Exploring the accuracy of drone-applied herbicide treatments

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About Jason Deveau (Spray Guy)

Dr. Jason Deveau has been the OMAFA Application Technology Specialist since 2008. He researches and teaches methods to improve the safe, effective and efficient application of agricultural sprays in specialty crops, field crops and controlled environments. He is the co-administrator of Sprayers101, co-author of the Airblast101 Textbook, a slow cyclist and an even slower runner.

See all posts by Jason Deveau (Spray Guy).

In 2024, Corteva conducted a study entitled “Drone-Delivered Herbicides: Comparing Lontrel XC (Clopyralid) Efficacy Across Application Techniques and Water Volumes”. Their objective was to compare the relative efficacy of hand booms and drones, and to determine if drone efficacy was affected by low water rates. The researchers evaluated the area treated and the effective swath width by manually tracing the burned areas from an aerial NDVI image.

Interestingly, the study found that water volume had an insignificant impact on herbicide efficacy. But what really caught our attention was the inconsistent and variable shape of the treated area along each flight path (Figure 1).

Figure 1 – Image from work performed by Kevin Falk, Rory Degenhardt, Angela Fawcett and Neil Spomer, as presented at the 2025 Canadian Weed Science Society annual meeting in Vancouver, BC.

If the swath width fluctuates and vacillates along the flight path, then there is great potential for overlaps and misses throughout a treated area. Common practice is to rely on displacement (and drift) from upwind passes to deposit a sufficient cumulative dose of herbicide to mask areas of low coverage. This would be facilitated by consistent wind direction, higher altitudes, and a surface with little or no canopy to interfere with secondary deposition.

On the other hand, if the programmed swath width (i.e. route spacing) is too wide, and/or the the droplet size too large to permit sufficient displacement, then gaps in coverage would appear. And there is always the consideration of restricting the deposit to field boundaries and margins, particularly on the downwind side of the treatment area.

We explored these considerations by conducting a study that emulated aspects of Corteva’s work. We applied Roundup Transorb HC (a non-selective herbicide) instead of Lontrel (a selective herbicide specifically for broadleaf weeds). We used the DJI Agras T50 and the new T100 with two atomizers and a DJI RTK-2 base station, employing an array of operational settings. And, we flew multiple passes rather than a single pass for each treatment.

Part one of the study examined three programmed swath widths from both drones to compare their performances directly. Part two of the study evaluated the T100’s performance over a series of flight speeds and spray qualities. Burndown was evaluated using post-application orthomosaic images taken at 200 feet using a DJI M3M drone. Images were analyzed using Pix4D software.

Materials And Methods

Field Conditions

Applications took place in a 160-acre field of wheat stubble in Central Elgin, Ontario (42°45’29.3″N 81°05’58.9″W) on September 13, 2025.

Treatments

Each flight was centred on the right boundary of the treatment block, as indicated by a pin flag. Four passes were flown per treatment (i.e. two out-and-backs). There were no repetitions for treatments, so there was no need to randomize them.

Part One

The intent of this part of the study was to make a direct comparison of the swaths produced by the T50 and the T100. The T100 is heavier, has a larger volumetric capacity (100 L vs. 40 L) and is capable of faster flight (20 m/s or 64 km/h vs, 10 m/s or 23 km/h). We wrote about our first impressions of the T100, here.

Drone operational settings were selected to replicate those used in previous corn and wheat fungicide experiments with the T50. These settings are admittedly more restrictive (from the perspective of productivity) than those commonly used for herbicide applications. For example, and anecdotally, we have been told the T100 can spray a full section (~260 hectares or 640 acres) at 2.8 gpa and 20 m/s on one tank and one battery charge. However, we have no information about subsequent coverage, efficacy or off-target deposition.

Maintaining these operational settings allowed us to make a more direct comparison of herbicide vs. fungicide placement and efficacy. All applications were performed using a 250 µm spray quality (Table 1).

Treatment CodeRPASProgrammed Swath (m)Speed
(m/s, km/h)
Altitude (m)Volume (gpa)
AUnsprayed
BT5066, 21.635
CT5086, 21.635
DT50106, 21.635
ET50610, 3635
FT50810, 3635
GT50108, 28.8*35
HT10066, 21.635
IT10086, 21.635
JT100106, 21.635
KT100610, 3635
LT100810, 3635
MT1001010, 36*35
Table 1 – Part one: Drone settings. (*10 m/s was intended, but the T50’s pumps could not produce a 10 m swath at 5 gpa at that speed.)

Each treatment block was 150 m long, 50 m wide and a 20 m buffer was maintained between treatments (Figure 2). Two, 1 m scale indicators were placed in Treatment B to confirm scale during image analysis.

Figure 2 – Part one treatment layout.

Part Two

The intent of this part of the study was to explore the new drone design and its capabilities. Particularly, the impact of high-speed flight on swath width, displacement and drift. The DJI controller advises an altitude of 5 m or higher (likely a safety consideration). We felt this was too high for consistent coverage, and compromised by flying at 4 m (Table 2).

Treatment CodeRPASSpray Quality (µm)Speed
(m/s, km/h)
Altitude (m)Volume (gpa)
NT10050018.3, 65.843
OT10025018.3, 65.843
PT10080*12.5, 45*43
QT10025018.3, 65.843
RT10025015, 5443
ST10025010, 7243
Table 2 – Part two: Drone settings (20 m/s was intended, but the drone only reached a maximum of 18.3 m/s before slowing as it approached the end of the treatment. *50 µm and 20 m/s was intended, but the T100 controller would not permit those settings, so a compromise was made.)

Given the greater potential for displacement and drift in this part of the study, we established wider and longer treatments blocks, and wider buffers between treatments. Each treatment was 250 m long, 70 m wide and a 40 m buffer was maintained between treatments (Figure 3).

Figure 3 – Part two treatment layout.

Chemistry

The spray solution (PMRA research authorization 0054-RA-25) was premixed in a single batch. For part one, 80 L Roundup Transorb HC in 1,000 L water plus 0.05% Halt (defoamer). For part two, 700 L of the solution remained, so we added an additional 20 L of Roundup to approximately maintain the dose when dropping from 5 gpa to 3 gpa. Drones were refilled after each treatment (to 40 L for T50 and to 65 L for T100) to negate any weight effect on the magnitude of the downwash.

Weather

Weather data was collected using a Kestrel 3550AG weather meter (Kestrel Instruments) in a vane mount positioned 2.5 m above ground (Table 3). For part one, conditions were ideal: humid with a light wind in a consistent direction. For part two, afternoon wind speed increased, but predominant direction remained consistent (Figure 4A).

TimeExperiment PartTreatmentWeather
10:05 – 10:581B – G18.6 ̊C, 78% RH, 0.0 km/h wind.
10:58 – 12:401H – M19.8 ̊C, 72% RH, 2.0 km/h wind.
12:40 – 1:502N – S22 ̊C, 61.2% RH, 7.0 km/h wind.
Table 3 – Treatment times and weather conditions
Figure 4 – Left (a): Prevailing wind direction overlaid on orthoscopic image. Right (b): Polygons representing manual traces of the perimeter of the burned treatment areas. Areas are noted for each treatment.

Calculating Effective Swath Width

Traditional methods for calculating effective swath utilize a sampling system aligned perpendicular to the flight path. Whether dyes or water sensitive paper, this approach produces a coefficient of variation and some measure of over- and under-dose. The two methods used in this study are less quantitative, but the burned area clearly represents a threshold, efficacious dose, which is a commodity that can only be assumed using traditional sampling systems.

In the first method, the perimeter of the area burned was traced to create a polygon (above, in Figure 4B). Then, the average width of that area was established from measured spans along the block. Finally, that average was divided by the four passes. Hereafter referred to as the “treatment width ÷ passes” method. This method produces an estimate that likely underestimates the swath width because each upwind drone pass can overlap and hide any displacement (and drift) from the previous. It is included in this study for interest, and to compare the results to a second, and likely more accurate, method.

The second method overlays the flight path onto the area burned. The upwind side of the swath was determined from an average of at least five measurements along the upwind flight path. The downwind side of the swath was calculated the same way (Figure 5). Both the average upwind and downwind distances were added to arrive at the effective swath width. Hereafter referred to as the “port + starboard extent” method, this approach captures the cumulative downwind effect of multiple passes (representing a real-world situation) but still isolates the upwind side of a single pass.

Figure 5 – Example of port and starboard measurements along the downwind and upwind-most flight paths. The averages were calculated and added to estimate swath width.

Results – Part One

Planned versus Measured Treatment Area

Planned treatment areas were calculated from distance flown × programmed swath width × number of passes. Measured treatment areas were calculated by tracing a polygon along the perimeter of the area burned. In all cases, actual was larger than planned by an average 36.1%. The T50 treated 32% more area than planned and the T100 treated 40% more area than planned, or 8% more than the T50 (Figure 6).

Figure 6 – Planned and Measured Swath Widths for T50 and T100.

Programmed and Measured Swath Widths

In all cases, the “treatment width ÷ passes” method produced a swath width that was greater than, and positively correlated with, programmed swath width (Figure 7). For the T50, it was an average 26.8% wider. For the T100, it was an average 38.3% wider. The swath widths calculated by the “port + starboard extent” method were larger still, but were not positively correlated with programmed swath width. For the T50, it was an average 52.8% wider. For the T100, it was an average 62.5% wider.

No matter the method used to calculate swath width, the T100 exceeded the planned swath width by more than the T50. Using the “port + starboard extent” method, the average T100 swath width was 21.3 m, which is an average 15.4% wider than the average 17 m swath produced by the T50.

Figure 7 – Average measured swath width (two methods) compared to planned swath width for the T50 and T100 flown at 5 gpa, 3 m altitude, 250 µm spray quality and multiple speeds.

T50 Swath Width by Travel Speed

When travel speed becomes the independent variable for the T50, the “treatment width ÷ passes” method produces average swath widths that positively correlate with flight speed. At 21.5 km/h, the average swath was 10 m, increasing to 11.9 at 30-36 km/h (Figure 8). This is typical and expected as higher speeds have been shown to produce wider swaths with the T10 and T50.

However, the relationship between speed and swath width is less clear when calculated using the “port + starboard extent” method. At 21.5 km/h the average swath was 18.2 m, but reduced to 15.8 km/h at 30-36 km/h (Figure 8).

Figure 8 – Average measured swath width by speed for the T50.

T100 Swath Width by Travel Speed

When travel speed becomes the independent variable for the T100, neither method for calculating swath width show an effect from flight speed. The “treatment width ÷ passes” method produced average swath widths of 13 m at 21.5 km/h and 12.9 at 30-36 km/h (Figure 9). The “port + starboard extent” method produced average swath widths of 21.7 m at 21.5 km/h and 21 at 30-36 km/h.

Figure 9 – Average measured swath width by speed for the T100.

Results – Part Two

T100 Swath Width by Travel Speed

The effect of flight speed on treated area and swath width was examined. In each case, the treated area was significantly larger than the programmed area (Figure 10).

Figure 10 – Actual treatment areas compared to expected for the T100; three speeds.

Similar to Part one, travel speed did not appear to influence swath width in any consistent or significant way (Figure 11).

Figure 11 – Average swath width for T100 calculated using two methods at three speeds.

T100 Swath Width by Spray Quality

The effect of spray quality on treated area and swath width was examined. Once again, in each case, the treated area was significantly larger than the programmed area (Figure 12).

Figure 12 – Actual treatment areas compared to expected for the T100 using three spray qualities.

Swath widths from both calculation methods were negatively correlated with spray quality (Figure 13). Coarser droplets have greater mass, making them are less prone to displacement by wind than finer droplets. The “treatment width ÷ passes” saw an 80 μm spray quality produce a swath 46.8% larger than a 500 μm spray quality. The “port + starboard extent” method saw an 80 μm spray quality produce a swath 22.6% larger than a 500 μm spray quality.

Figure 13 – Average swath width for T100 calculated using two methods for three spray qualities.

Conclusions

The swath widths calculated from herbicide efficacy appear to be considerably larger than those observed in previous studies with fungicide efficacy, or using traditional assessment methods (e.g. swath gobbler or water sensitive paper). This is likely a function of herbicides having a lower threshold dose, and their application on bare earth or into sparse canopies permit the lateral spread of droplets. When even the slightest degree of coverage produces a visual effect, this binary result (hit or miss) will certainly extend the effective swath. This should also raise awareness surrounding the importance of field boundaries and margins.

The method for estimating effective swath width from an area treated with herbicide is an important consideration. The “port + starboard extent” method captures the cumulative downwind effect of multiple passes (representing a real-world situation) without masking that of the upwind side of a single pass. It follows that the “treatment width ÷ passes” method likely underestimates the swath width. This supposition is supported by the fact that in all cases, the former method produced a significantly greater swath width than the latter. Ultimately, both methods are simply models for assigning values to treated areas; they are not intended to replace traditional, quantifiable assessment methods.

When the same operational settings are used, the T100 consistently produced a wider swath than the T50. Using the “port + starboard extent” method, the average T100 swath width was 15.4% wider than that of the the T50.

The lack of any correlation between programmed swath widths (i.e. route spacing) and the relatively consistent widths calculated using the “port + starboard extent” method suggest we reached a threshold swath width of 17 m for the T50 and 21.3 for the T100. These figures should not be interpreted as recommendations for planning herbicide treatments. They simply reflect what was observed for this herbicide, for a given set of operational parameters, in specific atmospheric conditions with no replications. For example, when droplet size was increased to 500 μm, the swath width for the T100 was only 15 m.

The relationship between flight speed and swath width was unclear. Employing the “port + starboard extent” method, the T50 produced shorter swaths at higher speeds, which is the reverse of what has been seen in previous swathing studies with the T10 and T50. Speed appeared to have no effect on the swaths produced by the T100. In this case, perhaps it’s capacity for higher speeds has allowed the T100 to pass beyond transitional lift into true flight, like a helicopter.

According to Transport Canada, transitional lift is a phenomenon that occurs then a helicopter gains speed and height after the hover. It is present with any horizontal flow of air across the rotor and most noticeable when the airspeed reaches 16 – 24 knots flight (8.25 – 12.8 m/s or 30 km/h – 46 km/h). This would greatly reduce the effect of the downwash on droplet movement. In our first impressions of the T100, we found that flying slower overheated the battery, implying that it was far more efficient at higher speeds (i.e. implying transitional lift and true flight).

This is a new development for rotary drones, which were not previously capable of reaching these speeds. Downwash was an unavoidable side effect of the flight of rotary drones, but may now be a tool for the operator to use as the situation warrants – battery temperature notwithstanding.

Swath widths from both calculation methods were negatively correlated with spray quality. Coarser droplets have greater mass, making them are less prone to displacement by wind than finer droplets. Fortunately, weather conditions during this study were conducive to droplet survivability. The finer droplets were able to impart a greater biological effect over a wider span than might under hotter, drier and windier circumstances. Using coarser droplets is an effective means for reducing drift and maintaining a consistent swath width over a greater range of environmental conditions.

Acknowledgements

Adrian Rivard and Stuart Hunter (Drone Spray Canada), Adam Pfeffer (Bayer Canada) and Mike Cowbrough (Ontario Ministry of Agriculture, Food and Agribusiness) are gratefully acknowledged for their participation, and both in kind and financial support of this study. Thanks also to Mark Ledebuhr for discussions surrounding the interpretation of these results.

Author

  • Dr. Jason Deveau has been the OMAFA Application Technology Specialist since 2008. He researches and teaches methods to improve the safe, effective and efficient application of agricultural sprays in specialty crops, field crops and controlled environments. He is the co-administrator of Sprayers101, co-author of the Airblast101 Textbook, a slow cyclist and an even slower runner.

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