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.
Highlights
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 promising approach for the plant sciences, which are marked by the distinctive challenge of understanding complex gene-environment (GxE) interactions that span multiple scales from the cellular through the microbiome to climate systems, and their interaction with rapidly shifting human management practices (GxExM) in agricultural and other settings, whose reliance on digital innovations is growing at a fast pace (Wang et al 2020; Harfouche et al 2019)
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
Summary
Data management challenges for artificial intelligence in plant and agricultural research [version 1; peer review: 1 approved with reservations].
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