A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions ar...

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Autores principales:	Jonathon A. Gibbs, Lorna Mcausland, Carlos A. Robles-Zazueta, Erik H. Murchie, Alexandra J. Burgess
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep learning gsmax – maximum stomatal conductance high-throughput phenotyping semantic segmentation stomata Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/8561a7030a4143a7ac1c866c04c6c2ce
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id	oai:doaj.org-article:8561a7030a4143a7ac1c866c04c6c2ce
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spelling	oai:doaj.org-article:8561a7030a4143a7ac1c866c04c6c2ce2021-12-02T15:10:26ZA Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation1664-462X10.3389/fpls.2021.780180https://doaj.org/article/8561a7030a4143a7ac1c866c04c6c2ce2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.780180/fullhttps://doaj.org/toc/1664-462XStomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.Jonathon A. GibbsLorna McauslandCarlos A. Robles-ZazuetaErik H. MurchieAlexandra J. BurgessFrontiers Media S.A.articledeep learninggsmax – maximum stomatal conductancehigh-throughput phenotypingsemantic segmentationstomataPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning gsmax – maximum stomatal conductance high-throughput phenotyping semantic segmentation stomata Plant culture SB1-1110
spellingShingle	deep learning gsmax – maximum stomatal conductance high-throughput phenotyping semantic segmentation stomata Plant culture SB1-1110 Jonathon A. Gibbs Lorna Mcausland Carlos A. Robles-Zazueta Erik H. Murchie Alexandra J. Burgess A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
description	Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.
format	article
author	Jonathon A. Gibbs Lorna Mcausland Carlos A. Robles-Zazueta Erik H. Murchie Alexandra J. Burgess
author_facet	Jonathon A. Gibbs Lorna Mcausland Carlos A. Robles-Zazueta Erik H. Murchie Alexandra J. Burgess
author_sort	Jonathon A. Gibbs
title	A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
title_short	A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
title_full	A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
title_fullStr	A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
title_full_unstemmed	A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
title_sort	deep learning method for fully automatic stomatal morphometry and maximal conductance estimation
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/8561a7030a4143a7ac1c866c04c6c2ce
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A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

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