10.5061/DRYAD.65M87
Shi, Yeyin
Texas A&M University
Thomasson, John Alex
Texas A&M University
Murray, Seth C.
Texas A&M University
Pugh, Nicholas Ace
Texas A&M University
Rooney, William L.
Texas A&M University
Shafian, Sanaz
Texas A&M University
Rajan, Nithya
Texas A&M University
Rouze, Gregory
Texas A&M University
Morgan, Cristine L.S.
Neely, Haly L.
Texas A&M University
Rana, Aman
Texas A&M University
Bagavathiannan, Muthu V.
Texas A&M University
Henrichson, James
Bowden, Ezekiel
Texas A&M University
Valasek, John
Texas A&M University
Olsenholler, Jeff
Texas A&M University
Bishop, Michael P.
Texas A&M University
Sheridan, Ryan
Texas A&M University
Putman, Eric B.
Texas A&M University
Popescu, Sorin
Texas A&M University
Burks, Travis
Texas A&M University
Cope, Dale
Texas A&M University
Ibrahim, Amir
Texas A&M University
McCutchen, Billy F.
Texas A&M University
Baltensperger, David D.
Texas A&M University
Avant Jr, Robert V.
Vidrine, Misty
Texas A&M University
Yang, Chenghai
Morgan, Cristine L. S.
Texas A&M University
Avant, Robert V.
Texas A&M University
Henrickson, James
Texas A&M University
Data from: Unmanned aerial vehicles for high-throughput phenotyping and
agronomic research
Dryad
dataset
2017
Precision agriculture
Sorghum
unmanned aerial vehicle
corn
weed management
soil properties
plant height
winter wheat
biopysical properties
High throughput phenotyping
2017-07-13T00:00:00Z
2017-07-13T00:00:00Z
en
https://doi.org/10.1371/journal.pone.0159781
20104838495 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
USA
Texas
College Station