DOI: 10.5061/dryad.65m87
title: Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research

description: 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.
description: Case_Study_1_plant_height_CornRaw data sample and ground truth data for corn plant height estimate in case study 1.Case_Study_1_plant_height_SorghumRaw individual images with gps log and ground truth data for the sorghum plant height estimate in case study 1.Case_Study_2_winter_wheat_biophysicalRaw individual images with gps log and ground truth data for case study 2 winter wheat biophysical properties study.Case_Study_3_soil_plant_interaction_RGBRaw RGB images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit.Case_Study_3_soil_plant_interaction_NIRRaw NIR images with gps log and ground truth data for case study 3 soil and plants interaction. The RGB and NIR images were split as two files due to the file size limit.Case_Study_4_weed_management_evaluation_Raw_Image_Data_1_of_10Part 1 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_2_of_10Part 2 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_3_of_10Part 3 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_4_of_10Part 4 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_5_of_10Part 5 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_6_of_10Part 6 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_7_of_10Part 7 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_8_of_10Part 8 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_9_of_10Part 9 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10.Case_Study_4_weed_management_evaluation_Raw_Image_Data_10_of_10_GroundTruthPart 10 of 10 of raw individual images with gps log and ground truth.Case_Study_1_plant_height_corn_Raw_Image_Data_1_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_2_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_4_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_5_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_6_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_7_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_8_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_9_of_10Case_Study_1_plant_height_corn_Raw_Image_Data_10_of_10_and_GCPsRaw individual images with gps log and shapefile of ground control points of case study 1 corn plant height measurement. The raw images are separated into ten files.