Release 2025

• Documentation has been published : https://phenomenal.readthedocs.io/
• An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping
• optional package openalea.phenotyping_data provides Data from plant phenotyping platform primarly aimed at desmonstrating/testing openalea.phenomenal, needed to run some of the tutorials
• Calibration has been extented to n-cameras n-calibration targets cases, and with simpler parameterisation

Test Release

Mostly, similar to 1.10.0, used to test CI

prerelease 2025

What's Changed

• Refactor calibration by @christian34 in #59

Full Changelog: v1.9.10...v1.10.0

v1.9.8

Remove notebook data, use phenotyping_data instead

v1.9.6

First release with tag-versioning and conda-forge-pinning config

v1.8.2

What's Changed

• Force stem by @BenoitDaviet in #29
• Merging maizetrack into phenomenal by @AurelienBesnier in #40
• Update conda to build on all Python version by @pradal in #43

New Contributors

• @BenoitDaviet made their first contribution in #29
• @AurelienBesnier made their first contribution in #40

Full Changelog: v1.8.1...v1.8.2

Phenomenal version 1.7.1

Add serialisation for VoxelSquelettons

Phenomenal version 1.7.0

Clean-up rst code, refactor data and debug CI

Phenomenal release

Phenomenal is a library for 3D plant reconstruction and analysis from high-throughput phenotyping images.

It is an open source library.
It contains novel algorithms and tutorials, that can be run directly in the browser using binder.