Tag: Swath

  • The Hidden Shape of a Drone’s Spray Swath: What 2-D Imagery Reveals

    The Hidden Shape of a Drone’s Spray Swath: What 2-D Imagery Reveals

    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).

    CategoryDetails
    CropCrop Soybean (highly sensitive to clopyralid, ideal for visualizing deposition)
    HerbicideLontrel XC (clopyralid) @ 300 g ae/ha (highest labeled rate)
    EquipmentDJI Agras T50 with rotary atomizers
    Spray Altitude3 m above canopy
    Water Volumes20 L/ha, 50 L/ha, 100 L/ha
    Droplet Size300 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 Dimensions10 m wide × up to 110 m long (location dependent)
    Analysis WindowCentral 50 m (avoids acceleration/deceleration effects)
    Wind~5 km/h (cross wind)
    Data ExtractionDroneDeploy 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:

    1. Misses (especially with herbicides): escapes, patchy control.
    2. 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.

  • Droplet Trajectories from RPAS Application – Implications for Swath Width Measurements

    Droplet Trajectories from RPAS Application – Implications for Swath Width Measurements

    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:

    1. 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).
    2. 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 merasured 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 oerientations, 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 measurment technique. It is possible that the greater concentration of downwash energy at the lower flight 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

    1. A drone’s downwash results in spray droplets moving in many directions which cannot be accurately sampled with a single collector orientation.
    2. If only a single plane can be sampled, it should be facing the retreat side of the spray pass.
    3. Three-dimensional sampling may be required to better simulate the spray capture of an agricultural canopy.
    4. Higher travel speeds resulted in slightly more uniform, wider, and repeatable deposition.
    5. The variability of a drone’s deposition, both in ESW and CV, is considerable and remains a barrier for consistent efficacy.
    6. Multiple replicate passes, analyzed discretely, are required to understand the variability of the drone’s spray deposit, both in ESW and in CV.

    Thanks to Drone Spray Canada and Don Murdoch (University of Guelph) for their cooperation and in kind support of this study.

  • The Carvalho Boom and the Stages of Quadcopter Flight

    The Carvalho Boom and the Stages of Quadcopter Flight

    The Hypothesis

    The results of a recent herbicide deposition study performed with the DJI T100 led us to observe that after ~13 m/s, swath width and drift were no longer directly related to travel speed; They appeared unaffected. The result was completely unexpected as it was counter to several years of prior study with smaller drones. This led to a hypothesis that the aerodynamics of this new generation of quadcopters might be similar to that of a helicopter, and it was impacting spray deposition in a similar fashion.

    Let’s use the stages of quadcopter flight to set up the premise.

    1. Hover

    When a drone hovers, each rotor draws air from above and accelerates it downward in a high-velocity blast. The cumulative effect 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”.

    The initial strength of the downwash depends on the degree of “disc loading” which is the weight of the drone divided by the rotor area. The intensity of the downwash wanes with distance from the rotor, spreading out in three dimensions until it impacts the ground and becomes the outwash.

    During hover, the drone recycles some of its downwash. This turbulence affects the stability of the drone, requiring a great deal of power to stay aloft, especially when it’s full.

    2. Low-speed flight

    A helicopter achieves forward thrust by changing the pitch of its rotor blades. Most 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 is visualized by a pair of counter-rotating, cylindrical vortices that trail behind. Some journal articles suggest the downwash for medium-sized drones (e.g. < 50 L capacity) detach from the ground at speeds as low as 3 m/s.

    3. Effective Translational Lift (EFT)

    As the drone accelerates it continues to angle forward, likely not exceeding 30°. At some point (~15 m/s?), we suggest it enters a state of “effective translational lift”, becoming more stable and therefore more energy efficient. This speed is notably slower than is commonly reported for a helicopter.

    During the transition, the drone behaves more like a wing as it essentially outruns its downwash, moving undisturbed air over the rotors. This horizontal air provides some lift, making flight more energy efficient, at least until drag begins to pull on the drone.

    The Possible Effect of Flight Stage on Spray Behaviour

    Droplets released beneath a drone at hover are completely entrained by the downwash. The majority get driven to the ground and then laterally along the outwash, while some small portion (likely smaller droplets) recirculate back up through the rotors.

    At low-speed flight, the downwash begins to tip backwards and the downwash trails behind and at some point detaches from the ground. Spray released beneath the drone is still entrained and will trail on a downward and rearward vector in that downwash. However, a portion will get caught in the wake. We can sometimes see this spray separation occur when lighting conditions are just right.

    As speed continues to increase, much of the spray would still be entrained in the downwash, but a greater portion would get caught in the wake, appearing as spray curling at the extremes of the swath. At some point, perhaps if and when the drone enters EFT, the the downwash might be less chaotic and behave more like laminar air. In which case some spray would still curl in the wake, but much of it would fall in a more stable sheet. Further increases in speed would not affect spray behaviour appreciably.

    Taking Advantage of ETL

    If this is the case, it is conceivable that rotary atomizers positioned under the front rotors could fling some droplets beyond the leading edge of the downwash. What if instead, it were a horizontal boom positioned out in front of the rotors, transecting the chord line?

    As the drone tipped forward during high-speed flight, so too would the boom, bringing it closer to the ground and releasing droplets ahead of, and below, the leading edge of the downwash. This should produce a more uniform swath, perhaps subsequently pushed down as the drone passed over.

    It’s an interesting idea that is only made possible when drones are capable of high-speed flight.

    Reception

    In January 2026 I presented this concept during a lecture at the 4th annual Drone End-User meeting in Kansas City. The response was polite, but skeptical. I then shopped the idea around the trade show floor where drone manufacturers suggested a front-mounted boom would interfere with obstacle avoidance sensors, or shift the centre of gravity, making the drone difficult to fly and to land. And what about the impact of wind speed and direction? All good points. Then, Nino Carvalho introduced himself.

    The Carvalho Boom

    Nino Carvalho and his son, Emilio, own and operate NC Ag Spraying in the Central Valley of California, USA. Emilio was inspired to modify his drone after discussing matters with his mentors; one who owns and operates a fixed wing aerial business, and another that pilots a Huey helicopter. In late 2025, they designed and built a horizontal boom which I’ve dubbed “The Carvalho Boom”.

    Their first attempt was with a DJI T50, but the boom mount interfered with the stacked rotors, and the atomizer cables were difficult to extend. The XAG P150 had fewer cables and only top-mounted rotors, so it was a better fit. After experimenting with various materials (PVC was too flimsy, steel too heavy) they mounted a length of ½ inch metal conduit directly under the drone.

    In California, aircraft booms must be limited to 90% of the rotor width (because of rotor tip vortices). The greatest span of the rotors was 312 cm (122.8 in), so they made the boom 275 cm (~9 ft) long. They spaced the rotary atomizers evenly along the boom every 69 cm (~ 2 ft 3 in), extended the original 30.5 cm (12 in) nozzle cables to 305 cm (10 ft) to reach their respective electronic speed controllers, and plumbed them using 1.25 cm (0.5 in) diameter tubing.

    They flew this first prototype over water sensitive papers. Dropping from a 3 m (10 ft) altitude to 2 m (6.6 ft) improved coverage uniformity and resulted in a 10.3 m (34 ft) effective swath width. They could see the downwash was interfering with deposition, and while increasing to a larger droplet size helped, it didn’t help enough. Then they made some design changes, extending the boom 30.5 cm (12 in) beyond the rotors, and they saw they had something. They reached out to Agri-Spray Consulting (Nebraska) and arranged to run a series of Operation S.A.F.E. fly-ins.

    There were more than 25 flights that day, so we’ll focus on three specific load-outs. The critical parameters are listed in the following table in the order that they flew them. The first load-out (N7696-01) was deemed the best, and was the only one with the boom extended out front, beyond the rotor tips. This information is italicized. The other two are included here for interest. N7696-03 attempted to shift the boom back under the drone for cosmetic reasons, but also for ease of transportation. N7696-04 was the same configuration as the last, but with coarser droplets in an attempt to battle the downwash. The first fly-in report (N7696-01) is shown below, but all three reports can be downloaded by clicking the links above.

    Load-OutBoom PositionVolume Speed Droplet Size (µm)Altitude Wind VelocityEffective Swath WidthC.V. (Race Track / Back & Forth)
    N7696-01Beyond Rotors50 L/ha
    (5 gpa)
    16 m/s
    (36 mph)
    2302.75 m
    (9 ft)
    10.7 kmh
    (6.7 mph)
    10 m
    (33 ft)
    10%/10%
    N7696-03Beneath Rotors50 L/ha
    (5 gpa)
    16.5 m/s
    (37 mph)
    2302.75 m
    (9 ft)
    12.5 kmh (7.7 mph)7.6 m
    (25 ft)
    9%/11%
    N7696-04Beneath Rotors50 L/ha
    (5 gpa)
    14.3 m/s
    (32 mph)
    4002.75 m
    (9 ft)
    8.5 kmh
    (5.3 mph)
    8.5 m
    (28 ft)
    18%/11%

    Observers said it looked like the swath was rolled with a paintbrush and that there were no observable vortices – just a sheet of spray. The following videos show some of the passes from that day. Actually, you can see vortices, but only in the passes where the boom is positioned beneath the rotors and not when it’s extended out front.

    A 10% CV is spectacular, and the profile of each pass (even before averaging) was far flatter than any drone deposition I’ve seen previously. This design has not yet been used for custom application because there are still questions about how flight speed and pump flow will affect performance. But, the Carvalhos are already discussing the next design, constructed with carbon fibre tubes.

    Impacts and Musings

    Perhaps our description of how the air is moving over the drone is correct, or perhaps it isn’t quite right. Dr. Fernando Kassis Carvalho (no relation to Nino and Emilio) (AgroEfetiva, Sao Paulo, Brazil) recently shared that he also observed swath width no longer changed at speeds exceeding 13 or 14 m/s (personal communication). So, whatever the aerodynamic cause, the result seems clear.

    Does this mean we’ll see a new generation of quadcopters with front mounted booms? It’s certainly possible, and kind of poetic as some early drone designs featured a centrally-mounted boom that extended beyond the rotor tips. Emilio wondered aloud about possible wear on the front motors, and likely there will be other issues as they experiment, but it’s early days and they’re enthusiastic about pursuing the design.

    Nozzle Design

    Should we also consider a return to hydraulic nozzles? The rotary atomizers on a drone currently leave a lot to be desired. Dr. Ulisses Antuniassi (Prof., Sao Paulo State University) studied the spray quality produced by rotary atomizers. He ran atomizers from a DJI T40 and from a XAG P60 in a wind tunnel spraying WG and SL formulations with either MSO or NIS adjuvants and found no logical trends in VMD, relative span or DV 0.1

    Further, work by Dr. Steven Fredericks (Land O’Lakes) showed that the rotary atomizer from a DJI T40 created droplets roughly one ASABE category smaller than the software indicated. Conversely, common knowledge is that the XAG P100 version produces a coarser spray quality than anticipated, and slow motion video produced by Mark Ledebuhr (Application Insight LLC) and Dr. Michael Reinke (Michigan State University) clearly showed the flooding issue reported by Dr. Andrew Hewitt (University of Queensland), where excessive flow to the disc interferes with its ability produce a uniform droplet size.

    I photographed no less than nine different rotary atomizer designs while at the End-User meeting. So, perhaps we should embrace a standardized design, or perhaps hydraulic nozzles should make a comeback. If the later, it would be a great opportunity to include PWM to increase their flow range.

    Acceleration and Flight Pattern

    And what of kinematics? A drone’s “acceleration time” is calculated by dividing the change in velocity by the acceleration rate. We’ve seen that a DJI T100 must travel up to 100 m before it reaches target velocity. Admittedly, it was full and attempting to fly at high speed. Kevin Falk (Corteva Agriscience) noted a 25 m acceleration distance and a 15 m deceleration distance for a T50 flying mostly-full at 6 m/s. That’s a not-insignificant distance to achieve target flight speed.

    What happens to the spray from a quadcopter drone with a front mounted boom as it transitions through the stages of flight? We don’t know for sure, but we can infer an inconsistent swath. Perhaps the prolonged acceleration time is sufficient reason for drones to start flying racetrack flight patterns like planes and helicopters, where they reach sufficient speed before passing over and spraying the target area. Current software does not allow that practice.

    All this to say that as drone design continues to evolve, we must continue to challenge and test assumptions surrounding best practices. It has been fascinating to see how spray drones are finding their place in Western crop protection systems.

    Acknowledgements

    Thanks to Mark Ledebuhr (Application Insight LLC), Dr. Michael Reinke (Michigan State University), Kevin Falk (Corteva Agriscience), Dr. Tom Wolf (Application Research & Training), Adrian Rivard (Drone Spray Canada), and Adam Pfeffer (Bayer Crop Science) for insightful discussions.

    Special thanks to Nino and Emilio Carvalho (NC Ag Spraying) for sharing their experience and practical approach to improving drone spray deposition.

    Additional Resource

    In early February, 2026, I gave a short interview with RealAgriculture. We discussed the state of spray application by drone in Canada as well as some of the possible impacts of higher speeds.

  • Exploring the Accuracy of Drone-Applied Herbicide Treatments

    Exploring the Accuracy of Drone-Applied Herbicide Treatments

    Author’s note: Minor edits were made to this article on December 12, 2025. While the results remain unchanged, aspects of the interpretation have been adjusted upon reflection.

    In 2024, Corteva conducted a study entitled “Drone-Delivered Herbicides: Comparing LontrelTM XC (Clopyralid) Efficacy Across Application Techniques and Water Volumes”. Go read all about it here. 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 effective 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. This is a high dose of Roundup (~1.5 L/ac), selected to ensure that every drop that landed would create an obvious burn for easier analysis. It does, however, also mean that any reduced dose (i.e. striping) between passes would likely be masked. 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.

    Estimating Effective Swath Width

    The burned area indicates that the spray deposited met or exceeded an efficacious dose. This agronomic consideration of real-world efficacy sets the Effective Swath Width (ESW) apart from a swath width measured during calibration. Methods for calculating swath width utilize a sampling system aligned perpendicular to the flight path. Whether continuous or discreet samplers, this approach produces a coefficient of variation and some measure of over- and under-dose based on an assumed target threshold (dose or coverage). By measuring the biological effect (i.e. the burned area), we need not assume a target threshold – it’s indicated by the burn. Work with fungicides has demonstrated that the ESW can be a fraction of the measured swath width.

    ESW was estimated using two methods, and while both approaches have inherent flaws, they still provide valuable information. A more realistic representation of ESW likely falls between the two.

    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 underestimate of ESW because each upwind drone pass can overlap and hide any displacement (and drift) from the previous. It divides the drift over however many passes are made.

    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 ESW. Hereafter referred to as the “port + starboard extent” method. This approach captures a clear representation of the upwind side of a single pass, but overestimates ESW by including any cumulative increase in drift from multiple passes on the downwind side.

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

    Results – Part One

    Planned versus Measured Treatment Area

    The “programmed swath width” is something of a misnomer. More accurately, it is the route spacing and it describes the distance between passes over a target area. However, most drone manufacturers refer to this variable as programmed swath width, so that’s what we’ll do.

    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 Effective Swath Widths

    In all cases, the “treatment width ÷ passes” method produced an estimated ESW 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 ESW calculated by the “port + starboard extent” method was larger still, but was 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 estimate ESW, the T100 exceeded the planned swath width by more than the T50. Using the “port + starboard extent” method, the average T100 ESW was 21.3 m, which is an average 15.4% wider than the average 17 m ESW 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 ESW by Travel Speed

    When travel speed becomes the independent variable for the T50, the “treatment width ÷ passes” method produces an average ESW that positively correlates with flight speed. At 21.5 km/h, the average ESW 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 ESW is less clear when estimated 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 ESW by Travel Speed

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

    The effect of flight speed on treated area and ESW 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 ESW in any consistent or significant way (Figure 11).

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

    T100 ESW by Spray Quality

    The effect of spray quality on treated area and ESW 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.

    Effective swath widths estimated from both 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 an ESW 46.8% larger than a 500 µm spray quality. The “port + starboard extent” method saw an 80 µm spray quality produce an ESW 22.6% larger than a 500 µm spray quality.

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

    Discussion

    In all cases, the area treated (i.e. burned) exceeded the area planned. The T50 covered 32% more area while the T100 (with the same operational use case) covered 40% more. This implies that the T100 created wider swaths and/or drifted more than the T50.

    The ESW estimated from herbicide efficacy appears to be considerably larger than those observed in fungicide efficacy / coverage studies. This is likely the result of the agronomic use case. Consider that herbicides have a relatively lower threshold dose than fungicides. Further, herbicide application on bare earth or into sparse canopies permits the lateral spread of droplets, where spraying fungicides into a dense canopy limits penetration in all directions. Even the sparsest coverage from a systemic herbicide produces a visual effect, and this binary result (i.e. hit or miss) extends the effective swath width. This should raise awareness of the importance of field boundaries and margins, particularly with herbicides.

    When estimating ESW, the method used affected the results. The “port + starboard extent” method resulted in large and low-resolution estimations of ESW, whereas the “treatment width ÷ passes” method seemed to respond in a more predictable way, even if it underestimates the ESW. Ultimately, both methods produce rough estimates; they are not intended to replace traditional, quantifiable assessment methods. The “truth” is likely somewhere in between.

    With that caveat reaffirmed, we assessed ESW using the “treatment width ÷ passes”. It was positively correlated with flight speed for the T50, as observed in previous work. However, this was not the case with the T100. Given that both drones were operated using the same settings, it is unclear why the T100 would produce such erratic results. Future work will evaluate T100 ESW using conventional methods.

    When the T100 was flown using a span of three droplet sizes, there was a strong negative correlation between average droplet size and ESW. Once again, this aligned with previous experience. While rotary atomizers on drones tend to create smaller droplet sizes than reported by the flight controller, coarser droplets have greater mass, making them less prone to displacement by wind.

    However, when the T100 was flown at at three speeds, the relationship with ESW was once again unclear. When flown at 36 km/h (~10 m/s) the T100 was flying at the top speed of the T50. It also flew at 54 km/h and at 66 km/h, which was the highest speed we could achieve at 5 gpa. The ESW (as estimated using the “treatment width ÷ passes” method) was essentially unchanged. While it is possible (and likely) that any increase in effective swath width due to travel speed was obscured by drift, pervious work has shown that drift increases concomitantly with speed. That does not appear to have happened here.

    Perhaps this is a function of a greatly reduced dwell time diminishing the effect of the downwash. Or, perhaps, the T100’s capacity for higher speeds has allowed it to pass beyond translational lift into true forward flight, similar to a helicopter. Translational lift occurs any time there is relative airflow over the rotor disk. As headwind and/or forward speed increase, translational lift increases, resulting in less power required to hover. According to Transport Canada, it is present with any horizontal flow of air across the rotor but most noticeable when the airspeed reaches 16 to 24 knots flight (8.25 to 12.8 m/s or 30 km/h to 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. This did not occur at higher speeds, and this efficiency supports the premise that it moved past translational lift, perhaps achieving true forward flight.

    If this theory is correct, it’s a new development for rotary drones, which were not previously capable of reaching these speeds. Downwash was an unavoidable side effect of the flight, but may now be a tool for the operator to use as the situation warrants – battery temperature notwithstanding. Perhaps it warrants a return to horizontal booms positioned beyond the downwash in order to improve coverage uniformity. On the other hand, we saw that it took the T100 roughly 100 m to reach the target 66 km/h, meaning it moved from hover to translational flight and beyond over that distance. This raises questions about how they spray would respond throughout that transition.

    More work is required.

    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 and Tom Wolf for discussions surrounding the interpretation of these results.

  • RPAS Swathing in Broad Acre Crop Canopies

    RPAS Swathing in Broad Acre Crop Canopies

    This work was performed with contributions from Adrian Rivard (Drone Spray Canada) and Adam Pfeffer (Bayer Crop Science – funding partner). Dr. Tom Wolf is gratefully acknowledged for his editorial support and assistance interpreting the results.

    Introduction

    This research is part of a continuing effort to identify best practices for broad acre crop protection using remote piloted aerial systems (RPAS). Previous work in wheat, corn and soybean has provided insight into how RPAS operational settings and environmental factors affect drift potential, effective swath width and spray coverage. This information, paired with advancements in RPAS design, has helped operators to improve spray deposit accuracy.

    However, RPAS still produce what has traditionally been considered poor (or at least sporadic) broad acre coverage. Many studies have illustrated these shortcomings using herbicides or fluorescent tracers. Contributing factors include inappropriate operational settings, low application volumes (20-50 L/ha) paired with coarser spray qualities, and inaccurate swath widths. In light of these issues, we struggle to reconcile claims of acceptable disease control, which is arguably the greatest challenge in a spray-based crop protection paradigm.

    Tar Spot

    One real-world example of intermittent disease control from aerial applications (not just RPAS) is the case of tar spot in corn. Tar spot is a fungal disease caused by Phyllachora maydis and it is becoming a significant economic concern in Ontario. Left unchecked the disease causes rapid, premature leaf senescence. This reduces photosynthetic capacity, and ultimately, yield. Depending on spray timing, crop variety, environmental stressors, and the product applied, protection should last for up to three weeks.

    In the last few years there have been several reports (both in Ontario and in corn-producing US states) of tar spot “striping” following aerial sprays. Crops seem well protected directly beneath the flight path (green and healthy), but efficacy tapers to failure towards the edges of the swath (brown and desiccated). Fundamentally, this is likely due to inadequate spray coverage caused by an overestimation of the effective swath width.

    Figure 1 Tar spot striping in Western Illinois following two applications from a fixed wing sprayer (2023).
    Figure 2 Tar spot striping from RPAS volume trials. A brown strip can be seen between two passes in each RPAS treatment of 30 and 50 L/ha. The top is an application by a 100 foot horizontal boom. Each treatment is separated by an unsprayed check. (2023).
    Figure 3 – Tar spot striping in Ontario corn following fungicide application by helicopter (2024).

    Effective Swath Width (ESW)

    The measured swath width presents the lowest variability (as indicated by the coefficient of variability, CV) while minimizing the degree of over- and under-dosing. As a matter of operational productivity, wider swaths mean wider route spacing, which is attractive because it means fewer passes and faster applications. Once the agronomics are considered, the effective swath width is that portion of the swath that gives the desired biological result. It may equal, or only be a fraction of, the measured swath width. It is plausible that inappropriate effective swath widths from aerial applications are common, but have not always been detected, because:

    • Generally, fungicides are weakly systemic and give modest yield increases from disease suppression and their “stay green” properties. Until tar spot, a sub lethal dose of fungicide did not lead to rapid and acute crop failure.
    • Most growers do not intentionally leave unsprayed checks, or the check locations do not coincide with disease presence.
    • The applied product rate is sufficiently high to cover regions of under-application.
    • Taken together, deficiencies are often too subtle for passive detection.

    This is not to suggest that pilots intentionally inflate swath widths. Swaths are evaluated during fly-in calibration sessions using established protocols (e.g., Operation S.A.F.E.), and RPAS swath evaluation has emulated these practices. Calibrations take place on bare ground or stubble/grass using two-dimensional samplers (i.e., continuous samplers like string or bond paper, or discreet samplers like water sensitive paper). However, this protocol does not account for any physical interference from the crop canopy itself. This may have negative implications, particularly given the unique nature of the RPAS swath.

    RPAS tend to produce swaths with a very narrow span and a steep profile. To a certain extent, their swath widths share a direct relationship with altitude and headwind speed, and coarser sprays result in narrower swaths (with Dr. Michael Reinke, MSU). The outer edges of the RPAS swath represent the least amount of spray volume along the width, and this coincides with the turbulent dispersion zone of the downwash. Therefore, those extremes should contain a higher proportion of low-energy droplets moving in multiple directions relative the centre of the swath.

    While crop morphology and planting architecture are contributing factors (i.e. part of the agronomic use case), it is generally accepted that the degree of spray penetration falls off exponentially with canopy depth. It follows that this should also be the case for any lateral movement, resulting in a significantly shorter swath in-canopy versus on bare ground.

    Materials and Methods

    Spray Sampling

    Spray deposition was sampled using a 15.8 m (52 ft) Speed Track (Application Insight LLC) loaded with 3-inch bond paper (Staples Canada). The spray mix was 0.3% v/v FD&C Blue #1 Liquid. Bond papers were analyzed using a Swath Gobbler (2nd gen software – Application Insight LLC) at 100 mm sampling rate (i.e., ~150 discreet images per sample). Hue: 32-180. Saturation 17-60. Value: 156-255.

    The Swath Gobbler produces a complete, correlated and ordered record of the cross-section of a swath. For each discreet image, it reports the number of individual droplet stains on the sampler per area. It also reports percent area covered by measuring the total number of pixels with dye divided the total number of pixels in the image.

    The device deliberately does not calculate a Droplet Size Distribution (DSD) of the stains. This is because any DSD calculated from paper collectors relies on assumptions that cannot be validated, such as the fact that all droplets are captured and detected, spread factors are known for that application condition and similar for all stain sizes, there are no multiple hits, etc.

    RPAS

    The sprayer was a DJI T40, calibrated according to the pilot’s standard operating procedure (Drone Spray Canada). Certain operational settings varied with treatment and will be detailed later in this section.

    The flight path was perpendicular to the sampler, aligned with the centre using pin flags as references for the pilot. Spraying began approximately 20 m prior to the sampler to ensure the RPAS was at target speed and continued some 20 m past the sampler.

    Figure 4. DJI T40 approaching sampler on bare ground. Sampler was later moved into the adjacent wheat field (left).

    Defining Coverage

    Swath width will be calculated from two different coverage metrics.

    Percent Area Covered describes the amount of surface area covered by deposit. Given the variable degree of stain diameter (a function of sampler material, spray mix, and droplet velocity) this value can only be used as a relative index (i.e., can only be compared to itself). No conclusions can be drawn about how spray interacts with plant tissue, but generally more coverage correlates to improved crop protection.

    Deposit Density describes the number of individual droplet stains on the sampler per area. Higher densities can imply more uniform distribution over the plant surface, which is particularly important for contact materials.

    Previous studies (with Dr. Tom Wolf, Agrimetrix Research and Training, data not shown) indicate a higher correlation between deposit density and swath width at lower versus higher spray volumes. Lower volumes are typically comprised of finer droplets, which are more accurately resolved using deposit counts. Swath widths determined by deposit density also tend to be longer than those determined using percent coverage, better aligning with real-world observations of efficacy.

    Wheat

    R40 wheat was planted on October 9th, 2023, at 808,000 seeds/ha (2 million seeds/ac). Wheat height at the time of the trial was 60 cm (25 in). The location was 45180 Fruit Ridge Line, St. Thomas, Ontario. Deposition trials took place on May 23rd. Wheat stubble swath testing also took place at this location on May 15th.

    The RPAS was programmed to apply 50 L/ha using a 260 µm droplet diameter according to the DJI software. Air speed was 5 m/s and the flow rate was 11-12 L/min as it passed over the sampler. Swath was programmed at 8 m.

    Coverage was evaluated for water (control) and for a spray mix containing 0.15% v/v Interlock (a drift mitigating adjuvant – Winfield United) and 0.15% v/v Interlock + 0.125% v/v Activate Plus (a spreader adjuvant – Winfield United). For bare ground, each treatment had three passes (n=3) except for water, which had four (n=4).

    The wheat canopy was only sprayed with water three times (n=3). Limited passes were made because it served as a proof of principle. Any indication of relevant differences in the swath width would justify later trials in corn and soybean. These first passes revealed issues with the experimental design that were later corrected:

    • The RPAS spray tank level was not held constant. The RPAS weight affects the intensity of the downwash. The volume dropped from 30 L to ~20 L over the course of the experiment. In future trials, a tank volume of 20 L was maintained from a premixed source.
    • The wind direction occasionally shifted from a direct headwind to a partial cross wind from the RPAS’s right. In future experiments, we waited for an optimal wind direction before starting each pass.
    • The RPAS altitude was set to 3 m above bare ground. We assumed it would climb to account for the height of the wheat, but the canopy did not register with the RPAS sensors. As a result, spray was released ~60 cm closer to the wheat heads than to the ground in bare ground swathing. In future experiments, we confirmed that the RPAS was 3 m from the top of the crop canopy.
    • Despite best efforts, moving the sampler into the wheat parted and distorted the canopy. As a result, the sampler was not as obscured as it should have been. We developed strategies to minimize canopy distortion in corn and soybean that will be described later.
    Figure 5. Top-down view of sampler in wheat canopy. Note that the canopy did not close over the sampler as intended.

    Corn

    Corn was planted on May 15th, 2024, at 13,300 seeds/ha (33,000 seeds/ac). The sampler was erected in the field on July 3 to allow the canopy to grow up and around it. Deposition trials took place on July 26 and every effort was made to leave the canopy undisturbed around the sampler. Corn measured 2.4 m (9 ft) at the tassel and 1.2 m (4 ft) at the silks. The sampler height corresponded to the ears. The location was 42°40’52.1″N 81°04’45.9″W near 5277 Quaker Road, Sparta, Ontario.

    Figure 6 Sampler erected to 4 ft. Crop grew around the sampler to minimize any canopy disturbance.
    Figure 7 Sampler position relative to ears during sampling.
    Figure 8 Installing Speed Track for swath testing in wheat stubble.

    Soybean

    Soybean was planted on June 30th, 2024, at 80,800 seeds/ha (200,000 seeds/ac) on 38 cm (15 in) centres. Deposition trials took place the morning of August 14. While the densest area was selected for the trials, the field was patchy with crop height spanning 20-25 cm (8-14 in). Each section of the Speed Track was inserted under the canopy separately to avoid disturbing or damaging the plants. The track was elevated ~10 cm off the ground. The location was at 42°46’50.4″N 81°08’20.8″W near 43900 Talbot Line, Central Elgin, Ontario.

    Figure 9 Sampler in soybean.

    Corn and Soybean Treatments

    The following treatments were repeated three times in-canopy (n=3) (Table 1). The actual flow rate (recorded as the RPAS passed over the sampler) was always ~1.5 L/min less than programmed.

    Treatment #Droplet Diameter (µm)Programmed Swath (m)Volume (L/ha)Rate (L/min)Flight Speed (m/s)Spray Mix
    1320102010.510water
    232083010.58.3water
    332085010.55water
    43208305.75water
    550085010.55water
    63208505.750.5% Masterlock
    732083010.58.30.5% Masterlock
    Table 1 RPAS operational settings for corn and soybean treatments

    The following treatments were repeated three times on wheat stubble (n=3) (Table 2). Once again, the actual flow rate (recorded as the RPAS passed over the sampler) was always ~1.5 L/min less than programmed.

    Treatment #Droplet Diameter (µm)Programmed Swath (m)Volume (L/ha)Rate (L/min)Flight Speed (m/s)Spray Mix
    1320102010.510water
    232083010.58.3water
    332085010.55water
    43208305.75water
    Table 2 RPAS operational settings for wheat stubble treatments

    Weather Data

    The RPAS flight path was into the prevailing wind, but minor variations occurred throughout sampling. Weather was recorded as the RPAS passed over the sampler using a Kestrel 3550AG weather meter in a vane mount positioned on a tripod 2 m above ground (Table 3).

    TerrainWind Speed (km/h)Direction Relative to Flight PathTemperature (°C)Cloud Cover (%)RH (%)
    Bare Ground3-5Headwind +/- 25° from starboard20-21060
    Wheat Canopy5-7Headwind +/- 25° from starboard21-22060
    Corn Canopy2-4Headwind +/- 15° from starboard23-26<1075
    Wheat Stubble4-7Headwind +/- 15° from starboard26-28<1065
    Soybean3-4Headwind +/- 15° from starboard22055
    Table 3 Average weather conditions during trials.

    Results

    Raw Coverage Expressed as Percent Coverage or Deposit Density

    Coverage can be presented as raw data plotted by swath position. This is a qualitative means for assessing the swath. The bare ground data has been presented (using both coverage metrics) as an example (Figures 10 and 11).

    Figure 10 Swath coverage data for water on bare ground expressed as percent area covered. All four passes are plotted.
    Figure 11 Swath coverage data for water on bare ground expressed as deposit density. All four passes are plotted.

    Repetitions were similar enough to imply that environmental conditions were consistent during sampling. By averaging the repetitions, coverage in-canopy can be more easily compared to that on bare ground Figures 12 and 13).

    Figure 12 Average swath coverage data expressed as percent area covered. Bare ground (n=10). Wheat canopy (n=3).
    Figure 13 Average swath coverage data expressed as deposit density. Bare ground (n=10). Wheat canopy (n=3).

    The magnitude of coverage on bare ground exceeded that in-canopy, tapering to similitude and near-zero at the edges of the pattern. It can therefore be concluded that the entire swath was captured, and that spray was filtered by the canopy before reaching the sampler within.

    The difference between bare ground and the wheat canopy was greater when the data were presented as percent area versus deposit density. Differences in the number of deposits from finer sprays were more accurately resolved using deposit density than percent coverage. Since it can be expected that smaller droplets penetrate a canopy better than coarser droplets, it may be more appropriate to use deposit density to document their presence. We also saw indications of wider swaths when data were presented as deposit density, as well as a bimodal distribution that reflected the positions of the two rotary atomizers.

    While informative, this raw coverage format did not allow empirical comparisons. Each pass must be converted to a swath width.

    Converting to Swath Width

    Each pass was transformed by averaging Swath Gobbler data to a single value every 0.5 m. Data were then entered into the www.sprayers101.com swath width calculator and the SW was manually determined for each pass. Criteria was the lowest overdose, lowest underdose and lowest CV for an idealized threshold coverage of 90% that of the highest value in the swath. In the following histogram, the SW from all treatments have been averaged for ground and canopy terrains (Figure 14).

    There was a significant reduction in swath width in a wheat canopy compared to stubble or bare ground. There was a 41.2% reduction in swath width in a canopy when measured as percent area covered and a 26.6% reduction when expressed as deposit density. As previously stated, deposit density better reflects the contribution of finer deposits, which tend to penetrate deepest into crop canopies.

    Figure 14 Average effective swath width for all treatments on all terrains. Swaths expressed from both percent coverage and deposit density metrics. Standard error bars presented. Canopy (n=45). Ground (n=22).

    When the data is considered by terrain and by crop, we see that swathing on bare ground or in wheat stubble doesn’t have a significant impact. This justifies combining those data as “Ground” in subsequent analyses.

    Another observation that supports the use of deposit densities is the difference between the intended (i.e., programmed) swath width and the detected swath width on ground (Figure 15). The SW on ground was closer to the intended 8 or 10 m swath width when expressed as deposit density. It was approximately half the desired width when expressed as percent coverage, which is considerably less than common practice.

    Figure 15 Average effective swath width for each crop and terrain. Swaths expressed from both percent coverage and deposit density metrics. Standard error bars presented. Ground 8 m swath (n=19). Ground 10 m swath (n=3). Canopy 8 m swath (n=39). Canopy 10 m swath (n=6).

    Canopy Effect

    By percent area, corn had the biggest reduction in swath width compared to bare ground, then soybean, then wheat (Table 4 and Figure 16). This suggests the SW shares an inverse relationship with the canopy depth. However, the relationship reversed when SW was expressed as deposit density. The relationship between droplet size, crop physiology, planting architecture and canopy penetration is complicated, and no conclusions can be drawn beyond a reduction in SW in-canopy.

    Crop% Reduction in SW (% area)% Reduction in SW (deposits/cm2)
    Corn44.020.6
    Soybean32.228.3
    Wheat21.731.5
    Table 4 Reduction in average effective swath width in-canopy by crop compared to on ground. Swaths expressed from both percent coverage and deposit density metrics.
    Figure 16 Average effective swath width for each terrain. Swaths expressed from both percent coverage and deposit density metrics. Standard error bars presented. Bare ground (n=10). Wheat Stubble (n=12). Corn Canopy (n=21). Soybean Canopy (n=21). Wheat Canopy (n=3).

    Effect of Volume on SW

    The effect of spray volume on swath width is not immediately clear. When the data were expressed as deposit density, volume shared an inverse relationship with SW in canopy (Figure 17). There appeared to be no effect when expressed as percent coverage. The inverse relationship is weakly expressed, if at all, for both metrics on bare ground.

    Figure 17 Average effective swath width by volume and terrain. Swaths expressed from both percent coverage and deposit density metrics. Standard error bars presented. Canopy 20 L/ha (n=6). Canopy 30 L/ha (n=18). Canopy 50 L/ha (n=21). Ground 20 L/ha (n=6). Ground 30 L/ha (n=3). Ground 50 L/ha (n=13).

    Effect of Speed on SW

    For most RPAS designs, lower volumes are applied at higher flight speed (Table 5). Previous work demonstrated that higher flight speeds tended to result in wider swaths and an increase in drift. Do higher speeds cause wider swaths in-canopy, despite lower volumes?

    Volume Applied (L/ha)5 m/s Flight Speed8.3 m/s Flight Speed10 m/s Flight Speed
    203 treatments9 treatments
    309 treatments12 treatments
    5034 treatments
    Table 5 – Number of treatments for each flight speed by volume applied.

    Flight speed had a clearer impact on swath width than spray volume did (Figure 18). There was a positive relationship between flight speed and swath width as measured by deposit density in canopy and on bare ground.

    Figure 18 Average effective swath width by speed and terrain. Swaths expressed from both percent coverage and deposit density metrics. Standard error bars presented. Canopy 5 m/s (n=27). Canopy 8.3 m/s (n=12). Canopy 10 m/s (n=6). Ground 5 m/s (n=16). Ground 8.3 m/s (n=3). Ground 10 m/s (n=3).

    Just as with volume, there appeared to be no significant effect on swath width in either canopy when expressed using percent coverage. This was likely because finer sprays were better able to penetrate a canopy and deposit density is better able to resolve their presence.

    Conclusions

    There was no difference in SW between stubble and bare ground. The SW on-ground was far closer to the programmed 8 or 10 m swath width when expressed as deposit density.

    There appears to be a significant reduction of SW in-canopy versus on-ground. A crop canopy created a 26.6% reduction when expressed as deposit density. Specifically, corn was -20.6%, soybean was -28.3%, and wheat was -31.5%. Previous work has demonstrated diminishing coverage with canopy depth in corn, but it is difficult to make comparisons between agronomic use cases (e.g. different planting architectures and plant physiologies).

    When the data were expressed as deposit density, spray volume shared an inverse relationship with SW in-canopy, but the effect on SW on-ground was less clear. However, RPAS speed had a clear inverse relationship with SW in-canopy and strong trend on-ground.

    It is understood that finer spray is better able to penetrate canopies. One reason is because finer droplets are able to become entrained the downwash. Another is simply mathematical advantage, given that finer sprays are comprised of exponentially higher numbers of droplets than coarser sprays, increasing the odds of deposition. Conversely, coarser droplets (which have the greatest influence on percent area covered), are more likely to impinge on the canopy structure before reaching the sampler. Deposit density appears to be the more accurate metric for calculating SW both on-ground and in-canopy.

    The reduced SW in-canopy versus on-ground explains, in part, why striping is occurring in aerial corn fungicide applications. The route spacing reflects on-ground swath width, where it should reflect the shorter, ESW.