An image-processing script works well on your first dataset. Months later, a new field campaign brings another batch of images, and you need to repeat the analysis. Which version of the script did you use? What settings produced those outputs? Could a colleague reproduce the same steps from the files you shared?

Reproducible data processing depends on preserving those details alongside the method. LgoPy blocks help by packaging a transformation with defined inputs, configurable parameters, and versioned code. In this tutorial, we will use a simple RGB-to-HSV conversion to show how to read plot images, save derived visualizations, and build a processing component that can be shared and reused across datasets in PhenoWorks.

PhenoWorks beta is here: a crop phenotyping web platform that brings research data, reusable analysis methods, and an AI assistant into one workspace. Load and visualize your data, extract structured features with LgoPy blocks, and use the results to support analysis, interpretation, and research writing.

The PhenoWorks website now uses Docusaurus for documentation, project updates, release notes, and development notes.