Back to Search Start Over

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

Authors :
Natalie L. R. Love
Pierre Bonnet
Hervé Goëau
Alexis Joly
Susan J. Mazer
Source :
Plants, Vol 10, Iss 11, p 2471 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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, Streptanthus tortuosus, 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.

Details

Language :
English
ISSN :
22237747
Volume :
10
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Plants
Publication Type :
Academic Journal
Accession number :
edsdoj.74bc584fbc4140d7a554f9f0788e9d00
Document Type :
article
Full Text :
https://doi.org/10.3390/plants10112471