Minimum requirements

PlantCV has been tested on the following systems:

  • Linux: CentOS 7 (RedHat Enterprise Linux)
  • Linux: Ubuntu 12.04, 14.04, and 16.04
  • Linux: Raspbian "Jessie"
  • Mac OSX 10.11 and macOS 10.12+
  • Windows 10
  • Cloud9 IDE

Required dependencies

  • Python (tested with versions 2.7 and 3.6)
    • argparse
    • cv2 (we recommend 2.4.14+ or 3.3+)
    • matplotlib (requires at least 1.5, works with 2+)
    • numpy (requires at least 1.11)
    • pandas
    • pytest
    • python-dateutil
    • scikit-image
    • scipy
    • setuptools
  • OpenCV (we recommend 2.4.14+ or 3.3+)

Note: OpenCV 3 will work with either Python 2 or 3 but OpenCV 2 will only work with Python 2.

  • Anaconda
  • Git
  • Jupyter
  • SQLite

Anaconda-based installation procedure

Platforms: Linux, macOS, Windows

There are a variety of options for installing PlantCV depending on your use case. If you have experience with system administration you can install PlantCV and the dependencies using system package management tools and administrator privileges (feel free to ask if you get stuck).

For most users we recommend installation using Anaconda, a cross-platform package management system. Here is an overview of the process:

  1. Download and install the version of Anaconda that is appropriate for your system. Unless you have other reasons to, we recommend using Python 3. PlantCV is compatible with Python 2.7 but eventually support for 2.7 will end.
  2. Clone or download PlantCV from GitHub. Feel free to use GitHub Desktop or command-line git. Git will allow you to pull updates from GitHub, but if you prefer not to use git you can download a zip file of the package from GitHub.
  3. Create a Python environment for PlantCV that includes the Python dependencies.
  4. Install OpenCV and PlantCV.

Once you have Anaconda and git/GitHub Desktop installed, clone the PlantCV repository, open a command-line terminal application (on Windows there are other options but for this tutorial we will use the Anaconda Prompt application). In the examples below we use Python 3 and OpenCV 3, feel free to substitute your preferred versions.

# Clone PlantCV if you did not use the GitHub Desktop application
git clone

# Enter the PlantCV directory (if you cloned with GitHub Desktop your path may be different than below)
cd plantcv

# Create an Anaconda environment named "plantcv" and automatically install the dependencies
conda create --file requirements.txt -n plantcv -c conda-forge python=3.6 opencv=3

# Activate the plantcv environment (you will have to do this each time you start a new session)
source activate plantcv

# Install PlantCV
python install

# If PlantCV is installed successfully it should import without error
python -c 'import plantcv'

# Optionally, you can run automated tests on your system to make sure everything is working correctly
python test

Install optional Jupyter Notebook support.

conda install nb_conda

sqlite3 comes standard on macOS and many Linux distributions. On Windows Anaconda can be used to install the optional sqlite3 package.

conda install -c blaze sqlite3

Using PlantCV containers

Platforms: Linux, macOS, Windows

PlantCV currently supports the Docker container system but support for Singularity and other container systems are on our to-do list. Docker is a company/platform that provides operating-system-level virtualization (containers). See Wikipedia for more background. Containers are a useful way to package and isolate applications (and their dependencies) into a portable, lightweight virtualized environment. A PlantCV Docker container is available through Docker Hub. To use the PlantCV container you will need docker installed on your local system. If you have docker, you can use PlantCV as in the following examples:

# Pull the latest image of PlantCV from Docker Hub
docker pull danforthcenter/plantcv

# A simple command to demonstrate it works (nothing returned if import is successful)
docker run danforthcenter/plantcv python -c 'import plantcv'

To analyze data with the PlantCV Docker container you will need to map a local folder that contains your inputs into the container filesystem. We have set up a directory in the container at /data to get data into/out of the container. In the example below, local data and scripts are in a directory called /home/user but it can be any directory you want. Everything in /home/user will be accessible in the container and any outputs written to /data in the container will be written locally to the directory you provide.

For the sake of this example, assume that /home/user contains a PlantCV script called and an image called test-image.png. The in this case would be a script like the one described in the VIS tutorial.

# Analyzing data using the PlantCV docker image
docker run -v /home/user:/data danforthcenter/plantcv \
python /data/ -i /data/test-image.png -o /data -r /data/plantcv-results.txt

Script-based installation

Platforms: Ubuntu, macOS

Clone the PlantCV repository:

git clone

Run the setup script:

cd plantcv
bash scripts/

The script guides you through the installation steps. Successful completion ends with a usage report.

The script has been tested on Ubuntu x86_64-bit 16_04 server edition, OSX 10.11, and macOS 10.12.

Installation on other systems

Cloud9 IDE

Cloud9 is a development environment in the cloud that works with Chromebooks or other thin clients. The IDE workspaces are powered by Docker Ubuntu containers within a web browser.

After signing up for an account create a new workspace and choose a Python template.

Install update

sudo apt-get update

Install software dependencies

sudo apt-get install git libopencv-dev python-opencv python-numpy python-matplotlib sqlite3

Clone the PlantCV repository into your home directory

git clone

The default branch (master) is the latest release. If you want to check out a specific version:

# Switch to a stable release
cd plantcv

git checkout v1.1

Install PlantCV

sudo python install

After installation test with the following:

python -c 'import plantcv'

You will be given the following error:

libdc1394 error: Failed to initialize libdc1394

libdc1394 allows a program to interface with cameras that work on the ieee1394 standard(firewire). Due to no option to enable USB access in the Cloud9 workspace this error will keep occuring when running a pipeline. This error will have no effect on the output of your pipelines and can continue working despite the warning.

To temporarily remove the driver and error use:

sudo ln /dev/null /dev/raw1394

Test import again and you should see no more errors. Restarting workspace will require input to remove libdc1394 error again.

python -c 'import plantcv'