Swath width is a fundamental parameter in spray drone mission planning. It facilitates the uniform application of broadacre pesticides at the target rate. Pilots adjust the swath width via operational settings such as droplet size, flight speed and altitude to produce the most effective and efficient application.
Rapid advances in drone design, however, may warrant a re-evaluation of how operational settings affect swath width. For example, the most recent generation of drones are now capable of speeds up to 20 m/s (72 km/h), which is twice that of the previous generation.
In late 2025 we conducted a series of comparative herbicide applications using the DJI Agras T50 and T100. For both drones, swath width increased with speed up to ~10 m/s, as expected. However, between ~10 m/s and 18.5 m/s, swath width from the T100 did not seem to increase further. Similar observations have been reported by researchers at AgroEfetiva (São Paulo, Brazil; personal communication).
These results suggest that the relationship between speed and swath width is positive and direct at lower speeds, but reaches a saturation point beyond which any further increase in speed no longer affects swath width. This is an asymptotic relationship. To test this hypothesis, we conducted a deposition study where swath width was measured at flight speeds that increased incrementally from 8 m/s to 20 m/s.
Configuration Study
The standard T100 configuration uses two rotary atomizers (“sprinkler” nozzles; LX07550SX) with a reported maximum combined flow rate of 30 L/min. The alternate orchard configuration incorporates a boom that supports two additional “mister” nozzles (LX09550SX), increasing the reported maximum flow rate to 40 L/min.
To improve productivity in broadacre applications, some operators have adopted a hybrid configuration. In this setup, the orchard boom is retained, but the reputedly drift-prone mister nozzles are replaced with a second set of sprinklers. This approach is intended to achieve a higher flow rate than the standard two nozzle configuration while maintaining a larger mean droplet size.
A secondary objective of this study was to compare the Hybrid configuration with the Orchard configuration (Figure 1).
Figure 1 – Left: DJI Sprinkler Nozzle (LX07550SX). Four such nozzles comprised the “Hybrid” configuration. Right: DJI Mister nozzle (LX09550SX). Four such nozzles comprised the “Orchard” configuration.
Materials and Methods
Location and Layout
The study was conducted at Ontario’s Simcoe Research Station on May 12, 2026. The site (42.857414, -80.271759) was a flat, recently tilled sand/loam field with no vegetation present. A DJI Agris T100 drone was used to perform the spray applications, supported by the D-RTK 3 relay station and flown on full auto.
The spray mix was 0.2% v/v Super Signal Blue (Precision Laboratories) and 0.125% v/v Activate Plus NIS (Winfield United) in municipal water, pre-mixed to ensure consistency. A volume of 40 L – 60 L was maintained throughout the trial to minimize the effect of a changing payload.
The sampler was a flat, horizontal, continuous bond paper strip measuring 7.5 cm wide and 30 m long (secured in Speed Tracks™, Application Insight LLC). The sampler was oriented perpendicular to the prevailing wind, with the intention of flying the drone with a headwind across the 15 m mark (the centre) (Figure 2). Test passes determined that the T100 required 210 m to reach 20 m/s while half-full.
Figure 2 – T100 spraying indicator dye across the 30 m continuous sampler.
Before the swathing runs began, the prevailing wind shifted direction slightly. It was decided to fly the drone 5 m upwind (at the 20 m mark along the 30 m sampler) to ensure any downwind displacement was captured on the sampler (Figure 3).
Figure 3 – Trial layout and prevailing wind conditions.
Drone Settings and Swathing Order
The primary objective of the study was to explore the effect of flight speed on swath width. Speed was increased from 8 m/s to the maximum 20 m/s by 2 m/s increments. Trial and error with the controller indicated that we could achieve these speeds by balancing an application volume of 30 L/ha and a programmed swath width of 7 m.
Altitude was set to 4 m which is lower than the 5 m minimum recommended by DJI for high-speed flight. This was a compromise above the preferred 3.5 m altitude we have historically used with the T50. It was felt that higher altitudes would create unacceptable potential for swath displacement.
Rotary atomizer design is not standardized, and as a result, the droplet size selected on the controller did not necessarily produce the desired results. The Hybrid configuration was programmed to emit 350 µm droplets, selected as a compromise between drift mitigation and coverage potential. To offset the Mister nozzles’ reputation for producing a finer spray, the Orchard configuration was set to the maximum 500 µm. Operations settings are noted in Table 1.
Configuration
Nozzle
Droplet size (µm)
Speed (m/s)
Altitude (m)
Programmed Swath Width (m)
Application Volume (L/ha)
Hybrid
4 Sprinklers
350
8, 10, 12, 14, 16, 18, 20
4
7
30
Orchard
2 Misters, 2 Sprinklers
500
8, 10, 12, 14, 16, 18, 20
4
7
30
Table 1 – Operational settings for trials.
Three repetitions of seven speeds were flown for each configuration. Anticipating an increase in temperature and wind speed throughout the day, it was decided move through all seven speeds (a single repetition) before resetting and doing so two more times. The intent was to preclude confounding weather effects. Ideally, we should have alternated between configurations as well, but this proved impractical. As a result, we flew the Hybrid configuration first and the Orchard configuration last.
Weather
Weather data was collected using a Kestrel 3550AG weather meter (Kestrel Instruments) in a vane mount positioned 2.5 m above ground. Temperature and relative humidity were comparable throughout the ~3 hours of data collection, but as anticipated, wind speed was higher for the later Orchard configuration passes.
As previously indicated, wind direction shifted from an ideal headwind situation just before trials began, and was somewhat changeable, but the average wind direction for the two configurations was comparable (Figure 4 and Table 2).
Figure 4 – Weather conditions recorded at roughly 10-minute intervals, corresponding to the drone passing over the sampler.
Time
Configuration Flown
Average Temperature (°C)
Average Relative Humidity (%)
Average Wind Speed (km/h ± SD)
Average Direction (° ± SD)
11:55 am – 1:16 pm
Hybrid
11.7
59.9
8.4 ± 3.5
323 ± 46
1:43 pm – 2:52 pm
Orchard
12.9
51.6
11.9 ± 2.5
316 ± 48
Table 2 – Average weather conditions for the Hybrid configuration passes and for the Orchard configuration passes.
Collector analysis
Bond paper digitization
Bond papers were scanned using a Swath Gobbler™ (Application Insight LLC). The software measured deposition as both percent area covered (% area) and deposit density (deposits/cm2) every 100 mm, with a thresholded Hue of 23-280, a Saturation of 5-120 and a Value of 156-255.
Effective swath width calculation
The large data set produced by each pass was reduced in size by averaging the deposition for every 50 cm. This data was entered into our Excel-based swath width calculator, which assumes a racetrack pattern and sums deposits from adjacent swaths. The resulting swath width for each pass was the maximum width that minimized over- and under-dosing as well as the coefficient of variation (CV).
Analysis
The average swath width derived from deposit density data was wider than that derived from percent area covered (Table 3).
Table 3 – Group means and standard deviation for average swath widths derived from deposit density data and percent coverage data. The average CV was between 29 and 32%.
A two-way ANOVA (Analysis of Variance; α = 0.05) was performed to determine any significant effect of speed or configuration on swath width (Table 4). Flight speed had no significant effect on swath width, no matter how it was derived (% area covered or deposit density), for either configuration (Hybrid or Orchard). However, the average swath width derived from deposit density was significantly wider for the Orchard configuration compared to the Hybrid and presented higher variability.
Table 4 – Results of two-way ANOVA, exploring interactions between speed, swath width and configuration (95% confidence interval).
Orchard configuration was prone to displacement in a side wind. Shifting the flight path 5 m upwind improved the downwind capture, but for some flights it did trim a small portion of the upwind deposition. Deposit density gives greater resolution and exposes more variability than percent area covered. Figure 5 shows the average deposition by speed based on deposit density. Figure 6 shows the average deposition by speed based on percent area covered.
Figure 5 – Average deposition by speed based on deposit density. Arrow indicates flight path.Figure 6 – Average deposition by speed based on percent area covered. Arrow indicates flight path.
Figure 7 shows the average deposition by configuration based on deposit density. Figure 8 shows the average deposition by configuration, based on percent area covered. Based on deposit density, there were 55% more deposits on the downwind side of the sampler for Orchard configuration set to set to 500 microns compared to the Modified configuration set to 350 microns.
Figure 6 – Average deposition by configuration based on deposit density. Arrow indicates flight path.Figure 8 – Average deposition by configuration based on percent area covered. Arrow indicates flight path.
The average swath widths calculated from deposit density (Figure 9) and percent area covered (Figure 10) are shown with standard deviation. While it appears the swath width is less around 14 m/s, it is statistically insignificant and the response to speed is essentially flat. As with prior studies, swath widths calculated from deposit density are larger than those calculated from percent area covered.
Figure 9 – Average swath widths for each speed, derived from deposit density data. SD shown. n=3 for each speed, while n=2 for Hybrid configuration at 12 m/s and 14 m/s.Figure 10 – Average swath widths for each speed, derived from percent coverage data. SD shown. n=3 for each speed, while n=2 for Hybrid configuration at 12 m/s and 14 m/s.
Observations
Previous studies demonstrated a direct and positive relationship between drone speed and swath width up to 8-10 m/s. Here, we see no further response after ~8 m/s. This supports the hypothesis that rotary-wing drone speed and swath width share an asymptotic relationship that inflects at ~8-10 m/s. Variability makes it difficult to determine an exact value.
Despite increasing the programmed droplet size to the maximum 500 microns for the Orchard condition, there was 55% more downwind deposition compared to the Hybrid condition, which was set to 350 microns. This supports the claim that the Mister nozzle produces a span of droplet sizes that include far more fines than the Sprinkler nozzle, and underpins the need for a better understanding of the spray quality produced by rotary atomizers.
Spraying at high speeds is not an advisable practice. While swath width is no longer affected after ~8 m/s, there are other considerations. Note that it required 200 m for the drone to reach the highest speed, and in a related study we have seen swath width taper during initial acceleration and final deceleration, leaving gaps in coverage.
Further, the minimum 5 m altitude advised by DJI ensures a safe margin for the drone to respond to obstacles and topography during high speed flight, but is not conducive to spraying. The author is aware of a situation where flying the T100 at 4 m altitude and 18 m/s over a canola field with rolling hills caused it to perform an emergency landing.
An ideal speed is one that maintains the most consistent swath width at a reasonable altitude.
Thanks to Drone Spray Canada and Bayer Canada for in kind and financial support, and thanks to Cesar Cappa, OMAFA horticulture weed specialist for his participation in the study.
It can be challenging to illustrate how a drone deposits spray. We can take a cross-section of spray deposit (that is, the swath) using a continuous sampler, or a series of discrete samplers. This typically reveals a sharply-peaked curve that is symmetrical in a head- or tail-wind, or skewed if there is a cross-wind.
Spray coverage from a DJI T100 (4 m altitude, 30 L/ha, 16 m/s, 350 µm droplets) measured as percent area covered over a 30 m continuous sampler (Speed Track and Swath Gobbler – Application Insight LLC). Note: the prevailing wind was 8 km/h and generally from the right, skewing deposition to the left.
But this analysis only captures a slice of the swath at a single moment in time. Swath width and relative position along the flight path is inconsistent and highly variable. Some have shown this using herbicides and aerial photographs analyzed through a Normalized Difference Vegetation Index (NDVI). NDVI reveals the health and density of vegetation based on how they reflect different wavelengths.
A 50 m continuous swath visualization. This stitched graphic from work by K. Falk shows annotations for upwind/downwind edges and width measurements.
Design
We wanted to try something similar, but using dye instead of herbicide. Rhodamine WT is a synthetic fluorescent dye that absorbs green light and emits red light. It’s highly water-soluble and is the industry standard for environmental monitoring, including mapping groundwater flow, tracing pollution plumes and studying flow rates in rivers. We’ve used it in the past on Ontario’s snowy fields to show how boom height affects coverage uniformity with boom sprayers. Why not from a drone?
Spray coverage from a boom at an optimal height (left), too low (middle) and too high (right). From episode seven of our Exploding Sprayer Myths series.
The idea was to lay down swaths from a DJI T100 at different altitudes and speeds, and then take aerial photographs of the stained snow using a DJI Mavic 3 Multispectral drone. It would be visually impactful, and perhaps we could even quantify the deposition using photogrammetry and drone mapping software.
We flew a snowy field on Stefina Line, Blenheim, Ontario. It was 2°C and wind was gusting from 8-14 km/h. We mixed a 0.5% v/v solution of 0.5 L rhodamine WT in 900 L and flew a trial run (4 m altitude, 50 L/ha, 10 m/s, 500 µm droplets). But, as Robbie Burns said, “The best laid plans of mice and men [often go awry]. (If you’re not into late 18th century poetry, you’re forgiven.)
Rhodamine WT applied by drone did not result in high-contrast deposition. Hoping it was a function of concentration, we emptied the remaining rhodamine into the nurse tank, essentially doubling the concentration (we didn’t measure it), but to no avail.
Rhodamine WT applied by drone did not result in high-contrast deposition.
New Plan
We decided to pivot. We laid out a ~2.0 ha (5 ac) area and programmed the drone to fly a 10 m route spacing. Our hope was that the swaths would overlap for complete coverage, and perhaps we’d see a completely-stained rectangle. But, it seemed striated rather than solid, and the faint colouration made it difficult to see. This led us to shift to another location and spray another block, but this time we flew it four times in the hope that “multiple coats” would result in a brighter stain. We were already there, so why not?
Feeling a bit underwhelmed, we slowly flew the Mavic M3M to capture aerial footage. In the following image you see the less-than-spectacular result in the visual spectrum (left). Then our operator had the bright idea to filter the spectrum to enhance the rhodamine, and suddenly we had something (right). By reducing the green light, the rhodamine glowed gold, and we could see the faint block (sprayed once) and the much more intense block (sprayed four times).
(Left half) Image from DJI Mavic M3M stitched together in Pix4D. Faint pink colouring can be discerned in the visual spectrum. (Right half) when green is reduced, the rhodamine glows gold on a red background. We begin to see the faint ~2.0 ha (5 ac) block sprayed with a single pass on the right, and the much brighter ~2.0 ha (5 ac) block sprayed 4x on the left.
Note that the block is not a uniform, gold rectangle. The deposition is concentrated along the flight paths and does not seem to overlap (or even reach) the spray from adjacent passes. While this might be a resolution issue, it implies the swath width was less than the route spacing.
Then we zoomed-in on the downwind and upwind edges of the block that received multiple passes and noticed something. Given the variability of spray deposition, and the fact that we made four passes, we expected coverage would be blurred. Instead, there were clear, intense tapers where the drone was either accelerating or decelerating at the boundary (depending on the direction it was flying).
Close-up of the downwind edge of the ~2.0 ha (5 ac) area sprayed four times. Note the intensity and tapered profile of the swaths as they approach the boundary.Close-up of the upwind edge of the ~2.0 ha (5 ac) area sprayed four times. Note the intensity and tapered profile of the swaths as they approach the boundary.
We know from prior work with the T10, T30 and T50 that swath width and travel speed are directly related up to a certain speed (perhaps around 10 m/s, but this is yet to be determined). The practical implication was made clear in these images: there were significant gaps in coverage on two field margins. How long were the gaps? Did relative wind direction matter? Did this change with acceleration or deceleration? We went to the DJI flight record to find out.
Using the DJI telemetry data (specifically, the playback feature), we could see the flow rate relative to the drone speed and geographical position (that is, latitude and longitude). For each pass we established the point where the drone started or stopped spraying, and the point where the the target flow rate and speed were constant. Then, using the latitude and longitude, we calculated the intervening distance between coordinates.
Screen captures from the DJI flight recorder. (Left) The ~2.0 ha (5 ac) sprayed four times. You can see the drone icon displaying its position on the flight path, as well as the flow rate, altitude, speed and longitude (latitude was cropped out of the image). (Right) A close-up of one of the turns at the lower side of the sprayed block. All four passes are traced.
Analysis
We should note that we were able to spray just under 5 ac with a single tank, so the area that received four passes required multiple refills. This means the weight of the drone at any given location (and therefore the strength of the downwash) changed throughout the trial. We feel weight would affect acceleration and deceleration distances, but it wasn’t controlled in this study.
Most runs showed the acceleration distance was significantly greater than deceleration distance (Paired t-test: t = 2.54, p = 0.044).
Pass
Relative Velocity
Headwind Distance (m)
Tailwind Distance (m)
1
Acceleration
–
54.4
1
Deceleration
–
44.1
2
Acceleration
57.1
–
2
Deceleration
43.4
–
3
Acceleration
–
44.3
3
Deceleration
–
38.9
4
Acceleration
54.8
–
4
Deceleration
64.6
–
5
Acceleration
–
52.2
5
Deceleration
–
37.7
6
Acceleration
56.7
–
6
Deceleration
46.7
–
7
Acceleration
–
55.9
7
Deceleration
–
43.5
Headwind increased both acceleration and deceleration distances. There was a 7.5 m (16%) increase in a headwind, which was significant (r2 = 0.23). There was a stronger effect on deceleration, although not significantly.
WindDirection
Mean (m)
Std Dev (m)
Min(m)
Max (m)
Count
Headwind
53.9
7.7
43.4
64.6
6
Tailwind
46.4
7.0
37.7
55.9
8
Relative Velocity
Headwind Mean Distance (m)
Tailwind Mean Distance (m)
Acceleration
56.2
51.7
Deceleration
51.6
41.0
Conclusion
In a previous trial with the DJI T100 we noted the drone was not able to exceed 18.3 m/s over a 250 m treatment distance. It took roughly 200 m to get up to 18.3 m/s before the drone began to slow in anticipation of the end of the treatment block. At the time we assumed the drone would not be under- or over-applying because the pump flow rate compensated for a changing travel speed.
However, we had not considered the effect on swath width. Here, we see the T100 requires about 50 m to accelerate to / decelerate from 10 m/s. This distance is a function of wind direction, and to a lesser extent, whether the drone is accelerating or decelerating. During this time, the swath width is less than when at target flight speed, leaving (larger) gaps between passes.
This is a concern for broadacre applications where consistent, uniform coverage is required. Consider a fungicide that may no longer be effective at the extremes of the swath, or a systemic post-emerge herbicide where crops directly beneath the drone (especially horticultural crops or GMO’s with stressed metabolisms) could experience phytotoxic damage.
We have no practical work-around to suggest, but operators should be aware of the effect.
Thanks to Drone Spray Canada for in kind support and access to flight records, and thanks for Cesar Cappa, OMAFA horticulture weed specialist for his participation in the study and assistance with data analysis.
Remote Piloted Aerial Application Systems (RPAAS) or Unmanned Aircraft Spray Systems (UASS) are generally referred to as drones. They are an increasingly common tool for pesticide delivery in modern agriculture. They offer flexibility and access to difficult terrain, are capable of broadacre and patch applications, and facilitate air-assisted applications over perennial canopies. As with all application technologies, careful attention to fundamentals, safety, stewardship, and regulatory compliance remain the cornerstones of responsible use.
This document summarizes the state of the Canadian legal environment at the time of writing, and current best management practices for pesticide handling and application using drones. It is intended to support training and adoption for operators from a wide range of backgrounds. Given the rapid evolution of drone design and the changing regulatory landscape, key considerations are addressed without being overly prescriptive.
Drone best practices are, ultimately, a matter of context and compromise.
1. Categorization and the Canadian Legal Environment
Drones can be divided into three design categories (Figure 1):
Rotary-Wing: Single or multi-rotor, these drones employ vertical take-off and landing (VTOL) and can hover during spraying. They have relatively short flight times and low volumetric capacity.
Fixed-Wing: Resembling crewed airplanes, these drones require a runway for take-off and landing. They have relatively long flight times, operate at higher speeds and have more volumetric capacity.
Hybrid: Encompassing a range of designs including, for example, parasail-wing and VTOL-wing, this design combines aspects of rotary drones with the speeds, flight times and volumes of fixed-wing designs.
Figure 1 – Common drone designs.
Drones are also categorized by weight, which is used to define their legal use:
Small Drones (250 g to 25 kg): Typically have tank sizes up to 12 liters and speeds less than 25 km/h (10 m/s).
Medium Drones (25 kg to 150 kg): Typically have tank sizes ranging from 12 to 70 liters and a maximum speed of 25 km/h (10 m/s).
Large Drones (>150 kg): Typically have tanks >70 liters and a maximum speed of 72 km/h (20 m/s).
Pesticide use is regulated by both federal and provincial governments to protect human health and the environment. Anyone applying pesticides must ensure they are registered for use in Canada and must comply with all applicable federal and provincial/territorial requirements. Provincial rules vary, and it is the responsibility of the drone operator to understand and follow the requirements in their jurisdiction.
Transport Canada: Certification
Drone pilots must follow Canadian Aviation Regulations (CARS) Part IX. Drones must be registered and marked, and the pilot must carry valid pilot’s certification.
Table 1 – Drone Operational Categories and Pilot Certificates
Basic
Advanced
Level 1 Complex3
Age minimum for certification1
14
16
18
Fly in visual line-of-sight
Y
Y
Y
Closer to or over people2
N
Y
Y
Small drones
Y
Y
Y
Medium drones
N
Y
Y
Large drones4
N
N
N
Controlled airspace (air traffic control permitted)
N
Y
Y
Sheltered operators (small drones only)
N
Y
Y
Extended visual line-of-sight
N
Y
Y
Beyond visual line-of-sight
N
N
Y
1In Canada, you must be at least 16 years old to apply pesticides. 2Flying at an advertised event is considered a special operation, requiring permission. 3Operating a drone over 150 kg in Canada is classified as a high-complexity, specialized operation requiring a Special Flight Operations Certificate (SFOC) from Transport Canada. 4Operations with large drones are medium-complexity special operations and require SFOC permission.
Transport Canada has complied a list of drones that meet their RPAS Safety Assurance Standards to conduct specific operations. Pilots must declare their drone for advanced or Level 1 Complex operations, and any modification to RPAS components or configurable elements must also be declared to ensure the drone complies with Canadian Aviation Regulations section 901.194 and Standard 922.
Health Canada: Pesticide Labels
Health Canada is responsible for approving the registration of pesticides across Canada. Pesticide labels are legal documents and set rules on how a pesticide can be used. They define application rates, equipment settings, mixing instructions, environmental precautions, personal protective equipment (PPE), restricted-entry intervals, and disposal instructions.
On June 30, 2026, Health Canada released Science Policy Document SPN2026-02, allowing pesticide application by drones for most products currently registered for aerial application. At present, on pesticide labels that do not specifically mention RPAS application, users must follow all label directions for aerial application, with no changes to spray volume, application rate, droplet size, spray buffer zones, or any other conditions of use specified on the label.
This decision tree (figure 2) is not definitive, but it can be used to navigate a Canadian pesticide label to determine whether spraying by drones is permitted and which restrictions and settings might apply. Try our RPAS label search engine, here.
Figure 2 – Decision tree used to determine which Canadian labels permit application by drone and the pertinent restrictions.
Provincial / Territorial Regulations
Before using drones to apply pesticides, pilots and crew members must ensure they understand provincial or territorial requirements, complete any training and certification required, and obtain any applicable licenses, permissions and permits for pesticide use.
Questions regarding product label interpretations and uses can be directed to the Pesticides Information Service at pesticides-info@hc-sc.gc.ca.
2. Mission Planning and Logistics
Proper field mapping and mission planning are essential for safe and successful drone operations. Before spraying, identify and map obstacles, no-spray zones, buffer zones, sensitive areas or crops, locations with human activity, terrain features, and any other potential hazards.
Be aware that a drone may fly through the airspace above designated no-spray zones, but will not fly through airspace designated as an obstacle. Understanding this distinction is important and can help prevent potential collisions.
Always check for relevant Notice to Air Missions (NOTAMs), confirm that the airspace is not restricted, and be aware of any other aircraft operating nearby. Ensure the RPAS software is up to date and that the manufacturer’s latest approved version is installed.
Finally, remember that conditions can change between planning and application. If mission planning is completed well in advance of the spray day, review and update the plan before flying to account for any changes in the field, environment, or airspace.
Staging Area
Ideally, the staging area should be identified and prepared prior to the spray day. Select and clear a location for filling, take-off and landing that is safe for the operator, crew and equipment.
The staging area should present clear lines of sight and support efficient operations.
Drones should never fly over, or too close to busy roads.
The staging area should be upwind of the target site to reduce operator exposure to drift.
Bystanders must be at a safe minimum distance, as defined by the nature of the operation.
The operator and crew must be a safe distance from the drone during take-off and landing. Flying over crew is prohibited.
When spraying large fields, moving to an alternate staging area can save unnecessary ferry time, increasing efficiency and reducing battery strain.
Identify and be prepared to use connecting points and perform a manual landing when needed.
Drone tendering systems vary in size, complexity, cost and capacity, depending on the nature of the operation. For example, licensed exterminators (that is, those paid to spray properties other than their own) may have additional needs beyond what is listed here.
Mixing
Drone tanks are small and lack agitation. Therefore, most tendering systems include a nurse tank for pre-blending and agitating batches of spray mix. This helps ensure that active ingredients dissolve and disperse fully, that suspension products stay mixed and that the target site receives a consistent mix.
Water quality determines pesticide effectiveness; hardness, bicarbonate, pH, and turbidity can antagonize or degrade products. Water quality testing allows operators to correct potential problems before spraying. Higher spray volumes (that is, liters per hectare or gallons per acre) enable proper mixing and have been shown to improve spray coverage.
The act of mixing (and filling) carries the highest risk of operator exposure and environmental contamination. PPE requirements must be observed, and operators should avoid distractions or hurried work. Mix only the amount required for the task; leftover pesticide mixes create disposal problems and safety risks.
Fill the nurse tank halfway with clean water. Backflow prevention (for example, a valve or air gap) protects the water source.
Measure and add the pesticide, following the mixing order on the label and allowing time for each tank mix partner to dissolve and disperse. In Canada, tank mixing must be permitted on the label of each tank mix partner. Mixing multiple products at high concentration greatly increases the possibility of physical and/or chemical antagonism. If compatibility is in question, contact the manufacturers for guidance and conduct a jar test well in advance of spraying.
Rinse jugs and measuring tools into the nurse tank.
Top up with water and maintain agitation throughout the operation.
Transfer the spray mix into the drone tank using the most closed system available.
Filling and Battery Management
Rotary-wing drones carry relatively small spray volumes, so refills and battery swaps occur frequently. Large models, for example, might have a 10-minute flight cycle (also called a sortie), where the refilling and battery swap processes are simultaneous and comprise less than 2 minutes. Ferrying is the flight time between spraying and tendering represents a significant factor in productivity and should be minimized where possible.
Filling
Haste and inattention increase the chance of spills, overflows and leaks during refilling. This represents unnecessary point source contamination and operator exposure and must be avoided. While drone refills currently involve quarter-turn-valved faucets, or gas-station-style automatic fuel nozzles, neither are ideal. It is inadvisable to remove or otherwise modify the tank lid to expedite filling. Ensure filling is performed with the most closed system available.
Batteries
Batteries, like the drone, carry spray residue and must be handled using PPE. Some battery chargers feature water baths, misters or air conditioning. If water-cooled, treat the water as pesticide‑contaminated and dispose of accordingly. Batteries charge more efficiently and last longer if charged in a cool, ventilated location. Charge according to the manufacturer’s instructions.
Note: At the time of writing, there are new developments in drone tendering that permit autonomous charging and loading. This will reduce the potential for operator exposure and environmental contamination.
Operator Comfort
Drone operations are physically and mentally taxing. Attention to operator comfort improves safety and efficiency. Even seemingly minor accommodations have positive impacts:
Folding chairs combat operator fatigue.
RV awnings, umbrellas, foldable Bimini-style tops or flip-up doors provide shade.
Wear ear protection and consider lower-decibel equipment (for example, inverter gas generators are comparatively quiet, and electric pumps are even quieter).
Enclose or locate loud components far from the filling area to reduce noise and emission exposure.
Elevated Platforms and Flight Decks
Line-of-sight and Connectivity
While “beyond visual line-of-sight” operations are allowed under specific, authorized conditions, most current regulations require operators to maintain a visual line-of-sight with the drone. This supports swath alignment, obstacle avoidance, an ongoing assessment of drift risk, and general operational safety.
Operating from an elevated platform can help maintain visual line-of-sight and improve connectivity between the flight controller and the drone. Real-Time Kinematic (RTK) is a satellite positioning system that enhances GPS/GNSS data to provide centimeter-level accuracy in real time. A RTK platform will improve connection reliability and drone accuracy. Satellite internet providers can supplement connectivity in regions with unreliable cellular coverage, but be aware that network latency varies with provider.
The safest approach is for the pilot to control the drone from an elevated platform while a loader performs refill and battery-swap procedures on the ground. However, if operating off a flight deck:
Long flight decks keep landings and lift-offs at a safer distance.
Decks with pull-out platforms or hydraulic wings can increase the operating area and can be adjusted to account for adjacent roads and the slope of the ground.
A security rail around the landing area can prevent a drone from slipping off; A falling drone is expensive, but falling or sliding into an operator is a disaster.
An enclosed operations area can improve operator safety and comfort.
The operator should be focused on the drone/controller when flying; Flight is not an opportunity for performing other tasks.
Cleaning
Proper cleaning prevents cross‑contamination, maintains equipment lifespan, and avoids crop injury from residues. Perform cleaning away from open water and ensure rinsate is disposed of responsibly. Follow the pesticide label and adhere to the manufacturer’s instructions on allowable cleaning methods. The following recommendations do not supersede either resource. Be sure to clean the drone exterior, but do not pressure-wash to avoid damaging sensitive electronics.
Triple‑Rinse Procedure
Multiple, small-volume rinses are more effective than a single, large-volume one. Follow the triple-rinse procedure:
Ensure the drone tank is as empty as possible.
Fill the drone tank 1/4 full of clean water and, with a partner, agitate by rocking the tank (if removable).
Flush the rinse water through the plumbing and nozzles.
Repeat the process twice more.
Employ a similar procedure to remove residues from the nurse tank plumbing systems. Important reminders when cleaning:
Use a cleaning agent in the second rinse if recommended by the label. Soaking may be required.
While the drone exterior should be rinsed, avoid pressure washing (to protect electronics) unless explicitly permitted by the manufacturer.
Cameras and Lidar will not function if they are covered in residue.
Commercial drone residue removers are available to assist in keeping the drone clean.
Wash or dispose of PPE according to label and local regulations.
Record Keeping
Detailed record keeping will help operators better understand how operational and agronomic use cases affect the outcome of a spray mission. Quality records also help mitigate against any allegations of misapplication, such as a drift complaint. The following items should be recorded, but the list is not exhaustive:
Product name(s), rate(s) and water volume.
Sprayer operational settings (altitude, speed, route spacing, droplet size to supplement a digital record of the mission)
Swath measurements
Weather conditions
Note of buffers and sensitive areas
Crew names and roles
Unusual events or corrections
Results (return to site to assess efficacy)
3. Operational Use Case
Swath Width
For now, consider swath width to be the width of the area sprayed in a single pass. This definition will be refined later in the article.
Swath width is a fundamental variable for mission planning, ensuring the pesticide is applied at the correct rate and (in the case of broadacre operations) as uniformly as possible. A rotary-wing drone’s swath width is highly variable and affected by several factors, collectively referred to as the “Operational Use Case”. These factors include:
Downwash
Operational Settings (for example, altitude and travel speed)
Meteorological Conditions (for example, wind speed, wind direction, relative humidity)
Downwash
When a rotary-wing drone hovers, each rotor draws air from above and accelerates it downward in a high-velocity blast. The result is a vertical component referred to as the “downwash” and the turbulent splash of air that hits the ground and spreads laterally is the “outwash”. Droplets released beneath a drone at hover are almost completely entrained by the downwash. The majority get driven to the ground and then move laterally along the outwash, while a small portion (generally smaller droplets) recirculate back up through the rotors (see Figure 3a).
Most rotary-wing drones have fixed-pitch rotors, so the entire drone must tilt forward to enter low-speed flight. This causes the column of downwash to tilt backward. While the downwash is created by lift, “wake turbulence” is created at the tips of the rotors as high-pressure air beneath the rotor wraps around to the low-pressure area above.
As the drone flies at low speed (~3 m/s) the wake can be visualized as a pair of counter-rotating, cylindrical vortices that trail behind. Spray is still mostly entrained by the downwash on a downward and rearward vector with deposition aligning closely to the flight path. However, a portion will get caught in the trailing (aka rotor) wake (see Figure 3b).
Figure 3a (left). Rotary-wing drone at hover creates a high-energy downwash directly below the drone. 3b (right). Rotary-wing drone at low-medium flight speed trails a lower-energy downwash and creates a rotor wake.
The effects of higher flight speeds have not yet been fully characterized. While the additional thrust required at higher speeds may increase downwash energy, the downwash is also carried farther behind the drone and distributed over a larger area. As a result, it becomes less effective at entraining spray droplets and directing them to (or into) the target. Droplets remain suspended for longer, making them more susceptible to evaporation and wind displacement. This increases the proportion of spray carried within the rotor wake. Overall, these effects tend to widen the deposition pattern, increasing swath width.
The optimal flight speed achieves the intended result as efficiently as possible, and is therefore situation-specific. It must be assessed by the operator.
Operational Settings
When configuring a rotary-wing drone for a mission, pilots select operational settings through the controller. Of these, droplet size, flight speed, and altitude (specifically, the distance between the nozzles and the target) have the greatest influence on droplet behaviour and, consequently, swath width. Although the interactions among these factors are complex, some general trends have emerged. These trends can be used to predict how changes in a setting are likely to affect both swath width and drift potential (Table 2).
Table 2 – Effect of rotary-wing drone operational settings on swath width and drift potential.
Variable
Change
Effect on Swath Width
Effect on Drift Potential
Droplet size
Coarser
Narrows
Reduces
Droplet size
Finer
Widens1
Increases
Flight speed
Faster
Widens2
Increases
Flight speed
Slower
Narrows
Reduces3
Altitude
Higher
Widens1
Increases
Altitude
Lower
Narrows
Reduces3,4
1Coverage uniformity and overall number of deposits within the swath reduced due to evaporation and drift. 2Current evidence suggests that at high speeds (>~10 m/s) there may be a plateau where there is little or no further change to swath width, but deposition within the swath decreases, likely due to a combination of evaporation and drift. 3Lower speed and/or lower altitude will increase the influence of downwash on droplet behaviour. 4Low altitude may not permit sufficient overlap of the spray from each rotary atomizer, creating peaks and troughs in coverage.
Meteorological Conditions
Spray released from a drone is highly susceptible to environmental conditions. Drift potential increases when:
conditions are calm (inversion risk)
windspeed is too high (physical drift)
conditions are changeable (gusting and fluctuating wind direction)
conditions are hot and relative humidity is low (droplet evaporation)
Operators must observe label instructions, local laws, and use good judgment to minimize drift potential. At minimum, operators should consider adjusting settings for passes along the downwind field margin to account for swath offset and drift. Practical methods include:
reducing flight speed
increasing droplet size
increasing volume
reducing altitude (and compressing route spacing)
halting operations when conditions favour movement toward sensitive habitat / crop / residential areas.
Be aware that certain drift-reducing adjuvants have an unpredictable impact on the droplet size produced by current rotary atomizer designs. Until rotary atomizer design is standardized and tank mixes can be evaluated, do not assume adjuvants will work as intended.
Droplet Deposition
Consider the following operational use case: A rotary-wing drone spraying back and forth over rolling topography will experience changing wind speed and relative direction. The drone will respond by changing drone pitch, rotor speed and pump flow to maintain the desired altitude, travel speed, and application rate. Meanwhile, the drone gets lighter as it sprays, reducing the magnitude of the downwash. Ultimately, this results in a swath width that expands and contracts and may shift back-and-forth or be consistently offset along the flight path (Figure 4).
Figure 4 – Swath width and swath position along the flight path is variable.
Deposition studies using vertical targets have shown that when a drone sprays into a headwind, deposits spread laterally to either side of the flight path due, in part, to rotor outwash. Deposit density decreases with increasing distance from the flight path. Spray is also carried downward and rearward by the downwash, resulting in greater deposition on vertical surfaces that face the drone’s retreat (revisit Figure 3).
In contrast, a tailwind, even as light as 7 km/h, can carry suspended droplets forward and cumulatively deposit on vertical surfaces facing the drone’s advance. This is the opposite of the headwind situation. Similarly, a crosswind shifts the spray plume laterally in the downwind direction.
In summary, when the wind is strong enough to overcome the drone’s downwash, most spray will deposit downwind of the flight path, regardless of the drone’s direction of travel. This is especially true for smaller droplets.
Evaluating Swath Width
A drone’s swath width for a given operational use case must be determined through testing. The drone is first calibrated according to the manufacturer’s instructions. Swathing methods vary, but generally the drone is flown into the prevailing wind over a series of samplers (for example, discreet samplers like water sensitive paper or continuous samplers like string or bond paper). This creates a cross-section of the spray deposition (Figure 5).
Figure 5 – Methods for testing swath width.
Multiple passes are required to capture the variability that occurs along the flight path. Swath width is calculated for each pass and then averaged, as opposed to averaging deposition data and calculating a swath width. The later practice is incorrect because it conceals variability, resulting in a larger swath width and a more uniform distribution than can not be consistently achieved in practice.
This tapered coverage pattern may be unavoidable. Crewed aircraft reach and maintain target speed before spraying, but current software limitations prevent drones from separating flight and treatment zones.
Some drones will climb into the turn, ostensibly to reduce the impact of downwash on delicate canopies, but also to prevent tapering. In practice, this does not appear to change the effect on swath width significantly.
Therefore, when uniform coverage is critical, consider performing additional boundary passes, or headland passes at the beginning and end of each flight pass (see Figure 6).
Figure 6 – Swath width changes due to acceleration and deceleration.
Route Spacing and Overlap
A rotary-wing drone does not deposit spray uniformly across its swath. Deposition is typically greatest beneath the drone and decreases with distance from the flight path, creating a bell-shaped distribution (revisit Figure 5). In the presence of a sidewind, this distribution becomes skewed in the downwind direction.
Route (or pass) spacing is entered into the flight controller and determines the distance between adjacent flight passes. However, it does not influence the swath width itself. For example, if testing indicates a swath width of 7 m, entering a route spacing of 10 m will not increase it and striping (gaps between passes) will occur.
When uniform broadacre coverage is the objective, adjacent swaths should be overlapped to create the flattest possible combined distribution. Technically, for a series of repeated, symmetrical deposition curves, adjacent passes should be spaced so that the edge of one swath aligns with the centre of the next. Figure 7 models idealized, identical curves as if the operator flew into a headwind each time. In reality, curves are not normalized and repeated, so most operators employ wider route spacing to achieve a minimum threshold dose, and the result is an alternating pattern of excessive and “appropriate” doses. This will be addressed later in the article.
Figure 7 – Modeling a 7 m, normalized swath (headwind), on a 7 m route spacing. Illustrates cumulative coverage due to overlap.
It may seem that a sidewind would improve uniformity by spreading and smoothing deposits over a wider area. However, sidewinds produce skewed deposition patterns rather than symmetrical bell curves. Modelling shows that when these asymmetric profiles are overlapped at regular intervals, their steep leading edges and long trailing tails do not balance. Instead, they create pronounced peaks separated by broad, shallow valleys (Figure 8).
Figure 8 – Modeling a 7 m, skewed swath (moderate sidewind), on a 7 m route spacing. Illustrates cumulative coverage due to overlap.
As skew increases (due to higher wind, smaller droplets and/or higher altitude), uniform coverage becomes progressively more difficult to achieve, and no single route spacing can fully compensate for the asymmetry of the deposition pattern (Figure 9). Drones that adopt a horizontal boom with evenly-spaced atomizers, preferably preceding any downwash, will likely produce a more uniform pattern of deposition.
Figure 9 – Modeling a 7 m, significantly skewed swath (high sidewind), on a 7 m route spacing. Illustrates cumulative coverage due to overlap.
4. Agronomic Use Case
Effective Swath Width
We must now refine the concept of swath width. Some drone manufacturers define swath width as the distance between the furthest detectable spray deposits. However, detecting a deposit during testing does not necessarily mean enough product has been deposited to achieve the desired result. As a result, application efficacy can vary across the swath, even within the measured limits of detectable deposition.
A more practical definition is the Effective Swath Width (ESW), which is the maximum route spacing that achieves the desired biological result while minimizing over-dosing between adjacent passes. Determining the ESW requires establishing a minimum threshold dose, or put simply, knowing “how much is enough?” Traditional aerial application methods often assess deposit uniformity using the coefficient of variation (CV) and estimate the efficacy threshold as a percentage (70% or more) of the maximum deposition. We have developed an online tool to help calculate ESW from measured deposition patterns, but there are several others.
The need to establish the ESW becomes apparent at the outer edges of the swath, where deposition is often too sparse and/or variable to provide reliable control. Therefore, the Effective Swath Width is influenced by the Agronomic Use Case, which includes factors such as:
Minimum Effective dose: This is a complex relationship between coverage, spray mix concentration and pesticide mode-of-action. It is a threshold or narrow range that elicits an effective result while minimizing waste.
Spray Mix Rheology (that is, the interaction of spray mix viscosity and atomizer design on droplet sizes produced)
Target Location (for example, a pest within a dense canopy or a weed on relatively bare ground)
Minimum Effective Dose
Consider a systemic herbicide and a contact fungicide. Generally, a herbicide mixed according to the label will kill weeds with less volume per hectare and less target coverage than is required for most fungicides and insecticides. Therefore, a herbicide can still be effective at the extremes of the swath, whereas a fungicide may not.
Somewhere towards the middle of a drone’s steep deposition curve, the dose becomes excessive. If uniform broadacre coverage is the objective (as opposed to a directed application into a perennial, three-dimensional canopy), this represents waste. In exceptional circumstances, such as certain horticultural crops or GMOs with stressed metabolisms, it could potentially cause phytotoxic damage.
Therefore, two missions with identical operational use cases, but different agronomic use cases, can present the same swath width during testing, yet have different Effective Swath Widths in practice. This important distinction is why route (aka pass) spacing should match the effective swath width (see Figure 10).
Figure 10 – Matching route spacing to effective swath width represents the widest route spacing while achieving minimal threshold dosage. It is a compromise that trades increased productivity for reduced coverage uniformity. Deposition curves and thresholds shown here are idealized and not intended to be realistic.
Spray Mix Rheology
Most conventional hydraulic nozzle designs adhere to an international standard. This allows the operator to determine the size of droplets produced for a given operating pressure and flow rate. Droplet size is a not only a critical factor in mitigating drift and improving spray coverage, but also affects product efficacy (for example, droplets that stay wet longer tend to improve herbicide uptake).
Currently, most rotary-wing drones use rotary atomizers positioned within the rotor downwash. Rotary atomizer designs are not standardized, and their performance can vary considerably. Some atomizers are prone to “flooding” when the liquid flow rate exceeds their capacity. When this occurs, the atomizer produces a greater proportion of large droplets, which can reduce coverage and overall application efficacy.
As a result, the droplet size selected in the controller does not necessarily reflect the droplet size actually being produced. Research has shown that factors such as atomizer design, flow rate, spray mix composition, product concentration, and the use of adjuvants can significantly alter droplet size, producing droplets that are either much larger or much smaller than intended.
This variability creates a practical challenge for operators. When a pesticide label specifies a particular droplet size, it can be difficult to verify that the spray being produced by the drone is actually within the required range. Until rotary atomizers are standardized (or there is a return to conventional nozzles), operators can only select the desired size and infer the results based on in-flight behaviour and observing the size of the stains left on samplers during swath width testing.
Target Location
Spray coverage diminishes with canopy depth. The degree depends on crop morphology and planting architecture, as well as certain operational settings such as volume, droplet size and flight speed. Stated differently, a plant canopy filters out spray droplets, and this occurs both vertically and laterally. This is not represented during typical swath measurements, which tend to take place on bare ground in two dimensions.
Practical Impact
Taken collectively, research has shown a 20 to 30% reduction in ESW for corn, wheat and soybean fungicide applications compared to swaths measured on open ground. It is presumed that any complex canopy will reduce ESW to some degree.
The impact of agronomic use case on ESW must be considered during mission planning, because it may warrant adjustments to route spacing.
Figure 11 – Relative size of a measured swath width (that is, deposition analysis) versus effective swath width (that is, biological efficacy) for different agronomic use cases.
5. Conclusion
Drone technology is evolving rapidly, and best management practices will change with new research and more experience. However, the principles in this document (that is, proper preparation, careful mixing, responsible application, diligent maintenance, environmental awareness and swath testing) apply regardless of model or agronomic use case.
As the regulatory landscape also evolves, operators must ensure they are properly licensed and comply with all applicable federal and provincial requirements, including those related to the sale, use, transportation, storage and disposal of pesticides.
With thoughtful planning, practice and record keeping, drones can be a safe and effective means of crop protection.
Thanks to Dr. Steve Li (Auburn University, College of Agriculture), Dr. Michael Reinke (Michigan State University Extension), Mark Ledebuhr (Application insight LLS), and Dr. Tom Wolf (Agrimetrix Research and Training) for their contributions to this article.
Most operators assume drone swath widths are wide, stable, and predictable. That confidence generally comes from three places:
Manufacturer specs, often broad, vague and dependent on working conditions, not to mention each drone model is different, and even two drones of the same model behave differently.
Single point calibrations (water sensitive paper, Swath Gobbler, etc.) that are useful and display a 1-dimensional point-in-time snapshot of the swath.
“Looks good from the ground.” Watching a plume from below often makes it feel wider than it is.
But drones move through space and time; spray patterns evolve as they fly. What you think is happening in the two seconds you glance up is not what’s happening over a 50 metre pass. The following video shows a multi-drone comparison where three drones apply 20, 50 and 100 L/ha.
Why Single Point Methods Fall Short
This isn’t a criticism. Water sensitive paper (WSP) cards and tools like Swath Gobbler™ are valuable. But they are 1-D snapshots of a 2-D, time-evolving problem. WSP captures a moment, not a pattern. Swath Gobbler™ helps visualize centre mass but can’t show edge dynamics or how edges wander along the pass.
Real deposition and uniformity depend on:
Flight parameters (altitude, speed, droplet size)
Ground or crop size and shape
Path stability and lane keeping of the aircraft
Continuous micro corrections the aircraft makes
Gusts, even in “light” wind
Onboard wind compensation behaviour
We noticed an observation from the field: gusts → aircraft corrections → amplified drift. If a left-side gust pushes the aircraft, the autopilot often dips into the wind to hold course. The nozzles are mounted to the airframe, so that slight tilt can direct spray downwind, in the same direction the gust is pushing, amplifying drift rather than cancelling it.
Hidden Message: Many operators are doing their homework. At SDEUC 2026, I was impressed by how many pilots were calibrating and said they “knew” their drone’s pattern. My data suggests your drone may be subtly lying to you – its pattern shifts as it moves through air over distance.
What Happens Over 50 m: The Swath You Weren’t Expecting
The setup matters, because without context, a lot of people will assume the pattern you’re about to see is an artifact. It’s not. It’s the result of a controlled, repeatable field experiment designed specifically to expose real-world swath behaviour.
During August–September 2024, we conducted clopyralid herbicide application trials in soybean, a crop that is extremely sensitive to clopyralid. Even a trace amount causes clear visual symptoms four weeks after application, which makes soybean a perfect bio indicator of spray deposition. (I jokingly call this sensitivity the “touch of death” because it reveals every detail.)
We used a DJI T50 to apply Lontrel™ XC (clopyralid) at the highest labeled rate (300 g ae/ha) across three water volumes (20, 50, 100 L/ha) over 100 m long field plots. From each pass, a continuous 50 metre analysis zone was extracted to see how the swath behaved over distance (Table 1).
Category
Details
Crop
Crop Soybean (highly sensitive to clopyralid, ideal for visualizing deposition)
Herbicide
Lontrel™ XC (clopyralid) @ 300 g ae/ha (highest labeled rate)
Equipment
DJI Agras T50 with rotary atomizers
Spray Altitude
3 m above canopy
Water Volumes
20 L/ha, 50 L/ha, 100 L/ha
Droplet Size
300 µm (rotary atomizer setting)
Flight Speeds Achieved
~7.0 m/s (20 L/ha), ~6.9 m/s (50 L/ha), ~4.2 m/s (100 L/ha)
Plot Dimensions
10 m wide × up to 110 m long (location dependent)
Analysis Window
Central 50 m (avoids acceleration/deceleration effects)
Wind
~5 km/h (cross wind)
Data Extraction
DroneDeploy orthomosaic → continuous 2 D swath visualization
Table 1 Key application parameters for the 50 m Swath Visualization trials
Now, here’s what the swath actually looked like over 50 m (Figures 1 and 2):
Figure 1 – 50 m continuous swath visualization, Trial 1. This stitched graphic shows annotations for upwind/downwind edges and width measurements.Figure 2 – 50 m continuous swath visualization, Trial 2. This stitched graphic shows annotations for upwind/downwind edges and width measurements.
What becomes immediately obvious is that this is not the clean, geometric ribbon many expect. Here’s what the 50 m swath showed:
Despite all the drone consistently flying north to south in a straight line, the path of efficacy isn’t consistently straight, appearing to subtly be affected by the wind.
Within the path, the edges are also not straight, the upwind edge can often appear jagged. Each “tooth” could correspond to a micro correction the drone makes to hold course. The downwind edge adds a frayed or tattered look, not as clean of a boundary, likely caused by drifting spray.
The width changes along the pass. Some sections widen, while others pinch inward. It would be unlikely to see these 2-D effects with 1-D sampling such as WSP cards.
The plume tail wanders. The airborne portion of the spray oscillates left and right in response to gusts and minor stability corrections.
The pattern is asymmetric. Left ≠ right. Upwind ≠ downwind. A drone swath is not a mirror image, and each pass is different.
The bottom line: A drone’s real swath is not a clean bar of colour, it’s an irregular coastline. And once you visualize it in 2-D over 50 m, the story becomes clear: swaths are dynamic, variable, dependent on conditions, and often narrower than manufacturer recommendations.
Why I Think It Looks Like This
It’s not that drones are bad sprayers; it’s that their reality is dynamic. 2-D imagery simply reveals what single point tools cannot:
Drones are constantly making tiny left/right/forward/back corrections to counter act the forces (mostly wind) acting on them.
The wind and the resulting corrections of the aircraft slightly change where the spray actually travels.
The downwash column shifts with the aircraft’s posture.
Even light wind (< 5 km/h) is enough to expose these shifts.
The Wandering Swath and Jagged Edge Problem: Swaths Don’t Fit Together Like Shark Teeth
This is where mis-set swath widths come back to bite. When you slide one measured swath against its neighbour, the non-linear path and jagged edges don’t interlock. Some spots show metres of overlap; others flirt with gaps. The following video shows us sliding a measured 2-D swath polygon until it just touches the neighbouring swath. Note how irregular edges force uneven overlap and occasional near misses.
Operationally, if you rely on the widest advertised swath—or on a single clean snapshot—two things happen:
Misses (especially with herbicides): escapes, patchy control.
Dose non uniformity: some areas get 0x, others 2x.
Sure, the average across 160 acres may equal the target rate, but field level uniformity is not icing-smooth.
Practical Recommendations You Can Use Tomorrow
These are observational, conservative, and based on what the 2-D data actually shows:
1. Calibrate, test in your conditions, over distance
Run a long test strip and evaluate coverage continuously (not just a few cards). Evaluate in many wind conditions, to best understand your swath variance given the situation.
2. Tighten your swaths beyond what’s stated in the brochure
Depending on the application and product, plan for more overlap than the manufacturer’s suggested swath width. Adjust from there with your own measurements.
3. Different jobs, different risk tolerance
Herbicides: misses are costly → tightest spacing.
Fungicides: somewhat more forgiving but still benefit from stability and overlap.
4. Faster (7 m/s) with lower water volume displayed more variable swath
Higher water volumes direct in a large amount of water being pushed down within the downwash resulting in less drift and more consistency. This coupled with slower flight = fewer corrections = a straighter, more consistent swath.
At ~5 m/s, droplets fall mostly into the downwash beneath the drone, deposition is close to the flight line.
At ~9 m/s, the airframe tilts forward to hold speed. The nozzles are slightly tilted back and the spray is deflected backward, it trails the drone like Superman’s cape.
Downwash is no longer straight down.
Coupled with the flight speed, the downwash is no longer pushing the spray into the canopy.
Deposition lands farther behind the drone.
Small gusts now matter more.
A backward angled plume has more side profile for crosswind to grab. The following speed comparison (same drone, two speeds) illustrates this effect:
5. Respect “light” wind
The imagery shows meaningful edge change and drift even in 5 km/h. Even the ‘gusts’ in light wind move the swath. In relatively calm days continue to watch variability, plan overlap, and validate.
Conclusion – Know the Swath You Actually Have
Drone spraying is promising and can be very effective and is getting better fast. Fit the setting with the task. If there is less room for error (herbicide), tighten those swaths to prevent misses caused by the wandering swath. Swaths are often misunderstood when we only look at single points.
When you test over distance and see the 2-D pattern:
Coverage becomes more reliable
Reduces misses
Efficacy gets consistent
Confidence rises
The first step to improving application is knowing the real shape of your swath. Tighten spacing, higher water volumes, slow down when you can, validate in your own conditions, and keep learning as the technology evolves. Spray drone technology is rapidly evolving, and many of today’s limitations will be addressed with innovation.
We conducted a series of drone deposition studies with three main objectives:
We wanted to:
Measure the swath width of a T50 drone at two flight speeds;
Document the nature of the downwash along the swath width;
Compare different techniques for measuring and analyzing swath widths.
The four swath width measurement methods were:
Horizontal bond paper (H-BP)
Horizontal water-sensitive paper (H-WSP)
Retreat-facing water-sensitive paper (R-WSP)
Three-dimensionally arranged water-sensitive paper (3D-WSP)
Assuming a trapezoidal-shaped spray swath, the Effective Swath Width (ESW) can be roughly defined as the span between two points that represent 1/2 of the average maximum deposit density. The idea is to create a cumulative pattern that is as uniform as possible when adjacent flights are added (Figure 1).
As with any application system, if we assume that the target rate provides acceptable control, any deviation from the intended target rate along the pattern is either over-dosing (waste), or under-dosing (reduced control). It is therefore imperative that the distributed dose, as received by the intended target, be as uniform as possible.
Figure 1. A spray pattern from DJI Agras T50, as depicted by horizontally oriented bond paper and scanned by Swath Gobbler™. In this case, the effective swath width is estimated from 1/2 the maximum deposit density, spanning approximately 7.5 m.
Materials and Methods
The study was conducted at Ontario’s Simcoe Research Station on September 17, 2024. The site was a flat, sand/loam field with no vegetation present (Figure 2).
Figure 2. Ground conditions at site.
A sampling array was established perpendicular to the forecast prevailing wind direction (150º). The sampling array had 17 discrete sampling locations (0 m to 16 m at 1 m intervals).
Two collector methods were used simultaneously, centered on and perpendicular to the flight path:
A flat, horizontal, continuous bond paper strip measuring 7.5 cm wide and 16 m long (secured in a Speed Track™ and analyzed using a Swath Gobbler™, Application Insight, Lansing MI).
Discrete water-sensitive paper (WSP) collectors facing in three directions (x, y, z), each clamped back to back. WSP measured 26 x 76 mm (Spraying Systems, Glendale Heights, IL) and were analyzed using a DropScope™ (SprayX, São Carlos, Brazil).
Sampling height was 30 cm above ground to simulate a fungicide application into a soybean crop. To avoid crowding collectors on each sampler, three parallel sampler rows were established, separated by a 1 m spacing (Figure 3).
Figure 3. Sampling array and collectors.
The first row contained the WSP oriented in the Y direction (WSP facing upward and downward. The second row contained the WSP in the X direction (WSP facing left and right relative to flight direction), as well as Z direction (WSP facing sprayer approach and retreat (Figure 4).
Figure 4. Examples of discrete sampler design and collector orientation.
Drone settings
A DJI T50 drone fitted with four rotary atomizers was used to make the spray applications. The flight controller settings were a 250 µm droplet diameter spray over a 7 m swath width, at an altitude of 3 m above ground. Flight speed was either 4 m/s or 8 m/s. Application volume was 30 L/ha. Each flight speed was replicated three times. A total of six passes were made in this trial.
The drone tank (capacity 40 L) contained tap water water with 0.2% v/v of Rhodamine WT 20% liquid (Hoskin Scientific, Burnaby, BC), prepared in a single batch (Figure 5). The level of liquid in the RPAS tank was maintained between 20 L and 30 L throughout the trial to minimize the effect of a changing payload. A volume of spray liquid was sampled prior to each pass to serve as standards for fluorometric analysis.
Figure 5. Preparing the dye solution.
Trial procedure
Collectors were placed in samplers and the drone was positioned ~75 m downwind of the array to allow it to reach the target flight speed. When wind conditions were deemed appropriate, a signal was given to initiate the flight. Upon pass completion, one minute was allowed to elapse before sampler collection to permit complete deposition and drying.
Labelled WSP were retrieved and placed loosely in paper bags to prevent any residual moisture from ruining the collectors. Bond paper from the Swath Gobbler™ was marked with treatment information and reeled onto individual spools (one per treatment).
Weather conditions
Both wind speed and direction varied slightly during the study, but it was always possible to run a trial with negligible sidewinds so that the sample array captured the majority of the spray swath. Air temperature was approximately 25 °C. Wind speed was ranged from 6 to 14 km/h during the trial. All spray passes were into a headwind with maximum deviations of -10 to +30°.
Collector analysis
Bond paper digitization
Bond papers (Figure 6) were scanned using a Swath Gobbler™. The software measured both deposit density and percent coverage at each scanned location, but only deposit density was used in the analysis.
Figure 6. Bond paper secured in a Speed Track™ and sprayed with a Rhodamine WT solution.
WSP digitzation
WSP were removed from paper bags, sorted and sequenced into reps. WSP were scanned using a Drop Scope™ set to “Ground sprayer” and “Syngenta WSP” (Figure 7). The software reported deposit density and percent area coverage, but only deposit density was used in the analysis.
Figure 7. In-box, Out-box procedure for scanning WSP using a Drop Scope™.
Effective swath width calculation
We used our Excel-based model which assumes a racetrack pattern and sums deposits from adjacent swaths. Swath width was adjusted to minimize over- and under-dosing as well as deposit coefficient of variation (CV), while maximizing swath width.
For the WSP collectors, each of the six orientations were first evaluated separately, and then averaged to simulate a three-dimensional plant structure. Given the similar orientations, the upward-facing WSP and bond paper were used as quality-checks.
Visualizing coverage in three dimensions
In order to understand the direction the spray cloud moved as it imacted the collector array, we declared a dominant side to each of the three cardinal directions, x, y, and z that we captured using the WSP.
X-axis: Looking in the direction of travel, WSP deposits facing right were subtracted from those facing left.
Y-axis: WSP facing up minus papers facing down.
Z-axis: WSP facing the RPAS retreat minus papers facing the advance.
This allowed us to estimate the vectors with which the spray was deposited.
Results and Discussion
Deposits on WSP
We first looked at WSP data to better understand the direction that the droplets flew at the time of impact.
X-axis: Note that the right-facing cards are depicted as being positive, whereas the left-facing cards are depicted as negative.
Only those WSP facing the drone received deposit, with the deposit amount being larger for the faster flight speed (Figure 8). This implies that the spray moved out to either side from the centre of the flight path, carried by a laterally moving downwash.
Figure 8. Coverage on the X-axis, with WSP faces oriented perpendicular to flight path. Note that the drone passed between the 7 and 8 metre marks.
Y-axis: On the whole, deposition on the horozontal collectors was most variable of the three orientations, and resulted in the lowest measured droplet densities (Figure 9). Upward-facing WSP received more of the deposits than the downward-facing WSP. However, at 3 m and 12 m, the majority of deposition appeared on the downward-facing WSP. Underneath the drone rotors, downwash force would prevent re-bound. But at the edge of the rotors, a lower pressure region would permit pressurized air to escape not just laterally but also vertically. Entrained droplets would therefore gain an upward vector, and impact the downward-facing WSP. A slight wind from the right truncated the swath at the 13 m mark. That same wind may have captured any spray from the “bounce” at 3 m to become secondary deposition along the 1 m – 3 m section.
Figure 9. Coverage on the Y-axis, with WSP faces oriented up or down. The drone passed between the 7 and 8 meter marks.
Z-Axis: Only those WSP facing the retreat of the drone received deposits (Figure 10). As previously discussed, this is likely due to the downwash, which is vectored downward and rearward along the flight path according to the drone orientation in flight. These deposits were further reinforced by the prevailing wind direction after the drone had passed.
This deposit pattern is opposite to that of a ground sprayer, where spray tends to deposit on the advance surfaces due to droplet inertia (assuming a low boom height and fast travel speed). A slight shift to the left is apparent in Figure 10, likely due to the headwind’s directional deviation to starboard. Note that the faster flight speed had higher deposit densities. Reasons for this are unclear, as there was no commensurate deficit in droplet numbers at other sampler orientations for the faster speed.
The overall deposit density on the retreat-facing orientation was highest of any single collector orientation. The high deposit density and swath width is likely the result of the prevailing wind direction as well as the additional contribution of the downwash from the forward-tilted RPAS. These two factors helped transport the spray plume backwards for efficient interception by retreat-facing collectors.
Further evidence of this dynamic was visible when examining the bond paper collector strips. In the lee of the track edge, deposits were scarce, indicating a predominant horizontal trajectory of the droplets (Figure 11).
Figure 10. Coverage on the Z-axis, with WSP faces oriented to face drone advance and retreat. The drone passed between the 7 and 8 meter marks.Figure 11. A shadowed region (highlighted in light red) that sometimes appeared along the retreat-edge of the Speed Track™.
ESW at 8 m/s flight speed
Only the upward- and retreat-facing WSP surfaces received consistent spray coverage. As a result, only these two orientations were individually used for ESW calculations. However, deposits from all six orientations were averaged for the combined ESW measurement.
Two analysis methods were compared. First, the ESW was calculated for each replicate run seprately, and the resulting ESW were then averaged. Second, the three replicate run deposits were first averaged, and then ESW was calculated from that average.
When ESW from the bond paper was calculated for each replicate and then averaged, the ESW was 6.8 ± 1.4 m (Table 1). The resulting average CV of those swaths in a racetrack pattern was 14.8%.
When ESW was calculated from the upward-facing WSP for each replicate, the ESW was 4.7 ± 0.2 m. This was narrower than the bond paper result oriented on the same plane. In addition, the average swath CV was now 34%, significantly higher than that from the bond paper collector.
The retreat-facing WSP resulted in the highest ESW so far, 7.8 ± 0.5 m.
To better simulate a plant’s cumulative deposit, reflecting the pesticide dose received on leaves and stems that might vary in location and orientation, all six orientations were combined for each pass. When ESW was then calculated for each replicate, it was 8.8 ± 0.2 m (CV = 20%).
The range of swath widths onserved within each of the three reps ranged from 6 to 51% of the mean ESW. Differences between replicates could be due to automatic, instantaneous adjustments in the flight path controlled by the drone, or it may be due to changes in environmental conditions in the two hours that elspsed between consecutive replications. It may be instructive to increase the replicate sampling to obtain better estimates of variability within any given treatment.
If reps were pooled before calculating ESW, ESW increased an average of 30% for all sampling methods (Table 1). The CV of multiple swath simulations also decreased an average of 28% with this approach. Pooling prior to analysis is, however, less accurate because it eliminates the variability one might observe between two dicrete locations, which is how product efficacy will be observed in a pest control situation.
Table 1. Calculated ESW (m) and CV (%) for 8 m/s flight speed based on deposit density (count/cm2). Range (% of mean) calculated for the averages. Change from Average is the % change in the ESW of a pooled sample compared to the averaged ESW from each replicate. H-BP: Horizontal Bond Paper, H-WSP: Horizontal water-sensitive paper, R-WSP: Retreat-facing water-sensitive paper, 3D-WSP: Sum of all six facets of water-sensitive paper.
ESW at 4 m/s flight speed
ESW were significantly narrower at the slower flight speed when measured on the bond paper, but of similar widths when measured using WSP (Table 2). The slower speed had much greater variability among replicate samples as well, ranging from 36 to 60% of the average ESW.
The retreat orientation showed the widest ESW, with the 3D orientations resulting in slightly narrower swaths. At the high speed treatment, the 3D analysis had produced the largest ESW.
Pooling the reps prior to analysis resulted in similar ESW for the bond paper and the upward-facing WSP, whereas the remaining orientations resulted in wider swaths when the reps were pooled.
In general, the swath CVs at the slower flight speed were similar to the fast RPAS speed, averaging in the low to mid 20s. Pooling the reps prior to analysis reduced swath CVs for the retreat orientation and the combined orientations, but not for the upward-facing collectors.
Table 2. Calculated ESW (m) for 4 m/s flight speed based on deposit density (count/cm2). Range (% of mean) calculated for the averages. Change from Average is the % change in the ESW of a pooled sample compared to the averaged ESW from each replicate. H-BP: Horizontal Bond Paper, H-WSP: Horizontal water-sensitive paper, R-WSP: Retreat-facing water-sensitive paper, 3D-WSP: Sum of all six facets of water-sensitive paper.
Comparing speeds
When ESW was calculated for each replicate, the slower flight speed resulted in ESW that were slightly smaller than the faster flight speed on average (6.8 vs 7.0 m). However, when reps were pooled, the slower flight speed resulted in significantly smaller ESW compared to the faster flight speed (7.4 vs 9 m). Pooling the reps prior to analysis also resulted in a lower coefficient of variation.
Generally, there was less variability among replicates for the faster flying speed. Whether this was the result of the speed itself or was an artifact of the specific conditions during which the flights occurred is unclear.
Comparing swath appearance from bond paper and optimal WSP orientations
When the ESW from the bond paper was calculated for each replicate and then averaged, the following graph was produced (Figure 12). Note the bi-modal shape produced at the slower flight speed. This corresponds with the position of the atomizers and it’s possible the increased dwell time directed more spray in those positions compared to the faster flight speed, which increased ESW and dispersed the spray more evenly.
This could also be responsible for greater uniformity among the three replicate flights that was observed.
Figure 12. Average of three passes from bond paper at 4 m/s and 8 m/s speed.
When we averaged coverage from the optimal 3D orientations (X-axis: inward-facing, Y-axis: upward-facing, and Z-axis: retreat-facing) and compared their swaths to the 2D, we are able to capture more droplets and eliminate the bi-modal pattern appearance of the lower speed, reducing CV and increasing the ESW (Figure 13). This may begin to explain why sprays that appear to have low coverage on horizontal collectors can produce better-than expected efficacy.
Figure 13. Average of three passes from optimal-facing WSP samplers at 4 m/s and 8 m/s speed.
Vector analysis
The sampler array permitted the generation of spray vectors that showed the inferred direction and intensity of the downwash movement.
To graph vectors for droplet movement at each position along the 16 m swath, we calculated net coverage as previously described (i.e. for each of the x, y, and z sampler orientations, the deposit density on one side was subtracted from the other). The magnitude of that value represented the relative dominance of that side of the orientation for spray deposition. We assumed that droplets were primarily carried by air movement to their collectors, therefore we inverted the sign on the coverage to express it as wind direction from the from the vantage of the drone. When these data were combined for the X-Y and the X-Z direction, we were able to estimate the origin and strength of the deposit vectors, and thus infer droplet-carrying airflow.
Plotting X by Z meant you are looking down from above (Figure 14). This created vectors that indicate lateral and rearward spray movement.
Figure 14. Plotting X by Z coverage creates vectors that indicate lateral and rearward forces that carry spray droplets released from the drone.
Note that the predominant direction of deposit in the X-Z plane was rearward, in the direction of the wind. The forward-tilt of the RPAS aso directed its downwash towards the rear, adding to the headwind effect. At the edge of the spray swath, the rearward vectors diminished, being solely under the influence of the headwind. The vectors were strongest at the locations corresponding to the RPAS rotors.
Plotting X by Y for each position means looking at the RPAS from ground level as it flies away from you. The resultant vectors indicated a combination of lateral and downward spray movement for the majority of the swath (Figure 15).
Figure 15. Plotting X by Y coverage creates vectors that indicate lateral and predominantly downward forces.
At two locations (3 m and 13 m) the net spray deposition was on the underside of the Y-samplers. This suggested that a special region in the downwash existed, where the high pressure air generated by the rotors dissipated, allowing droplet-laden air to move upwards, essentially re-bounding from the ground. At the same time, droplets moved laterally to escape the same high pressure region.
This region of high turbulence could be where plants in the canopy may see droplets arriving from a large number of directions, contributing to coverage that may not be similarly captured by a single flat collector.
Summary
ESW Measurement Method
There was significant variability in swath width and uniformity among three replicate measurements of the same drone configuration. We observed an average of 34%, and as much as 60%, variation in swath widths within replicate passes of the same speed treatment. Spray deposition cannot be assumed to be repeatable, and understanding deposit variability is likely more important than calculating its average.
Measurement techniques affected the observed swath widths. Generally, horizontal targets resulted in narrower swath width measurements than vertical targets facing into the wind and the direction of drone travel. A composite of various target orientations resulted in the widest and most uniform swath deposits.
Effect of Flight Speed
Faster drone flight speeds resulted in wider measured swath widths in all but one measurement technique. It is possible that the greater concentration of downwash energy at the lower flight speed prevented more of the droplets from moving laterally prior to impact with a collecting surface.
Measuring Downwash Turbulence
The direction of droplet movement varied with position under the drone. The majority of the droplets had a strong z-vector at deposition. This is the direction of the wind and of the retreating side of the drone. Both wind and backward-tilted downwash of the drone would contribute to this.
As the spray droplets neared the ground, the high air pressure under the drone rotors caused the downwash to move laterally away from the centreline. Droplets entrained in the downwash therefore moved strongly to the left and right of the centreline.
Of the dominant three collector orientations (retreat, lateral, and upwards), the lowest collection was achieved with horizontally oriented targets. Interestingly, at the rotor edge, vortices formed and these moved the droplets upwards, away from the ground. This effect was only observed in a narrow band at he outside edge of the rotors.
These effects were somewhat variable but consistent for all spray passes, and occurred at both travel speeds.
Overall Conclusions
A drone’s downwash results in spray droplets moving in many directions which cannot be accurately sampled with a single collector orientation.
If only a single plane can be sampled, it should be facing the retreat side of the spray pass.
Three-dimensional sampling may be required to better simulate the spray capture of an agricultural canopy.
Higher travel speeds resulted in slightly more uniform, wider, and repeatable deposition.
The variability of a drone’s deposition, both in ESW and CV, is considerable and remains a barrier for consistent efficacy.
Multiple replicate passes, analyzed discretely, are required to understand the variability of the drone’s spray deposit, both in ESW and in CV.
Author’s Note: Newer 3D deposition studies have revealed additional information that should be considered. Read more here
Thanks to Drone Spray Canada and Don Murdoch (University of Guelph) for their cooperation and in kind support of this study.
