Specs can tell you a lot about an agricultural drone. Field data can tell you what those specs look like in practice.
Recent Super User testing in Fargo, North Dakota, put that into perspective. The field work looked at two questions that matter to applicators: what happens to spray coverage and deposition at different carrier volumes, and how Black Betty Stacked C31 and the DJI T100 perform when application efficiency is measured in the field.
The results gave us plenty to look at.
The Super User Program brings together agricultural drone operators from across the United States who test technology across different crops, conditions, and terrain, then share their findings. For us at Ceres Air, that real-world data helps us better understand how equipment performs in the field, what operators are experiencing, and where there are opportunities to keep improving.
Here’s what stood out to us in the latest results.
The sugar beet testing looked at spray coverage and deposition across four treatments, measured in gallons per acre (GPA):
Each treatment used the same fluorescent dye rate of 30 grams per acre, providing a consistent tracer for evaluating deposition throughout the crop canopy.
Water-sensitive paper and Mylar sampling cards were positioned at upper, middle, and lower canopy levels. Water-sensitive paper measured spray coverage and droplet hits, while fluorescent tracer collected on the Mylar cards provided a measure of deposition.
The difference between those two measurements became important when the results came back.
As expected, the higher-volume ground applications produced substantially greater surface coverage on the water-sensitive paper than Black Betty (C31) at 3 and 5 GPA.
Applying 15 or 20 GPA puts considerably more carrier volume into the canopy than applying 3 or 5 GPA, and that difference was visible in the coverage results.
But coverage wasn’t the whole story.
When the team looked at fluorescent tracer deposition, more water didn’t automatically mean more measured deposition.
Black Betty (C31) applications at 3 and 5 GPA produced tracer deposition similar to the 15 GPA ground treatment at several sampled canopy positions. The 20 GPA ground treatment produced high water-sensitive-paper coverage but lower measured tracer deposition on the Mylar sampling surfaces at several positions.
For us, that is one of the most interesting findings from the sugar beet testing.
It shows why evaluating an application based only on how wet the crop looks can leave out an important part of the picture.
In discussing the results, Super User Cory Palm, owner and chief pilot of Pro Ag Solutions in Bismarck, North Dakota, identified runoff as one possible explanation for some of the 20 GPA findings, although the testing did not establish the cause. Rob Prunty of North Dakota State University (NDSU) Extension also noted an important limitation: Mylar cards and sugar beet leaves have different surface characteristics.
The trial measured spray coverage and tracer deposition, not biological efficacy or disease control. These results shouldn’t be interpreted as proof that one carrier volume will produce better crop protection than another.
What they do show is that more visible coverage did not automatically mean more measured deposition.
Spray reached the upper, middle, and lower canopy positions across the treatments, but the pattern changed depending on what was being measured.
In interpreting the canopy results, Cory identified rotor wash as one factor that may contribute to spray movement through the canopy during drone applications. The testing wasn’t designed to isolate rotor wash, so it can’t establish it as the cause of the observed results.
Field conditions also played a role. Wind varied during testing, and movement in the spray pattern was visible in the field data.
For applicators, the takeaway is straightforward:
GPA alone doesn’t tell the whole application story.
Coverage, deposition, canopy position, droplet characteristics, environmental conditions, and aircraft dynamics all influence what happens after spray leaves the aircraft or boom.
The second set of Super User data looked at application efficiency with Black Betty (C31) and the DJI T100.
The aircraft were tested on 40- and 80-acre fields, with the trailers positioned to maintain the same ferry distance. Actual takeoff, landing, and refill times were included rather than relying only on theoretical application rates.
The first two tests used matched operating parameters.
Both aircraft operated at 3 GPA, a 32-foot swath, and 40 mph.
|
Field Size |
Black Betty (C31) |
T100 |
|
80 acres |
91.52 ac/hr |
84.71 ac/hr |
|
40 acres |
82.76 ac/hr |
73.21 ac/hr |
The second test moved both aircraft to 2 GPA and a 35-foot swath while maintaining the 40 mph operating speed.
|
Field Size |
Black Betty (C31) |
T100 |
|
80 acres |
111.63 ac/hr |
102.89 ac/hr |
|
40 acres |
98.77 ac/hr |
88.67 ac/hr |
Across all four matched-condition measurements, Black Betty (C31) recorded the higher application rate.
At 2 GPA on the 80-acre field, both aircraft exceeded 100 acres per hour, with Black Betty recording 111.63 ac/hr and the T100 recording 102.89 ac/hr.
Both aircraft also recorded higher application rates on the 80-acre field than on the 40-acre field. That’s a useful reminder that acres per hour isn’t a fixed aircraft specification.
These figures reflect the specific field layouts, application parameters, and operating conditions tested. Field size, turns, ferry distance, and refill requirements all affect realized application capacity.
For the third efficiency test, the Super Users moved away from matched swaths and used the effective swath identified for each aircraft through string testing.
Both aircraft operated at 3 GPA and 40 mph. The T100 used a 32-foot effective swath, while Black Betty (C31) used a 39-foot effective swath.
Across the 80-acre test:
Unlike the first two tests, this wasn’t a matched-swath comparison. Instead, it shows what happened when each aircraft operated using the effective swath identified through previous testing.
For Black Betty, the combination of its 39-foot effective swath and operating parameters translated into greater application capacity under these test conditions.
When the Black Betty (C31) results were standardized to a one-minute refill, the calculated application rate increased to 117.98 ac/hr. This was a calculated result based on the recorded field data, not a separately measured field result.
One of the most useful lessons from the testing had less to do with flight and more to do with what was happening at the trailer.
During the tests, the Black Betty (C31) crew averaged approximately 80 to 90 seconds per refill, while the T100 crew averaged approximately 55 to 60 seconds.
Black Betty was being refilled through a one-inch hose, which Cory identified as a constraint on how quickly its larger tank could be refilled.
That matters because a higher-capacity aircraft can only deliver on that capacity if the ground operation is ready to support it.
Acres per hour isn’t just about the aircraft. It’s about the entire operation.
That’s an important consideration when evaluating which spray drone is the right fit. Specs matter, but field data shows how those capabilities translate into real-world application efficiency.
What makes the best spray drone for one operation won’t necessarily make it the best fit for another.
The Super User testing shows why effective swath, application rate, refill infrastructure, field size, and ferry distance all belong in that decision.
In these tests, Black Betty (C31) recorded the higher application rate across all four matched-condition measurements. When each aircraft operated at its identified effective swath, Black Betty recorded 111.58 ac/hr compared with 84.53 ac/hr for the T100.
Those results give operators additional field data to consider when evaluating application efficiency.
The testing also highlighted an opportunity on the ground. Black Betty’s larger capacity came with longer refill times under the setup used in Fargo. Improving the refill workflow could help the ground operation better keep pace with the aircraft’s capacity.
There isn’t one number that determines the best spray drone for every operation. The better question is how the aircraft, application parameters, and ground system work together for the acres you need to cover.
The Super User results give us a clearer picture of what happens when application performance is measured in real field conditions.
The sugar beet testing showed that substantially greater visible coverage from the higher-volume ground applications didn’t automatically translate into greater measured tracer deposition.
In the efficiency testing, Black Betty (C31) recorded the higher application rate across the matched-condition measurements and again when each aircraft operated at its identified effective swath.
The testing also showed where there’s more to learn and more opportunity to improve, from understanding unexpected deposition patterns to making sure ground infrastructure can keep pace with aircraft capacity.
That’s where Super User data is especially valuable.
Super Users put equipment to work, collect data, and share what they find. For us at Ceres Air, that information does more than show how Black Betty performs today. It helps us understand what operators need, where workflows can improve, and where we take the technology next.
That’s the value of putting the data to work.