Stomatal Comprehensive Automated Neural Network(SCAN)

About

SCAN is an automated tool for measuring stomatal density and aperture size specific for Canola.

Installation

Prerequisite:

Make sure you have conda and git installed in your local machine.

1. We use conda to manage environment, please download and install Miniconda with correct system version in https://docs.anaconda.com/free/miniconda/

   After installing, initialize your newly-installed Miniconda.

   For Windows: open the "Anaconda Prompt (miniconda3)" program and enter

   conda init
   

   For Mac and Linux: open terminal and enter

   ~/miniconda3/bin/conda init bash
   ~/miniconda3/bin/conda init zsh
   

2. download git from this link: https://git-scm.com/downloads

Step 1:

Open a terminal in Mac and Linux or Anaconda Prompt (miniconda3) in Windows, clone the project in your local machine:

git clone https://github.com/William-Yao0993/SCAN.git
cd SCAN

Step 2:

In terminal Configure the working environments:

conda env create -f environment.yml

Start SCAN

Make sure the current working directory in terminal is changed inside the SCAN folder, if not:

cd SCAN

Activate the working environment:

conda activate SCANenv

Run the following command to start SCAN in activated environment:

python -m main

ps: the first initialization would take 10-20 seconds to start up the application

Option 2:

Download the fully packaged executable for usage, but the model running time is slowing down by some reason, and console popups when multiprocessing engaged.

Scale bar practical measurement

Species	Resolution	Magnification	Length1	Unit1	Length2	Unit2
Arabidopsis	2560 X 1920	690.5±1 X	0.05	mm	221	pixel
Canola	2560 X 1920	692±2 X	0.05	mm	222	pixel
Canola	1280 X 960	691±2 X	0.05	mm	112	pixel
Maize	2560 X 1920	413±1 X	0.1	mm	269	pixel
Tobacco	2560 X 1920	690.5±1 X	0.05	mm	221	pixel
Panicum miliacecm	2560 X 1920	413±1 X	0.1	mm	269	pixel
Rice	2560 X 1920	413±2 X	0.1	mm	267	pixel
Rice	2560 X 1920	413±1 X	0.05	mm	227	pixel
Wheat	2560 X 1920	692±1 X	0.05	mm	224	pixel
Wheat	2560 X 1920	412.5±1 X	0.1	mm	267	pixel

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For training

Please refer to this repository for model weights and training parameters: https://github.com/William-Yao0993/FD_detection

Citation

If you use this project in your research or wish to refer to the baseline results, please cite us.

@article {Yao2024.06.12.598768,
	author = {Yao, Lingtian and von Caemmerer, Susanne and Danila, Florence R},
	title = {Automated and high throughput measurement of leaf stomatal traits in canola},
	elocation-id = {2024.06.12.598768},
	year = {2024},
	doi = {10.1101/2024.06.12.598768},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/06/14/2024.06.12.598768},
	eprint = {https://www.biorxiv.org/content/early/2024/06/14/2024.06.12.598768.full.pdf},
	journal = {bioRxiv}
}