期刊
ECOLOGICAL SOLUTIONS AND EVIDENCE
卷 1, 期 2, 页码 -出版社
WILEY
DOI: 10.1002/2688-8319.12023
关键词
artificial intelligence; automatic plant identification; citizen science; deep learning technologies; opportunistic data; plant biodiversity monitoring; public engagement; volunteers; intelligence artificielle; identification automatique des plantes; science citoyenne; technologies d'apprentissage profond; donnees opportunistes; surveillance de la biodiversite vegetale; engagement du public; volontaires
类别
资金
- European Union's Horizon 2020 Research and Innovation Programme [863463]
- Agropolis Fondation
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.
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