The deep learning revolution has brought significant advances in a number of fields , primarily linked to image and speech recognition. The standardization of image classification tasks like the ImageNet Large Scale Visual Recognition Challenge  has resulted in a reliable way to compare top performing architectures.
The use of deep learning for plant classification is not novel [3, 4] but has mainly focused in leaves and has been restricted to a limited amount of species, therefore making it of limited use for large-scale biodiversity monitoring purposes.
This Docker container contains a trained Convolutional Neural network optimized for plant identification using images. The architecture used is an Xception  network using Keras on top of Tensorflow.
As training dataset it has been used the great collection of images which are available in PlantNet under a Creative-Common AttributionShareAlike 2.0 license. It consists of around 250K images belonging to more than 6K plant species of Western Europe. These species are distributed in 1500 genera and 200 families.
A detailed article about this network and the results obtained with it can be found in .
This service is based in the Image Classification with Tensorflow model.
: Yann LeCun, Yoshua Bengio, and Geofrey Hinton. Deep learning. Nature, 521(7553):436–444, may 2015.
: Olga Russakovsky et al. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252, 2015.
: Sue Han Lee, Chee Seng Chan, Paul Wilkin, and Paolo Remagnino. Deep-plant: Plant identification with convolutional neural networks, 2015.
: Mads Dyrmann, Henrik Karstoft, and Henrik Skov Midtiby. Plant species classification using deep convolutional neural network. Biosystems Engineering, 151:72–80, 2016.
: Chollet, François. Xception: Deep learning with depthwise separable convolutions arXiv preprint (2017): 1610-02357.
: Heredia, Ignacio. Large-scale plant classification with deep neural networks. Proceedings of the Computing Frontiers Conference. ACM, 2017.
Run locally on your computer
You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:
$ docker pull deephdc/deep-oc-plants-classification-tf $ docker run -ti -p 5000:5000 deephdc/deep-oc-plants-classification-tf
If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:
$ virtualenv udocker $ source udocker/bin/activate $ git clone https://github.com/indigo-dc/udocker $ cd udocker $ pip install . $ udocker pull deephdc/deep-oc-plants-classification-tf $ udocker create deephdc/deep-oc-plants-classification-tf $ udocker run -p 5000:5000 deephdc/deep-oc-plants-classification-tf
Once running, point your browser to
and you will see the API documentation, where you can test the module
functionality, as well as perform other actions (such as training).
For more information, refer to the user documentation.