A drone used to photograph and identify weed problems is shown off during a July 17 field day in Cypress County, Alberta, Canada.
Researcher Chris Neeser, an Alberta Agriculture weed pest specialist, is experimenting with using unmanned aerial systems (UASs), or drones, to identify weed problems in crops.
Neeser is working with Jan Zalud of JZ Aerial in Calgary to develop a protocol on how to acquire and process overhead field imagery captured by UASs and then determine how accurate, useful and economical it is in identifying weed issues.
He will compare the results with conventional methods for weed identification and scouting. At a field day in Cypress County, Alberta, Canada, on July 17, 2014, Neeser said farmers are interested in aerial technology, and many have either bought a UAS or are thinking about doing so.
Is this just a toy or is it actually something that will help you make money? asked Neeser.
He and Zalud started to answer that question by collecting Normalized Difference Vegetation Index (NDVI) images of quarter-section fields in Alberta's Starland and Newell counties. JZ Aerial is using a commercially available camera attached to a small, fixed-wing craft. The camera filters have been modified to photograph near infrared, green and blue instead of filtering out the near-infrared spectrum.
The plane flies a grid pattern, taking a photograph every 2.8 seconds. It makes 11 passes on a quarter section and can be flown manually or using an autopilot program. Near-infrared images make plants stand out in the photographs, according to Neeser. Areas of lush foliage reflect a greater amount of infrared light.
Of course, the more plants there is, the better the canopy, the more photosynthesis, in other words, and the more of that infrared you get, explains Neeser. For the purposes of this project, we don't need centimeter accuracy. We're happy with half a meter or so.
Thus, his experiment results in photographs showing six centimeters per pixel, which isn't enough to see individual leaves but does show rows and the area between them. After the UAS collects the photographs, the multiple images are stitched together to form an image of the entire field. Distortions are removed by adding ground reference points and Global Positioning System locations to the data. Neeser said his research is in the early stages, but he sees promise.
Image courtesy of The Western Producer/Barb Glenn.
