v0.2.1

Release Notes

This is a minor release which includes the following:

• update equations and latex formating in Method.pdf
• add Method.md for online preview and update links in README

v0.2.0

AgReFed-ML v0.2 Release Notes

We are pleased to announce the release of AgReFed-ML version 0.2. This version comes with several updates and improvements aimed at enhancing usability and cleanliness of the codebase.

Highlights of the Update

1. New Code Reference API Docs: The documentation of AgReFed-ML has been improved with the addition of API code reference docs.

2. Added README and CC License to Data Sample Folder: A README file has been added to the data sample folder to provide more information about the included data samples. Also, we've included a Creative Commons (CC) License in the data sample folder to clarify the usage rights.

3. Codebase Cleanup: To improve the readability and maintainability of AgReFed-ML, redundant code has been removed, which should make it more easier to work with.

4. Updated Docs and Notebook Guide: Furthermore, the Notebook guide has been updated to provide clearer instructions and examples to help you in your machine learning tasks related to agriculture research.

About AgReFed-ML

The AgReFed-Ml project contributes software that provides multiple machine learning workflows and tools for agriculture researchers. A particular focus is to develop machine learning models to map soil properties under sparse and uncertain input with support for spatial-temporal correlations and multi-covariates.

While use-cases are developed for mapping soil bulk density, changes in carbon concentration, and soil moisture, the software can be used for a diverse range of soil property predictions such as sodicity, salinity, pH-values, and many more.

Contributions

We welcome and appreciate any contributions to AgReFed-ML. If you're interested in contributing, whether it's improving the documentation, adding new features, new notebooks, or reporting bugs, please check out our contribution guidelines. Your involvement is crucial in making these tools more effective and valuable for the agricultural research community. We look forward to collaborating with you!

Thank you for your support and happy modeling!

v0.1.0

First release of the AgReFed-ML project. This release includes:

• Python source code for multiple spatiotemporal models under uncertainty
• Use-case examples as series of Jupyter notebooks
• Sample data and settings files for reproducible workflows
• Documentation including description of functionalities, installation instructions, use-case description etc
• Method paper
• Project overview
• Contribution guidelines
• License

Main Contributor and author:
Sebastian Haan