Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

ABSTRACT: With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote

SUBMITTER: Toda Y 

PROVIDER: S-EPMC8966771 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature