Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images

Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents a...

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Bibliographic Details
Main Authors: Rachel A. Reeb, Naeem Aziz, Samuel M. Lapp, Justin Kitzes, J. Mason Heberling, Sara E. Kuebbing
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-01-01
Series:Frontiers in Plant Science
Subjects:
phenology
deep learning
citizen science
iNaturalist
Alliaria petiolata (garlic mustard)
convolutional neural network
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2021.787407/full

Internet

https://www.frontiersin.org/articles/10.3389/fpls.2021.787407/full

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