2 posts tagged with "Documentation"

Crop phenotyping research rarely stays in one application. You might organize field trials in PhenoWorks, explore measurements in a Python notebook, and fit a statistical model in R. Moving between those tools should not mean repeatedly downloading files by hand or writing the same HTTP requests for every project.

The PhenoWorks SDKs for Python and R bring projects, datasets, files, and analysis pipelines into your existing scripts. Browse data, submit a workflow, wait for its results, and download the outputs for your next analysis—all through your PhenoWorks account.

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 declared inputs, configurable parameters, and versioned code. In this tutorial, we will use a simple RGB-to-HSV conversion to show how data reaches a block from each upload method, how a block declares the sensors and data products it reads, and how to save derived outputs that can be filtered and reused across datasets in PhenoWorks.