Descrição
Pl@ntNet is a participatory botanical observation platform allowing to identify plants from photos (using deep learning) and to share the observations with the community. The platform has three main front-ends: Pl@ntNet androïd (http://bit.ly/1K4D1eU), Pl@ntNet iOS (http://apple.co/2cMtWgu) and Pl@ntNet web (https://identify.plantnet.org/). Pl@ntNet was founded in 2010 by a consortium of four French research organisms (CIRAD, Inria, INRAE and IRD) and is now open to other members. More information about Pl@ntNet can be found at https://plantnet.org/. The occurrences in this collection are Pl@ntNet observations that have been identified only by the deep learning algorithm but which the algorithm confidence was sufficiently high to consider them as valid.
Registros de Dados
Os dados deste recurso de ocorrência foram publicados como um Darwin Core Archive (DwC-A), que é o formato padronizado para compartilhamento de dados de biodiversidade como um conjunto de uma ou mais tabelas de dados. A tabela de dados do núcleo contém 12.142.287 registros.
This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.
Versões
A tabela abaixo mostra apenas versões de recursos que são publicamente acessíveis.
Como citar
Pesquisadores deveriam citar esta obra da seguinte maneira:
AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BOTELLA C, BONNET P (2023): Pl@ntNet automatically identified occurrences. v1.8. Pl@ntNet. Dataset/Occurrence. https://ipt.plantnet.org/resource?r=queries&v=1.8
Direitos
Pesquisadores devem respeitar a seguinte declaração de direitos:
O editor e o detentor dos direitos deste trabalho é Pl@ntNet. This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.
GBIF Registration
Este recurso foi registrado no GBIF e atribuído ao seguinte GBIF UUID: 14d5676a-2c54-4f94-9023-1e8dcd822aa0. Pl@ntNet publica este recurso, e está registrado no GBIF como um publicador de dados aprovado por GBIF France.
Palavras-chave
Occurrence; Observation; Occurrence
Contatos
Quem criou esse recurso:
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https://orcid.org/0000-0002-2161-9940
Quem pode responder a perguntas sobre o recurso:
Quem preencher os metadados:
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Quem mais foi associado com o recurso:
Cobertura Geográfica
Plant observations from Pl@ntNet users come from all around the world.
| Coordenadas delimitadoras | Sul Oeste [-90, -180], Norte Leste [90, 180] |
|---|
Cobertura Taxonômica
Pl@ntNet observations focus on plants.
| Reino | Plantae (Plant) |
|---|
Dados Sobre o Projeto
PlantNet is a participatory botanical observation platform allowing to identify plants from photos (using deep learning) and share observations with the community. This resource contains occurrences of plants automatically inferred from the plant observations submitted by the users of PlantNet application.
| Título | Pl@ntNet Queries |
|---|---|
| Identificador | queries |
| Financiamento | PlantNet is an open consortium founded by four French research organizations (CIRAD, Inria, INRAE, IRD) and supported by Agropolis Fondation. The two main funding resources are: (i) the annual contribution of the members of the consortium, (ii) donations from the end-users of PlantNet application (>10 million users). |
| Descrição da Área de Estudo | Entire world |
O pessoal envolvido no projeto:
Métodos de Amostragem
No sampling protocol, opportunistic observations by Pl@ntNet users.
| Área de Estudo | Entire world, Plantae. |
|---|---|
| Controle de Qualidade | The validation is based on two main criteria: - the output of the automated identification algorithm is greater than a threshold (more precisely the top-1 probability output by the convolutional neural network is greater than 0.9) - the species name matches the checklist considered as the most trusted one for the country where the observation was done |
Descrição dos passos do método:
- This collection contains occurrences of plants automatically identified from the observations submitted by Pl@ntNet users to identify them (using one of the three applications: androïd, iOS, web, more information here: https://plantnet.org/). The following filters were applied: - is geolocated - is valid (top-1 softmax output > 0.9) - from a user with an enabled account or from an anonymous user - with a known species name (valid or synonym) in PN - species name != Cannabis - date_query > 0 - remove shared queries (already present in observation dataset) - remove duplicate session (keep the most recent query based on the session number) - must be in one of the WGSRPD polygon level 3 and the binomial species name (without author) must match a species of the corresponding Kew checklist
Citações bibliográficas
- Joly, A., Goëau, H., Bonnet, P., Bakić, V., Barbe, J., Selmi, S., ... & Yahiaoui I., Carré J., Mouysset E., Molino J.-f., Boujemaa B., Barthélémy D., (2014). Interactive plant identification based on social image data. Ecological Informatics, 23, 22-34. https://doi.org/10.1016/j.ecoinf.2013.07.006
- Joly, A., Bonnet, P., Goëau, H., Barbe, J., Selmi, S., Champ, J., Dufour-Kowalski, S., Affouard, A., Carré, J., Molino, J.-f., Boujemaa, N., & Barthélémy D., (2016). A look inside the Pl@ntNet experience. Multimedia Systems, 22(6), 751-766. https://doi.org/10.1007/s00530-015-0462-9
- Goëau, H., Bonnet, P., Joly, A., 2017. Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017). CLEF: Conference and Labs of the Evaluation Forum, Sep 2017, Dublin, Ireland. ⟨hal-01629183⟩ https://hal.archives-ouvertes.fr/hal-01629183
- Affouard, A., Goëau, H., Bonnet, P., Lombardo, J. C., & Joly, A., (2017). Pl@ntNet app in the era of deep learning. ICLR: International Conference on Learning Representations, Apr 2017, Toulon, France. ⟨hal-01629195⟩ https://hal.archives-ouvertes.fr/hal-01629195
Metadados Adicionais
| Identificadores alternativos | 14d5676a-2c54-4f94-9023-1e8dcd822aa0 |
|---|---|
| https://ipt.plantnet.org/resource?r=queries |