Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries
Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
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dig-cirad-fr-5983792021-08-03T15:32:16Z http://agritrop.cirad.fr/598379/ http://agritrop.cirad.fr/598379/ Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries. Goeau Hervé, Bonnet Pierre, Joly Alexis. 2019. In : Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum. Cappellato Linda (ed.), Ferro Nicola (ed.), Losada David E. (ed.), Müller Henning (ed.). Aachen : CEUR-WS, 13 p. (CEUR Workshop Proceedings, 2380) Conference and Labs of the Evaluation Forum (CLEF 2019), Lugano, Suisse, 9 Septembre 2019/12 Septembre 2019.http://ceur-ws.org/Vol-2380/ <http://ceur-ws.org/Vol-2380/> Researchers Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries Goeau, Hervé Bonnet, Pierre Joly, Alexis eng 2019 CEUR-WS Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/598379/1/paper_247.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ http://ceur-ws.org/Vol-2380/ info:eu-repo/semantics/altIdentifier/purl/http://ceur-ws.org/Vol-2380/ |
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Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes. |
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Goeau, Hervé Bonnet, Pierre Joly, Alexis |
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Goeau, Hervé Bonnet, Pierre Joly, Alexis Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
author_facet |
Goeau, Hervé Bonnet, Pierre Joly, Alexis |
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Goeau, Hervé |
title |
Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
title_short |
Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
title_full |
Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
title_fullStr |
Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
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Overview of LifeCLEF plant identification task 2019: Diving into data deficient tropical countries |
title_sort |
overview of lifeclef plant identification task 2019: diving into data deficient tropical countries |
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CEUR-WS |
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http://agritrop.cirad.fr/598379/ http://agritrop.cirad.fr/598379/1/paper_247.pdf |
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AT goeauherve overviewoflifeclefplantidentificationtask2019divingintodatadeficienttropicalcountries AT bonnetpierre overviewoflifeclefplantidentificationtask2019divingintodatadeficienttropicalcountries AT jolyalexis overviewoflifeclefplantidentificationtask2019divingintodatadeficienttropicalcountries |
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