CIAT PlantCV Workshop

Here you will find resources to participate in our interactive workshop focused on using PlantCV to develop pythonic workflows to analyze your plant phenomic data. In this workshop, you will engage in instructor- and self-guided Jupyter notebook activities to obtain training in the following directories:

1. Jupyter-Tips_Image-Theory_Background.ipynb: This notebook provides the user with background information on using Jupyter notebooks for developing interactive Python notebooks in addition to information on image data for analysis. It is highly recommended that participants begin with this notebook.
2. Single-Plant-Analysis-Tutorial: Learn the fundamentals of image analysis and how to develop a workflow of a single plant image of Arabidopsis thaliana using PlantCV. You will learn the elements that make up an image and how PlantCV using image data to extract plant features and data.
3. Multiple-Plant-Parallelization-Tutorial: In this module, you will develop a workflow on a tray of Arabidopsis thaliana. Discover how to segment and identify individual plants amongst the tray and extract relevant trait data for each plant. Explore analyzing multiple images simultaneously by parallel executing your workflow over a subset of image data.
4. Seed-Workflow: Here you will see how PlantCV detects, counts, and extract seed characteristics on an image of Chenopodium quinoa seeds.
5. Random-Forest-Classifier-Tutorial: In this exercise, participants will work in groups of 2-3 to capture quality images of various dried legumes and import them into a PlantCV workflow. You will develop training sets for several different dried beans to train a machine learning model capable of identifying specific legumes in a heterogenous mixture.
6. naive-Bayes-Workshop: In this exercise! participants will utilize images with diseased leaves or roots imaged with a minirhizotron imaging system. In order to execute this notebook, you will need to have collected pixels using Pixel Inspector in ImageJ/Fiji (see naiveBayes.pdf).

By accessing this repository, you will have access to the necessary image datasets and Jupyter notebooks for each activity. These resources will empower you to learn and apply the image analysis techniques using PlantCV. These notebooks can also utilize Google Colaboratory, this means that you will need a Google account and have Google Colaboratory installed and added to your Google Workspace. See the markdown file named Google-Colaboratory.md.