CoolDB theme - Collaborative Data Collection, Management and Dissemination in Ecology and Botany

Description

Background

The collection, management, validation and analysis of botanical data, which are often complex and heterogeneous in nature, play a crucial role in many research activities and their applications. Until very recently, the modalities for aggregating these data were based either (i) on individual solutions and/or small groups of experts, or (ii) on highly integrative solutions, within large centralized and static institutional infrastructures, in which the produced data were difficult to reuse for other research work. The scientific data currently produced in ecology, taxonomy and botany are by their very complex and diversified multimodal nature, and are becoming increasingly voluminous and fast. New research challenges require increasingly integrative approaches that require the development of new forms of data aggregation, management and exploitation.

Objectives

The research addressed under this theme aims to facilitate the generation and exploitation of new collections of data, particularly visual, ecological and botanical data, through the development of innovative methods and tools for access to information for both the academic research community and the general public. In this context, we are interested (in a non-exhaustive way) in (i) the taxonomic identification of living organisms, based on collaborative and/or automated approaches, (ii) the modelling of the geographical distribution of plants, (iii) or the implementation of recommendation services based on the location of users or the taking into account of spatial constraints.

Expected Results

  •  Publication of new methods for large-scale aggregation and analysis of botanical data;
  •  Development of software infrastructures to facilitate the exploration, collection, management, validation and dissemination of large volumes of botanical data;
  •  Dissemination of software and establishment of collaboration networks with various research and technical institutes.

Significant events

  •  Special Jury Prize from La Recherche magazine for the article "A look inside the Pl@ntNet experience" published in Multimedia Systems (Joly et al. 2016).
  •  Presentation in the August 2017 Nature magazine News of the article on the use of Pl@ntNet for the identification of herbarium samples published in BMC Evolutionary Biology (Carranza et al. 2016).

Main funded projects

Acronym Title Duration
GUYAPATUR IIGuyapatur II2020 - 2021
MIKOKOMikoko: Conservation and resilience of Kenya’s mangrove forests2019 - 2021
WEEDELECRobot de désherbage localisé par procédé électrique haute tension combiné avec une gestion prédictive par vision hyper-spectrale et post-évaluation par drone2018 - 2021
FLORIS'TICFloris'tic2015 - 2018
ARCHIWOODMorphologie végétale, anatomie et architecture des espèces endémiques de bois à Madagascar2014 - 2016
E-RECOLNATValorisation de 350 ans de collections d'histoire naturelle : une plateforme numérique pour l'environnement et la société2013 - 2019
WIKWIOWeed Identification and Knowledge in the Western Indian Ocean2013 - 2016
PL@NTNETObservation et identification interactive des plantes2009 - 2013

Theses in progress

  •  Botella C. 2016-2019- Statistical methods for modelling habitats of plant species using large amounts of attendance data alone from citizen science programmes. Application to invasive alien species in the French Mediterranean region. ED I2S, Univ. Montpellier (Co-Director A. Joly, P. Monestiez, F. Munoz, INRA-INRIA Allocation).
  •  Justeau, D. 2017-2020 - Spatial modelling of ecosystems and resolution of the reserve selection problem: A global and flexible approach. ED Gaia, Univ. Montpellier (Co-director P. Birnbaum & X. Lorca; CIRAD thesis grant).
  •  Conde-Salazar R. 2017-2020. Towards an ontological approach for the management of databases in Agroforestry. ED I2S, Montpellier University (Co-Director. A. Stokes & I. Mougenot, Fondation de France doctoral grant).
Main scientific production 2019-2020
Publications HAL de la collection AMAP

2020

Journal articles

ref_biblio
Christophe Botella, Alexis Joly, Pascal Monestiez, Pierre Bonnet, François Munoz. Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection. PLoS ONE, Public Library of Science, 2020, 15 (5), pp.e0232078. ⟨10.1371/journal.pone.0232078⟩. ⟨hal-02639237⟩
Accès au texte intégral et bibtex
https://hal.archives-ouvertes.fr/hal-02639237/file/Botella_etal_PlosOne_2020_15_5.pdf BibTex
ref_biblio
Julien Champ, Adán Mora‐fallas, Hervé Goëau, Erick Mata‐montero, Pierre Bonnet, et al.. Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots. Applications in Plant Sciences, Wiley, 2020, 8 (7), ⟨10.1002/aps3.11373⟩. ⟨hal-02910844⟩
Accès au texte intégral et bibtex
https://hal.inrae.fr/hal-02910844/file/Bonnet%20etal_Appl_Plant_Sci_2020_8_7.pdf BibTex
ref_biblio
Charles Davis, Julien Champ, Daniel Park, Ian Breckheimer, Goia Lyra, et al.. A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN. Frontiers in Plant Science, Frontiers, 2020, 11, ⟨10.3389/fpls.2020.01129⟩. ⟨hal-02909794⟩
Accès au texte intégral et bibtex
https://hal.inrae.fr/hal-02909794/file/fpls-11-01129-1-1.pdf BibTex
ref_biblio
Hervé Goëau, Adán Mora‐fallas, Julien Champ, Natalie L. Rossington Love, Susan Mazer, et al.. A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction. Applications in Plant Sciences, Wiley, 2020, 8 (6), pp.#e11368. ⟨10.1002/aps3.11368⟩. ⟨hal-02894994⟩
Accès au texte intégral et bibtex
https://hal.inrae.fr/hal-02894994/file/aps3.11368.pdf BibTex
ref_biblio
Sue Han Lee, Hervé Goëau, Pierre Bonnet, Alexis Joly. New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, Elsevier, 2020, 170, pp.105220. ⟨10.1016/j.compag.2020.105220⟩. ⟨hal-02470280⟩
Accès au texte intégral et bibtex
https://hal.umontpellier.fr/hal-02470280/file/1-s2.0-S0168169919300560-main.pdf BibTex
ref_biblio
Katelin Pearson, Gil Nelson, Myla Aronson, Pierre Bonnet, Laura Brenskelle, et al.. Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research. Bioscience, Oxford University Press (OUP), 2020, 70 (7), pp.610-620. ⟨10.1093/biosci/biaa044⟩. ⟨hal-02573627⟩
Accès au texte intégral et bibtex
https://hal.umontpellier.fr/hal-02573627/file/biaa044.pdf BibTex

2019

Journal articles

ref_biblio
Stéphanie Bodin, Rita Scheel-Ybert, Jacques Beauchene, Jean-François Molino, Laurent Bremond. CharKey: An electronic identification key for wood charcoals of French Guiana. IAWA Journal, Brill publishers, 2019, 40 (1), pp.75-S20. ⟨10.1163/22941932-40190227⟩. ⟨hal-02098432⟩
Accès au bibtex
BibTex
ref_biblio
Titouan Lorieul, Katelin Pearson, Elizabeth Ellwood, Hervé Goëau, Jean‐francois Molino, et al.. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences, Wiley, 2019, 7 (3), pp.e01233. ⟨10.1002/aps3.1233⟩. ⟨hal-02137748⟩
Accès au texte intégral et bibtex
https://hal.umontpellier.fr/hal-02137748/file/Lorieul_et_al-2019-Applications_in_Plant_Sciences.pdf BibTex

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