Data management challenges for artificial intelligence in plant and agricultural research
Hugh F. Williamson, Julia Brettschneider, Mario Cáccamo, Robert Davey, Carole Goble, Paul Kersey, Sean May, Richard J. Morris, Richard Ostler, Tony Pridmore, Chris Rawlings, David J. Studholme, Sotirios A. Tsaftaris, Sabina Leonelli
Abstract
<ns4:p>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.</ns4:p>