This is the modified version of RootNav 2.0 Code repository. Original RootNav-2.0 algorithm works on encoder based CNN for plant skeleton detection, whereas this repository is based on Mask-RCNN for image segementation and skeleton extraction for further analysis of plant phenotype property study.
You will first need to download the code, either as a zip above, or by cloning the git repository (recommended):
git clone https://github.com/Kamlesh364/Modified-RootNav2.0/
Next, install the required dependencies. If you're using pip, then the following will work in Linux:
cd Modified-RootNav2.0
pip install -r requirements.txt
This will download and set up all the required libraries, providing you with a Python(>=3.6) installation.
This repository can only be used training and will be availble for inference shortly.
Training code may be found in the training folder. Instructions on training models are given in the training README.
[1] Yasrab, R., Atkinson, J. A., Wells, D. M., French, A. P., Pridmore, T. P., & Pound, M. P. (2019), RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures, GigaScience, 8(11), giz123.
[2] He, Kaiming. “Mask R-CNN.” arXiv.org, 20 Mar. 2017, arxiv.org/abs/1703.06870.