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Pl@ntNet, ten years of automatic plant identification and monitoring

Alexis Joly 1, 2 Antoine Affouard 1 Mathias Chouet 2, 1 Benjamin Deneu 1 Joaquim Estopinan 2 Hervé Goëau 1, 3 Hugo Gresse 2, 1 Jean-Christophe Lombardo 2 Titouan Lorieul 2 François Munoz 2 Maximilien Servajean 4 Pierre Bonnet 1, 3 
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
4 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : AI-based models for IUCN conservation status prediction (see Fig 1) Digitized Herbarium analysis (phenology, traits, identification) Plant disease recognition Agro-ecological robots (weeds detection and identification, mixed seeds) Biodiversity data quality and uncertainty New AI-based services for citizen science (cos4cloud project)
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https://hal.inrae.fr/hal-03343235
Contributor : Yannick Brohard Connect in order to contact the contributor
Submitted on : Tuesday, September 14, 2021 - 9:39:34 AM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
Long-term archiving on: : Wednesday, December 15, 2021 - 6:12:04 PM

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  • HAL Id : hal-03343235, version 1

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Alexis Joly, Antoine Affouard, Mathias Chouet, Benjamin Deneu, Joaquim Estopinan, et al.. Pl@ntNet, ten years of automatic plant identification and monitoring. IUCN - Congrès mondial de la nature, IUCN, Sep 2021, Marseille, France. ⟨hal-03343235⟩

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