(Graduate student Dorothy Kirsch operates a drone on the University of Minnesota St. Paul Campus)
Written by Jonathan Eisenthal
Farmers today have a number of models and software within reach, but most all of them require expensive hardware, software and services with trained technicians to deliver a crop forecast.
University of Minnesota Associate Professor Candice Hirsch, leading the project supported through the Minnesota Corn Growers Association, believes drones could offer a powerful predictive tool for farmers at a lower cost.
Her research, now one growing season down and two more planned, seeks to use periodic drone flights over farm fields during the course of the growing season to track stand count, plant height, canopy closure and growth rate. In addition to those morphological traits, Hirsch will also be able to measure how plants adapt to cold, heat and water stress using drone technology that identifies spectral wavelengths.
Hear Candice Hirsch discusses this project in the
latest episode of the Minnesota Corn Growers Association Podcast
All of this information obtained during the drone flights have the potential to predict performance at the end of the season. A geneticist by training, Hirsch sees the power of ‘big data’—crunching large volumes of data into useful predictive models—and she wants to put that power into the hands of farmers.
“We are trying to use this technology to determine how plants in different parts of the field are responding to their microenvironment and we rely on our agronomist partners to interpret and determine how you would actually use this information in directing targeted management decisions,” said Hirsch.
Hirsch and her team developed the method and the modeling software in the first year of the project, doing flyovers of nursery plots at the University of Minnesota St. Paul Campus. But for the next two growing seasons, they will conduct the research at actual farms, thanks to a handful of cooperators in southeastern Minnesota.
“We fly the drone and we take many pictures as it flies throughout the field and we stitch them all together to make one image,” Hirsch explained. “We can then generate a digital elevation model… a 3-D reconstruction of the field.”
Hirsch said her team will first fly the field with no noticeable corn to provide the base, followed by additional flights throughout the year to compare progress.
With each flight, Hirsch will be able to measure how a plant is performing based on appearance, as well as what is happening below the surface, including photosynthetic efficiency and its ability to transport sugars. By evaluating the plant’s growth under changing environmental conditions, she will then be able to compare that performance across different genotypes, evaluating their stress responsiveness.
In the end, Hirsch wants to be able to hand a piece of software over to farmers so they can use their drones to predict how the crop will finish, and perhaps even perform meaningful rescues with in-season management decisions.
“The advantage of using drones is you don’t have to have a pilot’s license at the heights we are flying, they are relatively inexpensive, and you don’t have the complexities of dealing with cloud cover,” said Hirsch. “So, by developing this system using drones we see this as something that can be democratized a lot faster, where anyone can have it.”
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