ChronoRoot: High-throughput phenotyping by deep learning reveals novel temporal parameters of plant root system architecture

Nicolás Gaggion¹, Federico Ariel², Vladimir Daric³, Éric Lambert³, Simon Legendre³, Thomas Roulé³, Alejandra Camoirano², Diego Milone¹, Martin Crespi³, Thomas Blein³, Enzo Ferrante¹

¹ Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, Argentina.
² Instituto de Agrobiotecnología del Litoral (IAL), CONICET, FBCB, Universidad Nacional del Litoral, Colectora Ruta Nacional 168 km 0, Santa Fe, Argentina.
³ Institute of Plant Sciences Paris-Saclay (IPS2), CNRS, INRA, University Paris-Saclay and University of Paris Bâtiment 630, 91192 Gif sur Yvette, France.

Installation:

First create the anaconda environment:

conda env create -f env.yml

Activate it with:

conda activate ChronoRoot

Then install the following packages via pip:

pip install tensorflow-gpu==1.15
pip install opencv-python
pip install git+https://github.com/lucasb-eyer/pydensecrf.git

In case of not having a GPU, use this line instead of the first one:

pip install tensorflow==1.15 

Usage:

Download the weights on ChronoRoot/modelWeights from:
https://drive.google.com/file/d/17g7vPcTo6bF1iCf5zrj8iZTK6q48lyRm/view?usp=sharing

or run this line:

python downloadWeights.py

For fast segmentation with the Deeply Supervised Residual U-Net use

python segmentFast.py imagePath --output_dir optionalSegPath --use_crf boolean --model ResUNetDS

For segmentation using the model ensemble use

python segmentEnsemble.py imagePath --output_dir optionalSegPath --use_crf boolean

For individual plant analysis, load the experiment data on config.conf and then run:

python chronoRoot.py