Description
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 project can be found at https://plantnet.org/. The observations in this collection are the ones for which the authors agree to share the associated pictures under a creative common licence and for which the species name is considered as valid.
Data Records
The data in this occurrence resource has been published as a Darwin Core Archive (DwC-A), which is a standardized format for sharing biodiversity data as a set of one or more data tables. The core data table contains 1,714,213 records.
1 extension data tables also exist. An extension record supplies extra information about a core record. The number of records in each extension data table is illustrated below.
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.
Versions
The table below shows only published versions of the resource that are publicly accessible.
How to cite
Researchers should cite this work as follows:
AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BONNET P (2023): Pl@ntNet observations. v1.8. Pl@ntNet. Dataset/Occurrence. https://ipt.plantnet.org/resource?r=observations&v=1.8
Rights
Researchers should respect the following rights statement:
The publisher and rights holder of this work is Pl@ntNet. This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.
GBIF Registration
This resource has been registered with GBIF, and assigned the following GBIF UUID: 7a3679ef-5582-4aaa-81f0-8c2545cafc81. Pl@ntNet publishes this resource, and is itself registered in GBIF as a data publisher endorsed by GBIF France.
Keywords
Occurrence; Observation; Occurrence
Contacts
Who created the resource:
-
https://orcid.org/0000-0002-2161-9940
Who can answer questions about the resource:
Who filled in the metadata:
-
Who else was associated with the resource:
Geographic Coverage
Plant observations from Pl@ntNet network come from all around the world.
| Bounding Coordinates | South West [-90, -180], North East [90, 180] |
|---|
Taxonomic Coverage
Pl@ntNet observations focus on plants.
| Kingdom | Plantae (Plant) |
|---|
Project Data
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 illustrated observations explicitly shared by PlantNet users under a Creative Common license.
| Title | Pl@ntNet Observations |
|---|---|
| Identifier | observations |
| Funding | 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). |
| Study Area Description | Entire world |
The personnel involved in the project:
Sampling Methods
No sampling protocol, opportunistic observations by Pl@ntNet users.
| Study Extent | Entire world, Plantae. |
|---|---|
| Quality Control | The validation is based on three main criteria: - the identification confidence score greater than a given threshold, the score being inferred from (i) the output of the automated identification algorithm (categorical probability) and (ii) the species names proposed by the members - the contributor has a reputation score higher than a given threshold - the species name matches the checklist considered as the most trusted one for the country where the observation was done |
Method step description:
- This collection contains observations of plants shared by Pl@ntNet users using one the three Pl@ntNet applications (androïd, iOS, web, more information here: https://plantnet.org/). The following filters were applied: - image license cc-* - is geolocated - is valid (identification score > threshold) - from a user with an enabled account - with a known species name (valid or synonym) in Pl@ntNet - species name != Cannabis - date_observation > 0 - 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
Bibliographic Citations
- 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
Additional Metadata
| Alternative Identifiers | 7a3679ef-5582-4aaa-81f0-8c2545cafc81 |
|---|---|
| https://ipt.plantnet.org/resource?r=observations |