Welcome to Pl@ntNet-identify,
a picture identification tool realized by the Pl@ntNet project.
The goal of this software is to allow you to submit botanical pictures against a database in order to help you in the identification process, and extract the closest matches in the database rather than manually searching through thousand of entries.
In order to use this software you will have to choose between the following databases which is most adapted to your picture identification, depending on the plant location, and the type of data contained in your picture (each database is illustrated by a random set of images to give you the big picture!).
After following the "Identify" link corresponding to the knowledge database you have chosen, you will then be invited to provide the picture(s) of the species you are willing to identify and launch the identification process.
The Tree database contains images of tree from the french flora. Images have been collected by amateurs’ botanists as part of the project “Capitalisationd’images de plantes “. The main objective of the project was to collect images of the various organs of the same plant. Indeed, the combination of images of different organs (leaves, flowers, etc.) is expected to improve considerably the identification process. Citizens were asked to take pictures of organs of European trees and capitalize it with the Carnet en ligne, which is a field survey’s tool.
You can still join the project “Capitalisationd’images de plantes” to know more about it and contribute to feed the database.
Partners and collaborators
Tela Botanica, CIRAD and INRIA as part of the project Pl@ntNet.
Thanks to the Tela Botanica’s volunteers for collecting the data.
Cette base de données est issue d'un travail collaboratif, mené grâce au partenariat de différentes structures, présentes sur le territoire de la Réunionnais. Elle porte principalement sur les espèces ligneuses indigènes de l'île de la Réunion, mais intègre aussi un certain nombre de plantes exotiques. Ce travail initié dans le cadre du cas d'étude Pl@ntTreeRun, permet de bénéficier d'une large expertise botanique, disséminée à travers des instituts de recherches et associations travaillant dans le domaine de la gestion de la Biodiversité, aussi bien naturelle que cultivée.
The Pl@ntScan database is a collaborative botanical dataset. The database focuses on leaves of 71 tree species from French Mediterranean area, and contains 3 different kinds of pictures: scans, scan-like photos and free natural photos. The uniqueness of the Pl@ntLeaves database is that the scans were created by 20 different persons, some professional botanists and some volunteers belonging to the French botanical social network Telabotanica. The leaves were collected at different places mainly in the south of France and were acquired with various scanners. All data were validated one-by-one by botanists from the Amap Unit Research of Montpellier. Each scan shows the upper-side of one leaf, most of the time centered and oriented vertically along the main natural axis and with the petiole visible.
farming provides about 75 percent of the world’s rice needs,
and has a
particularly important role to play at the moment as
prices are at a 10-year high, while global stocks are at a
Weeds are the major cause of the reduction of rice growth rates and productivity with a
yield loss of
10 to 30% worldwide. IRRI (International Rice Research
highlighted this problem in its 2000 strategic plan where
is its major concern. Improved weed control through
Integrated Pest and
Weed Management is the most attractive option for crop
involves the appropriate choice and combination of
mechanical, biological and chemical measures such that each
the others to maintain the weed population at manageable
approach based on weed control requires weeds to be
identified as soon
as they appear in the field, i.e. generally before
with access to up-to-date and reliable information on weed
species and weed management
Pl@ntNet identify now runs on the RiceWeeds database. It allows you to submit an image and identify it by visual similarity.