• Input data

    Whenever possible, you should use POST requests to improve processing speed

    Submitted images are resized to a maximum size of 1280 pixels on the large side.
    Smaller submitted images will never be upscaled.
    ⚠️ Identification performance is not guaranteed for images smaller than 800x800px.

    Compression: jpg with a quality=90 is recommended

    Sending several images of the same individual plant (up to 5) do improve the results, even if they are of the same type of view (flower, fruit, leaf, etc.). Some types of view/organs, however, are more informative than others in the following order (best first): flower, fruit, leaf, entire plant, bark. So that it is often more efficient to submit several images of the most informative parts (e.g. several flowers and/or fruits) than trying to be exhaustive in the coverage of the different types. The bark view in particular is challenging. It is useful when there is no other visible part of the plant (e.g. deciduous trees during winter) but it leads to significantly lower accuracy than other views. Leaves are also often much less discriminative than flowers and fruits. Nevertheless, Pl@ntNet's AI algorithm automatically assigns different weights to the different types of view in accordance with those properties. You can thus submit images of different types without worrying too much about it. You can also submit the images without specifying the type of view. In that case, it will be automatically inferred by the AI.

  • Rejection

    Images identified as non-plants will be rejected, and lead to a "404 Species not found" error. Should an inappropriate image be submitted, it will most likely be filtered by this mechanism. If you wish to manage the rejection of non-plant images by yourself, set "no-reject" parameter to true.

  • Information about the identification model

    Current model: Inceptionv3

    Methodology: Pl@ntNet's model is updated on approximately a two-monthly basis with a principle of non regression. Non regression is measured in terms of several indicators: accuracy, number of species recognized, respect of an acceptable response time, respect of an acceptable energy consumption, memory usage, etc. Accuracy is obtained by testing the model on a set of private datasets of different nature, from the easiest (e.g. common species from various families) to the most difficult (e.g. very similar species of complex and/or data deficient genera).

  • Additional data: GBIF species API

    To get more data about the species on the identification results, you can use the GBIF species API

    Some Pl@ntNet species have a gbif.id field.
    If they have, you can use it to load more data, for example: https://api.gbif.org/v1/species/5231190/vernacularNames