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Application of deep learning for the analysis of stomata: a review of current methods and future directions

Gibbs, Jonathon A.; Burgess, Alexandra J.

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ALEXANDRA GIBBS Alexandra.Gibbs1@nottingham.ac.uk
Assistant Professor in Agriculture and The Environment



Contributors

Tracy Lawson
Editor

Abstract

Plant physiology and metabolism relies on the function of stomata, structures on the surface of above ground organs, which facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore which make up the stomata, as well as the density (number per unit area) are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopes, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used; from data acquisition, pre-processing, DL architecture and output evaluation to post processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimise uptake; predominantly focusing on the sharing of datasets and generalisation of models as well as the caveats associated with utilising image data to infer physiological function.

Journal Article Type Article
Acceptance Date May 3, 2024
Online Publication Date May 8, 2024
Publication Date May 8, 2024
Deposit Date May 14, 2024
Publicly Available Date May 16, 2024
Journal Journal of Experimental Botany
Print ISSN 0022-0957
Electronic ISSN 1460-2431
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1093/jxb/erae207
Public URL https://nottingham-repository.worktribe.com/output/34858893
Publisher URL https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erae207/7666955

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