Difference between revisions of "Journal:Data management challenges for artificial intelligence in plant and agricultural research"
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==Introduction== | ==Introduction== | ||
Data science is central to the development of plant and agricultural [[research]] and its application to social and environmental problems of a global scale, such as [[food security]], biodiversity, and climate change. [[Artificial intelligence]] (AI) offers great potential towards elucidating and managing the complexity of biological data, organisms, and systems. It constitutes a particularly promising approach for the plant sciences, which are marked by the distinctive challenge of understanding not only complex [[Genotyping|genotype]]-environment (GxE) interactions that span multiple scales from the cellular through the microbiome to climate systems, but also GxE interactions with rapidly shifting human management practices (GxExM) in agricultural and other settings, whose reliance on digital innovations is growing at a rapid pace. [Wang et al. 2020; Harfouche et al. 2019] Accordingly, examples of useful applications of AI—and particularly [[machine learning]] (ML)—to plant science contexts are increasing, with the [[COVID-19]] [[pandemic]] crisis further accelerating interest in this approach. [King 2020] | |||
Nevertheless, we are still far from a research landscape in which AI can be routinely and effectively implemented. A key obstacle concerns the development and implementation of effective and reliable [[Information managment|data management]] strategies. Developing reliable and reproducible AI applications depends on having validated, meaningful, and usable ways to integrate large, multi-dimensional datasets from different sources and scientific approaches. This is especially relevant to the development of novel food and agricultural technologies, which rely on research from diverse fields including fundamental plant biology, crop research, conservation science, soil science, plant pathology, pest/pollinator ecology and management, water and land management, climate modelling, agronomy, and economics. | |||
This paper explores data-related challenges to potential applications of AI in plant science, with particular attention paid to the analysis of GxExM interactions of relevance to crop science and agricultural implementations. It brings together the experiences of an interdisciplinary set of researchers from the [[Botany|plant]] and agricultural sciences, the engineering and computational sciences, and the social studies of science, all of whom are working with complex datasets spanning [[Genomics|genomic]], physiological, and environmental data and computational methods of analysis. The first part of the paper provides a brief overview of contemporary AI and data science applications within plant science, with particular attention paid to the UK and European landscape where the authors are based. The second part identifies and discusses eight challenges in data management that must be addressed to further unlock the potential of AI for plant science and agronomic research. We conclude with a reflection on how transdisciplinary and international collaborations on data management can foster impactful and socially responsible AI in this domain. | |||
==AI in plant research: Current status and challenges== | |||
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==Notes== | ==Notes== | ||
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. | 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. The original article lists references in alphabetical order; this version lists them in order of appearance, by design. | ||
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Revision as of 22:43, 26 January 2024
| 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) |
 |
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
Introduction
Data science is central to the development of plant and agricultural research and its application to social and environmental problems of a global scale, such as food security, biodiversity, and climate change. Artificial intelligence (AI) offers great potential towards elucidating and managing the complexity of biological data, organisms, and systems. It constitutes a particularly promising approach for the plant sciences, which are marked by the distinctive challenge of understanding not only complex genotype-environment (GxE) interactions that span multiple scales from the cellular through the microbiome to climate systems, but also GxE interactions with rapidly shifting human management practices (GxExM) in agricultural and other settings, whose reliance on digital innovations is growing at a rapid pace. [Wang et al. 2020; Harfouche et al. 2019] Accordingly, examples of useful applications of AI—and particularly machine learning (ML)—to plant science contexts are increasing, with the COVID-19 pandemic crisis further accelerating interest in this approach. [King 2020]
Nevertheless, we are still far from a research landscape in which AI can be routinely and effectively implemented. A key obstacle concerns the development and implementation of effective and reliable data management strategies. Developing reliable and reproducible AI applications depends on having validated, meaningful, and usable ways to integrate large, multi-dimensional datasets from different sources and scientific approaches. This is especially relevant to the development of novel food and agricultural technologies, which rely on research from diverse fields including fundamental plant biology, crop research, conservation science, soil science, plant pathology, pest/pollinator ecology and management, water and land management, climate modelling, agronomy, and economics.
This paper explores data-related challenges to potential applications of AI in plant science, with particular attention paid to the analysis of GxExM interactions of relevance to crop science and agricultural implementations. It brings together the experiences of an interdisciplinary set of researchers from the plant and agricultural sciences, the engineering and computational sciences, and the social studies of science, all of whom are working with complex datasets spanning genomic, physiological, and environmental data and computational methods of analysis. The first part of the paper provides a brief overview of contemporary AI and data science applications within plant science, with particular attention paid to the UK and European landscape where the authors are based. The second part identifies and discusses eight challenges in data management that must be addressed to further unlock the potential of AI for plant science and agronomic research. We conclude with a reflection on how transdisciplinary and international collaborations on data management can foster impactful and socially responsible AI in this domain.
AI in plant research: Current status and challenges
References
Notes
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. The original article lists references in alphabetical order; this version lists them in order of appearance, by design.
