Computer Science, University of Manchester, Manchester, UK.
  • Kersey PJ; Royal Botanic Gardens, Kew, UK.
  • May S; School of Biosciences, University of Nottingham, Loughborough, UK.
  • Morris RJ; John Innes Centre, Norwich, UK.
  • Ostler R; Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK.
  • Pridmore T; School of Computer Science, University of Nottingham, Nottingham, UK.
  • Rawlings C; Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK.
  • Studholme D; Biosciences, University of Exeter, Exeter, UK.
  • Tsaftaris SA; Institute of Digital Communications, University of Edinburgh, Edinburgh, UK.
  • Leonelli S; Alan Turing Institute, London, UK.
F1000Res ; 10: 324, 2021.
Article em En | MEDLINE | ID: mdl-36873457
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: F1000Res Ano de publicação: 2021 Tipo de documento: Article País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: F1000Res Ano de publicação: 2021 Tipo de documento: Article País de publicação: Reino Unido