KSU, PrecisionHawk project to match UAV images with ag algorithms

An agreement between Kansas State University and PrecisionHawk is aimed to use unmanned aerial vehicles that will collect agronomic information to help farmers increase crop production.

The partnership, which began this month between the university and the company, establishes a four-year project “Advancing an end-to-end solution for agricultural applications of unmanned aerial vehicles and remote sensing.”

A Kansas State University agronomy expert along with researchers at Kansas State University Salina are teaming up to use their expertise on the project to help PrecisionHawk create apps and programs that will turn the aerial images of corn, and down the road, other field crops, into useful data about potential crop production challenges that include yield-limiting factors and characterization of yield potential, such as plant growth.

The project is creating tools farmers and agronomists can use on the pictures to get information, such as potential productivity, about crops. The key issue in the use of UAS to collect data isn’t whether the technology can collect the images, but whether those images are pictures that can be translated into scientifically sound and useful information, noted Ignacio Ciampitti, Kansas State University agronomy professor and project leader.

Kansas State University-Salina researchers are conducting UAS flights to determine what images and video sensors get the maximum information about a field. The images the researchers are taking include RGB, Normalized Difference Vegetation Index and multi-spectrum, Ciampitti said.

Atmospheric conditions, such as wind speed, the amount of cloud cover and high temperatures pose some of the greatest challenges to image collection, he noted.

The images are taken throughout the growing season, beginning shortly after spring planting, to help farmers improve crop yield potential. By looking at the images of newly-emerged corn, farmers and agronomists can determine stand count and whether they should re-seed their fields, Ciampitti said. The images also can look at agronomic issues, including pest problems and canopy health.

The advantage over conventional field scouting is that images of several thousand acres can be collected in a few hours, he said.

 Ciampitti is using information from the images to help develop algorithms that can be converted into computer software or apps for PrecisionHawk’s Algorithm Marketplace. The marketplace recently was launched with data analysis tools for the UAS market and is meant to simplify how UAS operators interpret data collected from a UAV’s geographic information system during flight.

The goal of the project team is to have the app ready for farmers by the spring 2016 farming season, Ciampitti said.