Plant identification | Roobos

Plant identification can be hard. There are literally 1000’s of plants out there, with countless varieties of the most beautiful plants. Especially when you work in a flowershop or gardencenter, it’s difficult to always know every plant that your customer maybe asks of you.

Well, now you don’t have to know every plant by hard anymore.
Roobos presents, in collaboration with Pl@ntnet, our own plant identification application.
This tool helps you to identify plants with pictures. Just upload a picture of a plant and the plant identification will tell you the best matches for that picture.
In our database we have litteraly thousands of plants and our application will tell you which plants resembles the most based on the picture that you have uploaded.
At least one photo of leaf, flower, fruit or bark is required for the plant identification to work
This online plant identification is free to use for all our customers and visitors of our website. If you like this application, please drop us a like on Facebook or Google. It tremendously helps us out to do further projects like this in the future.
It is recommended to use images of maximum 1280*1280 pixels

Plant identification by Pl@ntNet is implemented in accordance with a consortium agreement between CIRAD, INRA, INRIA and IRD, and separate funding agreements between Agropolis Fondation and each of the above-mentioned research organizations. Since 2015, the Pl@ntNet initiative is supported by the Future investment program Floris'Tic, involving the following consortium: Agropolis Fondation, Tela Botanica, INRIA, CIRAD, CNRS, INRA, IRD and Montpellier University.
Images indexed in the reference database are provided by Tela Botanica's volunteers [including Benoît Bock and Jean-LucTasset from PhotoFlora (photoflora.free.fr)], as well as contributors to Encyclopedia of Life (eol.org).

The Pl@ntnet project was earlier featured in the following scientific publications (selection):

• Joly A. et al., 2015. A look inside the Pl@ntNet experience. Multimedia Systems, Springer Verlag, 2015, pp.16.
• Bonnet P. et al., 2015. Plant identification: man vs. machine. Multimedia Tools and Applications, 1-19.
• Joly A. et al., 2014. Interactive plant identification based on social image data. Ecological Informatics, Elsevier, 2014, 23, pp.22-34.
• Goëau H. et al., 2014. Pl@ntNet Mobile 2014: Android port and new features. Demonstration. ACM Conference on Multimedia Retrieval (ICMR) 2014.

We hope you enjoy our application and that it will bring the beautiful world of plants closer to you.