A software framework for model-assisted analysis of high throughput plant phenotyping data

What is Phenomenal ?

Plant high-throughput phenotyping aims at capturing the genetic variability of plant response to environmental factors for thousands of plants, hence identifying heritable traits for genomic selection and predicting the genetic values of allelic combinations in different environment.

This first implies the automation of the measurement of a large number of traits to characterize plant growth, plant development and plant functioning. It also requires a fluent and versatile interaction between data and continuously evolving plant response models, that are essential in the analysis of the marker x environment interaction and in the integration of processes for predicting crop performance.

In the frame of the Phenome high throughput phenotyping infrastructure, we develop Phenomenal. A software framework dedicated to the analysis of high throughput phenotyping data and models.

Phenomenal currently consists of 2D image analysis workflows built with standard image libraries (VTK, OpenCV, Scikit.Image), algorithms for 3D reconstruction, segmentation and tracking of plant organs for maize (under development), and workflows for estimation of light interception by plants during their growth.


Tutorial Jupyter Notebooks

Tutorial Jupyter Notebooks are available on the git repository in the folder examples.

API References



Phenomenal is released under a Cecill-C license.


Cecill-C license is a LGPL compatible license.