"Rolled-upness": phenotyping leaf rolling in cereals using computer vision and functional data analysis approaches.

X R R SiraultA G CondonGregory J RebetzkeG D FarquharG J Rebetzke

Published in: Plant methods (2015)

A method applying smoothing splines to skeletonised images of transverse wheat leaf sections enabled objective measurements of inter-genotypic variation for hydronastic leaf rolling in wheat. Mean-curvature of the leaf cross-section was the measure selected to discriminate between genotypes, as it was straightforward to calculate and easily construed. The method has broad applicability and provides an avenue to genetically dissect the trait in cereals.

Keyphrases

data analysis
deep learning
high throughput
machine learning
genome wide
gene expression
convolutional neural network