“If you can’t measure it, you can’t improve it”. While the source is nebulous (Peter Drucker, Lord Kelvin, or Antoine-Augustin Cournot), the sentiment is clear.
The status quo
In the world of crop protection, considerable resources are expended to distribute a pesticide over a target. And yet, sprayer operational settings and spray coverage are rarely assessed. As a result, too much time elapses between the application and observing the biological results to evaluate and correct equipment performance. The damage (be it waste or an inconsistent and sub-lethal dose) is done. All sprayer operators know this to be true, so why do precious few perform these assessments?
Perhaps, dear reader, you have personal experience assessing coverage and already know the answer. Perhaps you’ve performed the iterative dance that is placing, spraying, retrieving, assessing and re-placing water sensitive paper (WSP). Perhaps you’ve sprayed fluorescent tracers and hunted for faint glows at twilight using UV lights. Perhaps you’ve looked for residue from diatomaceous earth or fungicides. Or, perhaps, you’ve trusted in the falsely-comforting “shoulder check” and assumed dripping must mean you’ve hit the target.
Existing methods are complicated, subjective, messy and time-consuming. We need an alternative.
The alternative
Consider a permanent, solar-powered sensor that supplies real-time spray coverage data to your smartphone via a cellular connection. The output could be visualised in a simple and intuitive way, and immediately available to both sprayer operators and farm managers. If the sensor was relatively inexpensive, sufficiently hardy, and easy to deploy, its utility would only be limited by your imagination:
- Stakeholders could confirm the correct functioning of their equipment before committing to the application. Decisions could be made to change operational settings, repair equipment, or delay until conditions improved.
- The sensors would provide coverage data specific to their location and orientation. Units could be installed in difficult-to-spray regions such as treetops, or canopy-centres, or fruiting zones. Sensors could be placed where pest/disease pressure has been historically high, or where wind is a known issue.
- Large operations could install them in a test-row, where sprayer operators would perform a gauntlet-style calibration run prior to a day of spraying.
- Spray records could inform compliance audits, supplement insurance or CanadaGAP traceability requirements, or be used in agronomic assessments.
In 2025 I was approached by an Australian developer who claimed he had a device that did all of this. And, if that weren’t enough, it could also monitor certain meteorological factors such as pre-spray moisture levels and temperature and report post-spray evaporation rates. I could barely contain my excitement. A prototype was in my hands a few weeks later.
Benchmarking the sensor
The Spray Doctor (working name for the prototype) started its life as a leaf wetness sensor, evolving into a spray coverage sensor piloted in 2023/24 in Australian and New Zealand grape production. The history of earlier iterations and company schisms is convoluted, and fortunately immaterial to our purposes. All I needed to know was that we weren’t starting from scratch. Several of the questions regarding how accurately the surface could detect spray deposition were already addressed by independent research.
The sensing surface is impregnated with an array of capacitive wetness sensors. The sensor responds to the surface area covered and not deposit density. Researchers reported a reliable response range between ~10% and 50% surface coverage. Given the arguable “ideal” coverage standard of 10-15% surface area, this includes the range of interest for most sprays.
Benchmarking against WSP was part of the foundational assessment. A droplet of water deposited on WSP produces a high angle of contact and very little spread, while the same droplet deposited on plant tissue tends to produce a lower angle of contact and more spread. This means the stain produced on WSP is smaller than would be produced on plant tissue, depending on how smooth, vertical or waxy the tissue surface was.
It was therefore surprising that WSP were found to report a higher degree of spray coverage during water-only sprays than the sensor. It seemed droplets more easily coalesced and ran off the sensor surface. This was ultimately interpreted as an advantage, because the sensor would better emulate how a leaf surface would respond to the influence of surfactants and spray quality.
Adding a surfactant to a spray solution improves droplet adherence, and/or reduces surface tension, improving the degree of contact on plant surfaces. Likewise, it was found that surfactants increased the degree of coverage reported by the sensor, and when actual chemistry was sprayed (e.g. sulphur powder or copper sulfate) there was an effect on the degree of coverage reported. This is unlike WSP, where adjuvants and chemistry do little to increase the spread.
And so, like every method for assessing spray coverage, the sensor has limitations and caveats. If you have some doubt as to the sensor’s accuracy, do not get distracted by the fine detail. Remember, most operators currently have no feedback whatsoever; even a binary response (e.g. hit or miss) would be welcome. The sensor is sufficiently sensitive and consistent to resolve coverage in a range relevant to most sprays, and therefore worth field testing.
The experiment
My role in this story was to work with a grower to evaluate the sensor’s ability to report coverage information in a clear and actionable way. There were three questions:
- Does data from the sensor influence a sprayer operator’s behaviour?
- Does that change in behaviour lead to improved spray coverage (implying more efficient and effective crop protection).
- Could we “dial in” the hardware and the interface based on the grower’s feedback?
In part two, we share our experience installing and using the Spray Doctor, as well as supply answers to these questions. Stay tuned.
Thanks to Brandon Falcon (Falcon Blueberries) for volunteering his time and farm for this evaluation, and the developer for the in kind donation of the prototype Spray Doctor.