IS.08: Advanced Statistical Methods for Data Arising from High Throughput Phenotyping in Agriculture

24 August 2020 | 09:00 UTC / GMT

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Just as marker technology led to an explosion of genetic data and analyses, high throughput phenomics (HTP) has led to large amounts of data. HTP raises issues of design, the development of traits and statistical analysis, much the same as any experiment, but with the added complication of high numbers of repeated measurements and hence “big data” issues. Not only can data be observed in time but also spatially and over wavelengths for spectral data. The development of appropriate statistical methods is paramount for such data and this session aims to have a snapshot of new and exciting developments in this area. 


Arunas (Ari) Verbyla, Data61, CSIRO

Alison Kelly, Queensland Department of Agriculture and Fisheries and the University of Queensland
Spatio-temporal analysis of plant root architecture high throughput phenotyping of a structured population of wheat genotypes using an aeroponic platform for imaging root growth 

Joanne De Faveri, Data61, CSIRO
Relation primary traits to high throughput phenotyping traits for crop varietal prediction allowing for non-genetic variation

Models and Methods for Predicting Plant Breeding Data Using Hyperspectral Image and Genomic Information

Martin Boer, Wageningen University
Using Tensor P-splines for the analysis of High Throughput Phenotype data in breeding trials

Fred van Eeuwijk, Wageningen University