Multitemporal multispectral imagery for rice yield and phenology prediction

Timeseries data captured by unoccupied aircraft systems (UASs) are
increasingly used for agricultural applications requiring accurate
prediction of plant phenotypes from remotely-sensed imagery. This
benchmark dataset for rice supports the development of improved analytical
approaches for phenotype prediction from multispectral timeseries of drone
imagery. The dataset includes five experiments conducted at the
USDA-ARS Dale Bumpers National Rice Research Center in Stuttgart, AR in
2021 and 2022: two nitrogen rate studies, a private hybrid study, an
inbred study, and a genetic diversity study. A randomized block design was
established in both years, with 252 total plots in 2021 and 180 plots in
2022. Plots were imaged at 12 timepoints throughout the season in both
years. The dataset includes images for each plot as well as extracted
features (49 features including vegetation indices, texture properties,
and thermal features), and per-plot yield and phenology data.