image_analysis_plantcv

Image analysis with PlantCV

Image analysis in Python with PlantCV

This workshop aims to provide a virtual, hands-on, and interactive introduction to image analysis with Python and PlantCV (https://plantcv.danforthcenter.org/). Participants will utilize interactive coding environments in the cloud (Jupyter notebooks) to gain hands-on experience with importing and working with image data in Python using PlantCV and other open-source tools. The learning goals of the workshop are: 1) participants will gain an understanding of image data structure and the primary tools available in Python to work with image data; 2) participants will learn how to use PlantCV to build image analysis workflows; 3) participants will learn how to process image data in parallel using PlantCV workflows; and 4) participants will gain a brief introduction to visualizing phenotypic measurements extracted from a PlantCV workflow analysis.

Project website: https://plantcv.danforthcenter.org/

Twitter: @plantcv

Detailed outline

1. The START_HERE.ipynb is a Jupyter notebook template for building a PlantCV workflow. We will start here and fill it out together. completed_multi_plant_notebook.ipynb is a completed workflow that you can use as a reference or if you follow the workshop afterwards.
2. We will use the Terminal to convert the finished workflow into a Python script using the command jupyter nbconvert --to python START_HERE.ipynb.
3. We will edit the Python script to polish it into a functioning workflow script. completed_multi_plant_notebook.py is included as a reference.
4. We will use the notebook parallel_configuration.ipynb to create a configuration template and use Jupyter's text editor to edit it. multi-plant-analysis.config is included as a reference.
5. We will use the Terminal to run our workflow on the full image dataset using the command plantcv-workflow.py --config multi-plant-analysis.config.
6. The output of running PlantCV on the full dataset is included in multi-plant-results.json.
7. We will convert the JSON output file to comma-separated (CSV) format using the Terminal and the command plantcv-utils.py json2csv -j multi-plant-results.json -c results.
8. We will use the notebook plot_results.ipynb to visualize the results.

Citations

Veley KM, Berry JC, Fentress SJ, Schachtman DP, Baxter I, Bart R. 2017. High-throughput profiling and analysis of plant responses over time to abiotic stress. Plant direct 1:e00023. DOI: 10.1002/pld3.23.

Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. 2017. PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ 5:e4088. DOI: 10.7717/peerj.4088.

Fahlgren N, Feldman M, Gehan MA, Wilson MS, Shyu C, Bryant DW, Hill ST, McEntee CJ, Warnasooriya SN, Kumar I, Ficor T, Turnipseed S, Gilbert KB, Brutnell TP, Carrington JC, Mockler TC, Baxter I. 2015. A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular Plant 8:1520–1535. DOI: 10.1016/j.molp.2015.06.005.