How citizen scientists contribute to monitor protected areas thanks to automatic plant identification tools

Bonnet Pierre, Joly Alexis, Faton Jeaqn-Michel, Brown Susan, Kimiti David, Deneu Benjamin, Servajean Maximilien, Affouard Antoine, Lombardo Jean-Christophe, Mary Laura, Vignau Christel, Munoz François. 2020. How citizen scientists contribute to monitor protected areas thanks to automatic plant identification tools. Ecological Solutions and Evidence, 1 (2):e12023, 8 p.

Résumé : 1. Successful monitoring and management of plant resources worldwide needs the involvement of civil society to support natural reserve managers. Because it is difficult to correctly and quickly identify plant species for non‐specialists, the development of recent techniques based on automatic visual identification should facilitate and increase public engagement in citizen science initiatives. 2. Automatic identification platforms are new to most citizen scientists and land managers. Pl@ntNet is such a platform, available since 2013 on web and mobile environments, and now included in several workflows such as invasive alien species management, endemic species monitoring, educational activities and eco‐tourism practices. The successful development of such platforms needs to identify their strengths and weaknesses in order to improve and facilitate their use in all aspects of ecosystem management. 3. Here we present two Pl@ntNet citizen science initiatives used by conservation practitioners in Europe (France) and Africa (Kenya). We discuss various perspectives, including benefits and limitations. Based on the experiences of field managers, we formulate several recommendations for future initiatives. The recommendations are aimed at a diverse group of conservation managers and citizen science practitioners.

Mots-clés Agrovoc : détermination des espèces, identification, intelligence artificielle, participation publique, biodiversité, botanique, botaniste, surveillance de l'environnement, technologie de l'information environnementale

Mots-clés complémentaires : deep learning, science participative

Mots-clés libres : Artificial intelligence, Automatic plant identification, Citizen science, Deep learning technologies, Opportunistic data, Plant biodiversity monitoring, Public engagement, Volunteers

Classification Agris : F70 - Taxonomie végétale et phytogéographie
F40 - Écologie végétale
P01 - Conservation de la nature et ressources foncières
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) Co-designed Citizen Observatories Services for the EOS-Cloud

Auteurs et affiliations

• Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389 - auteur correspondant
• Joly Alexis, INRIA (FRA)
• Faton Jeaqn-Michel, Réserve naturelle nationale des Ramières du Val de Drôme (FRA)
• Brown Susan, Lewa House Pimbi Ltd. (KEN)
• Kimiti David, Lewa Wildlife Conservancy (KEN)
• Deneu Benjamin, CIRAD-BIOS-UMR AMAP (FRA)
• Servajean Maximilien, CNRS (FRA)
• Affouard Antoine, CIRAD-BIOS-UMR AMAP (FRA)
• Lombardo Jean-Christophe, INRIA (FRA)
• Mary Laura, Tela Botanica (FRA)
• Vignau Christel, Tela Botanica (FRA)
• Munoz François, Université Grenoble Alpes (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597008/)

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