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

Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus
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.
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement France
- Département Sciences sociales, agriculture et alimentation, espace et environnement France
- California Polytechnic State University United States
- Laboratoire Parole et Langage France
- French National Centre for Scientific Research France
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], natural history collections, [INFO] Computer Science [cs], phenology, Article, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], phenological shift, visual data classification, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], veterinary and food sciences, Machine Learning and Artificial Intelligence, [SDE.ES] Environmental Sciences/Environment and Society, [INFO]Computer Science [cs], [SDE.ES]Environmental Sciences/Environment and Society, regional convolutional neural network, 580, Agricultural, [INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], Botany, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], deep learning, 006, 600, object detection, flowering time, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Biological Sciences, [SDE.ES]Environmental Sciences/Environmental and Society, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, phenological stage annotation, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Biological sciences, climate change, QK1-989, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], natural history collections, [INFO] Computer Science [cs], phenology, Article, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], phenological shift, visual data classification, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], veterinary and food sciences, Machine Learning and Artificial Intelligence, [SDE.ES] Environmental Sciences/Environment and Society, [INFO]Computer Science [cs], [SDE.ES]Environmental Sciences/Environment and Society, regional convolutional neural network, 580, Agricultural, [INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], Botany, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], deep learning, 006, 600, object detection, flowering time, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Biological Sciences, [SDE.ES]Environmental Sciences/Environmental and Society, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, phenological stage annotation, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Biological sciences, climate change, QK1-989, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
