Degree Type

Creative Component

Semester of Graduation

Fall 2018


Agricultural and Biosystems Engineering

First Major Professor

Matthew Darr


Master of Science (MS)


Seed Technology and Business


Early-season stress tolerance characterization of hybrid corn products relies heavily on early stand count and early vigor data from field trials in order to properly characterize products and to accurately assign stress emergence scores. The current manual collections of these data are labor-intensive, time-consuming, prone to human error, and in the case of vigor scoring, subjective. Unmanned aircraft systems (UAS) may provide a more accurate, rapid, objective, and efficient method for collecting stand count and vigor data resulting in higher quality products and overall cost-savings.

The purpose of this study was to determine if UAS could be used for stand count and vigor data collection for the early-season stress tolerance characterization of hybrid corn products. The early-season stress tolerance characterization field trial was flown on 12 different dates during the spring of 2017 representing plant growth stages from VE to V5. Stand count and plot cover values were calculated from the UAS obtained images for the 12 flight dates using a 2017 and an updated 2018 software algorithm. It was determined that the best time to collect UAS stand count data occurred at the V2 plant growth stage before leaf overlapping occurred. An UAS derived plot cover normalization method was also developed for assigning plot vigor scores allowing for more objective, reproducible, and unbiased assessments of plot vigor.

Copyright Owner

Erin Anderson

File Format