Journal:Data management challenges for artificial intelligence in plant and agricultural research
| Full article title | Data management challenges for artificial intelligence in plant and agricultureal research |
|---|---|
| Journal | F1000Research |
| Author(s) | Williamson, Hugh F.; Brettschneider, Julia; Caccamo, Mario; Davey, Robert P.; Goble, Carole; Jersey, Paul J.; May, Sean; Morris, Richard J.; Ostler Richard |
| Author affiliation(s) | University of Exeter, University of Warwick, National Research Institute of Brewing, Earlham Institute, University of Manchester, Royal Botanic Gardens, University of Nottingham, John Innes Centre, Rothamsted Research, Alan Turing Institute, University of Edinburgh |
| Primary contact | S dot Leonelli at exeter dot ac dot uk |
| Year published | 2023 |
| Volume and issue | 10 |
| Article # | 324 |
| DOI | 10.12688/f1000research.52204.2 |
| ISSN | 2046-1402 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://f1000research.com/articles/10-324/v2 |
| Download | https://f1000research.com/articles/10-324/v2/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
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 visualize 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 (ML), which holds much promise for this domain.
Keywords: data science, plant science, crop science, agricultural research, machine learning, data management, data quality, data sharing
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This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar (including to the title). In some cases important information was missing from the references, and that information was added.
