PT  - JOURNAL ARTICLE
AU  - Zhikai Liang
AU  - Piyush Pandey
AU  - Vincent Stoerger
AU  - Yuhang Xu
AU  - Yumou Qiu
AU  - Yufeng Ge
AU  - James C. Schnable
TI  - Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
AID  - 10.1101/169045
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 169045
4099  - http://biorxiv.org/content/early/2017/09/21/169045.short
4100  - http://biorxiv.org/content/early/2017/09/21/169045.full
AB  - Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.