“Precision agriculture” is many things to many people. In the context of spraying, let’s define it as “detecting and responding to variability”. One example of precision ag is the use of crop-sensing optics to efficiently and accurately direct spray application. This is nothing new to field sprayer operators, but did you know that before Ken Giles published the first paper on pulse-width modulating nozzles in 1989, airblast sprayers already had crop-sensing technology?
In the 1970s, Bert Roper noted the wastefulness inherent to citrus spraying. Losses to the ground of 30-50% and off-target drift of 10-20% of applied volume were (and still are) not uncommon for airblast sprayers. So, using Polaroid’s autofocus technology, and enlisting the help of a few engineers, they developed an ultrasonic sensor system that enabled a computer to “see” the target tree and engage nozzles accordingly. He and son Charlie built prototypes in their kitchen in before proving it in their family groves, spraying 10 gal/ac instead of the usual 250 gallons. The first Tree-See system was sold to Cola-Cola in 1984.
This technology is still used today; Sensors detect specific zones on the canopy and actuate boom sections, or individual nozzles, to only spray the target zone. But optics and machine learning have evolved to modulate flow from individual nozzles in response to changes in canopy density. To be clear, that’s not just “on/off”, but variable flow. Eventually, these systems will be able to identify and respond to specific pests (or pest damage) and adjust plant growth modifier rates based on canopy density or bloom counts. The possibilities are amazing. As an aside, interested readers can learn more about airblast sensors in this excellent article from Oregon State University.
However, as operators embrace this technology, they should be aware of the current limitations. All these systems assume that application efficiency is primarily dependent on rates that match the profile (or perhaps density) of the target canopy. I don’t believe that’s true. The impact of air settings on coverage efficiency and efficacy have been long been known. Therefore, while canopy-sensing optics are great at managing waste (their primary selling point seems to be pesticide savings), they can only promise “coverage potential”.
For example, they do not account for the spray’s ability to span the distance from nozzle to target (i.e., transfer efficiency). That depends on the droplet size, sprayer air settings and the environmental conditions – none of which are monitored by sprayer optics. They also cannot “know” if the spray gets intercepted by the target (i.e., catch efficiency) or if it deposits a biologically-active residue on the target surface (i.e., retention). Droplets must be retained by the target surface and not bounce or slide off.
What this means is that airblast optic systems are, ultimately, glorified rate controllers. Certainly, this is less of an issue when crop morphology, planting architecture and environmental conditions are less variable, but operators are still required to perform the following tasks:
- Optimize air direction and air energy in relation to canopy size, travel speed and environmental conditions.
- Use water-sensitive paper, or some other means of quantifying coverage, to ensure your target receives threshold coverage.
- Monitor and adjust practices throughout the season in response to changing conditions.
What would improve matters? In my opinion, I believe the pitcher needs a catcher – a closed-loop feedback system. Optics would identify the target, nozzle flow would respond, and then a digital sensor in the target canopy would detect and report coverage back to the sprayer so machine learning could make iterative adjustments in real-time.
Spray-sensors are not a new idea, as wetness-detection systems have been used in forestry since the 70s. But, a sensor that can discern spray coverage would yield far more detail, and once again it seems Ken Giles is a pioneer in this concept. Such a sensor, integrated with sprayer optics and machine learning, could summarily account for all the unknowns that interfere with spray from the moment it’s released to the point that it (hopefully) lands. That’s some serious crop-adapted spraying.
Until then, sprayer eyes can only blindly dictate the release of spray into the airstream based on an assumed coverage constant (e.g., 1.2 oz./ft3). It remains for the sprayer operator to act as the brain, optimizing sprayer settings, quantifying coverage, and making changes to reflect conditions.
Learn more about how to optimize the fit between your airblast sprayer and your target by downloading a free copy of our Airblast 101 textbook.