Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research

Advances in automation and data science have led agriculturists to seek
real-time, high-quality, high-volume crop data to accelerate crop
improvement through breeding and to optimize agronomic practices. Breeders
have recently gained massive data-collection capability in genome
sequencing of plants. Faster phenotypic trait data collection and analysis
relative to genetic data leads to faster and better selections in crop
improvement. Furthermore, faster and higher-resolution crop data
collection leads to greater capability for scientists and growers to
improve precision-agriculture practices on increasingly larger farms;
e.g., site-specific application of water and nutrients. Unmanned aerial
vehicles (UAVs) have recently gained traction as agricultural data
collection systems. Using UAVs for agricultural remote sensing is an
innovative technology that differs from traditional remote sensing in more
ways than strictly higher-resolution images; it provides many new and
unique possibilities, as well as new and unique challenges. Herein we
report on processes and lessons learned from year 1—the summer 2015 and
winter 2016 growing seasons–of a large multidisciplinary project
evaluating UAV images across a range of breeding and agronomic research
trials on a large research farm. Included are team and project planning,
UAV and sensor selection and integration, and data collection and analysis
workflow. The study involved many crops and both breeding plots and
agronomic fields. The project’s goal was to develop methods for UAVs to
collect high-quality, high-volume crop data with fast turnaround time to
field scientists. The project included five teams: Administration, Flight
Operations, Sensors, Data Management, and Field Research. Four case
studies involving multiple crops in breeding and agronomic applications
add practical descriptive detail. Lessons learned include critical
information on sensors, air vehicles, and configuration parameters for
both. As the first and most comprehensive project of its kind to date,
these lessons are particularly salient to researchers embarking on
agricultural research with UAVs.