Biodiversity Information Science and Standards : Conference Abstract
|
Corresponding author: Adán Mora-Fallas (adamora@ic-itcr.ac.cr), Hervé H.G. Goëau (herve.goeau@cirad.fr), Pierre Bonnet (pierre.bonnet@cirad.fr), Alexis A.J. Joly (alexis.joly@inria.fr)
Received: 14 Jun 2019 | Published: 26 Jun 2019
© 2019 Adán Mora-Fallas, Hervé Goëau, Susan Mazer, Natalie Love, Erick Mata-Montero, Pierre Bonnet, Alexis Joly
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Mora-Fallas A, Goëau H, Mazer S, Love N, Mata-Montero E, Bonnet P, Joly A (2019) Accelerating the Automated Detection, Counting and Measurements of Reproductive Organs in Herbarium Collections in the Era of Deep Learning. Biodiversity Information Science and Standards 3: e37341. https://doi.org/10.3897/biss.3.37341
|
Millions of herbarium records provide an invaluable legacy and knowledge of the spatial and temporal distributions of plants over centuries across all continents (
Recent progress on instance segmentation demonstrated by the Mask-RCNN method is very promising in the context of herbarium sheets, in particular for detecting with high precision different organs of interest on each specimen, including leaves, flowers, and fruits. However, like any deep learning approach, this method requires a significant number of labeled examples with fairly detailed outlines of individual organs. Creating such a training dataset can be very time-consuming and may be discouraging for researchers. We propose in this work to integrate the Mask-RCNN approach within a global system enabling an active learning mechanism (
We discuss experiments addressing the effectiveness, the limits and the time required of our approach for annotation, in the context of a phenological study of more than 10,000 reproductive organs (buds, flowers, fruits and immature fruits) of Streptanthus tortuosus, a species known to be highly variable in appearance and therefore very difficult to be processed by an instance segmentation deep learning model.
Herbarium collection, phenology, phenophase, deep learning, instance detection, active learning, visual annotation
Hervé Goëau
Biodiversity_Next 2019