New drone technology and LiDAR sensors create detailed maps of farm fields, allowing for broad scientific approach
The Ag in Motion Discovery Farm was recently used to fly the latest in state-of-the-art drone technology.
It’s a remotely piloted aircraft system, which was recently added to Saskatchewan Polytechnic’s fleet of eight aircraft used for training and applied research.
“We went to this newest platform with cutting edge light detection and ranging system that just totally revolutionizes what we can do,” said Dave Halstead, research chair in the School of Natural Resources and Built Environment at Saskatchewan Polytechnic.
LiDAR systems use Class 1, eye-safe lasers to direct pulses of light towards the ground. Based on precise positioning of the LiDAR unit and the time required to receive reflected pulses, they are able to accurately map ground-based features.
The new RPAS is called Quantum Trinity and made in Germany. It’s outfitted with the Yellowscan Qube 240 LiDAR, which generates 240,000 of these pulses every second and can receive as many as three reflected returns for each pulse. As might be expected, the data requirements are significant and a single mission can often generate 50 to 100 GB of data.
Its flight time is about 60 minutes but can reach up to 90 minutes, a huge leap forward from older drones that could only stay in the air for about 12 minutes.
Halstead said the extended range opens up many new applications to use specialized sensing and mapping technology for applied research and training in the agriculture, forestry and mining sectors.
“There’s so much that we can do in terms of landscape modelling and analysis and just straightforward data capturing,” he said.
The Quantum Trinity is a fixed wing with vertical takeoff and landing capability.
The two propellers can swing vertically to lift straight up. Once airborne the RPAS can tilt its rotors forward and fly in a fixed-wing format.
A seven-foot wingspan provides the lift the ability to handle winds and stay aloft — key to the long flight time.
The payload contains the LiDAR, tucked in the belly of the fuselage and does not produce drag compared to fixed rotary wings in older drone technology, said Halstead.
A high-resolution, 42-megapixel camera also fits into the payload compartment that will provide strong mapping capabilities as well as looking for invasive plant species.
“We just switch that payload compartment. We have one with the LiDAR and one with the camera. You just snap in the different payloads,” he said.
The battery is a 9.4 volt direct current 21.67 watt system housed in the plane’s nose cone.
“The real hero in this isn’t the battery but rather the aircraft design; very aerodynamic with good wingspan and high lift to load ratio,” he said.
Another useful feature of the LiDAR system is the ability to measure drainage at subtle levels in areas like Regina plains, or during a flood event such as Prince Albert in 2013.
“Nobody knew where that flooding was going to occur because the ground was so flat around that area on the highway that was submerged. In flat areas it can sometimes be really difficult to know where the water is going to drain in those kinds of events. This thing measures centimetre type variations in the ground, so you’ve got really powerful ability to measure elevation,” he said.
The new RPAS was recently used at the Discovery Farm to generate a digital elevation model of a research project on water drainage. A detailed map of landscape topography and water drainage characteristics was produced using LiDAR.
“With this technology, we will be able to map the topography of our research field sites at an incredibly high resolution. We’re hoping to tease out how small changes in topography across the field influence how effective management practices are at optimizing phosphorus-use efficiency by the crop and limiting nutrient losses in run-off water,” said Blake Weiseth, applied research lead for the Discovery Farm and agriculture research chair with Sask Polytech.
The technology is currently available for research purposes, but eventually could be accessed by an agronomist or farmer to identify stress in a crop due to disease, insects, or weeds and use that information to inform pesticide application decisions he said.