Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb...

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Autores principales:	Natalie L. R. Love, Pierre Bonnet, Hervé Goëau, Alexis Joly, Susan J. Mazer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	regional convolutional neural network object detection deep learning visual data classification climate change natural history collections Botany QK1-989
Acceso en línea:	https://doaj.org/article/74bc584fbc4140d7a554f9f0788e9d00
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spelling	oai:doaj.org-article:74bc584fbc4140d7a554f9f0788e9d002021-11-25T18:46:51ZMachine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>10.3390/plants101124712223-7747https://doaj.org/article/74bc584fbc4140d7a554f9f0788e9d002021-11-01T00:00:00Zhttps://www.mdpi.com/2223-7747/10/11/2471https://doaj.org/toc/2223-7747Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, <i>Streptanthus tortuosus,</i> were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.Natalie L. R. LovePierre BonnetHervé GoëauAlexis JolySusan J. MazerMDPI AGarticleregional convolutional neural networkobject detectiondeep learningvisual data classificationclimate changenatural history collectionsBotanyQK1-989ENPlants, Vol 10, Iss 2471, p 2471 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	regional convolutional neural network object detection deep learning visual data classification climate change natural history collections Botany QK1-989
spellingShingle	regional convolutional neural network object detection deep learning visual data classification climate change natural history collections Botany QK1-989 Natalie L. R. Love Pierre Bonnet Hervé Goëau Alexis Joly Susan J. Mazer Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
description	Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, <i>Streptanthus tortuosus,</i> were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.
format	article
author	Natalie L. R. Love Pierre Bonnet Hervé Goëau Alexis Joly Susan J. Mazer
author_facet	Natalie L. R. Love Pierre Bonnet Hervé Goëau Alexis Joly Susan J. Mazer
author_sort	Natalie L. R. Love
title	Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
title_short	Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
title_full	Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
title_fullStr	Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
title_full_unstemmed	Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>
title_sort	machine learning undercounts reproductive organs on herbarium specimens but accurately derives their quantitative phenological status: a case study of <i>streptanthus tortuosus</i>
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/74bc584fbc4140d7a554f9f0788e9d00
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Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of <i>Streptanthus tortuosus</i>

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