Getting started with PlantCV!
Welcome to PlantCV! This guide will help you quickly get up and running with our powerful open-source computer vision program designed for analyzing plant images and beyond.
Understand the data you can analyze
PlantCV isn't just for plant analysis! It can also be used for research in various fields such as medical imaging (eye veination) and material science (concrete crack detection). Image data is image data! For successful analysis, high-quality and consistent imaging is important. To ensure you’re capturing the right data, refer to our Imaging Guide (coming soon).
Learn How to Use PlantCV
PlantCV is designed for flexibility and ease of use, making it simple to analyze a wide variety of images, whether you're working with thermal, hyperspectral, RGB, or other types of images. Our tutorials can help to guide you through setting up workflows that will work for your data and making the most out of PlantCV's capabilities. Check out the Tutorials to understand how people have previously used PlantCV. If you have questions about your own data feel free to reach out to the PlantCV Team (plantcv@danforthcenter.org)
Install PlantCV
To begin your journey with PlantCV, you need to install the software. It’s available for Windows, Mac, and Linux. Follow the detailed installation instructions provided in our Installation Guide. We recommend using PlantCV with Jupyter Notebooks.
Cite PlantCV
We try to keep track of research that uses PlantCV so we can continue to support and sustain the project. See our guide on how to cite PlantCV and add yourself our user map.
Contribute to PlantCV and our GitHub Page
PlantCV thrives on community contributions. Whether you want to report bugs, share tutorials, or contribute code, your involvement is valued.
If you have specific questions, or want to request new features, visit our GitHub Page. It’s the place to find help and engage with the PlantCV community.
Learn more about how you can contribute by visiting our Contributing Page.
