Tag: coverage

Spraying Ginseng with Arag Microjets

In June 2013 we ran a ginseng spraying workshop and we learned as much as the growers did. Ginseng is notoriously difficult to spray:

It is highly susceptible to pathogens given the high humidity and still conditions generally found under the shade structure.
It forms a solid ceiling of leaves that resist spray penetrating to the stem and crown below and makes under-leaf coverage very difficult to achieve.

Many growers have (wisely) walked away from the old Casotti sprayers, which have been shown to give erratic coverage at best. They have adopted the Arag Microjet system with it’s characteristic orange shields. The >$80.00 CAD price tag for each nozzle is due to the brass mixing valve and swivel joint, as well as import costs from Italy. Contrary to popular belief, it does not use air-assist, or air-induction – it is strictly hydraulic. It does tend to create a ‘wake’ of air movement at high pressure. This phenomenon is called air entrainment and it is caused by large droplets travelling at high speed.

This nozzle is essentially the business-end of a spray gun. The way it is used in ginseng it works more-or-less like a hollow cone disc-core assembly. This begs the question “Why not use the cheaper and more readily available ceramic disc-core?” We set out to compare the two options using water sensitive paper set within the canopy. These yellow, paper targets turn blue when sprayed, clearly showing spray coverage.

Location of water-sensitive papers in the ginseng canopy. — Location of water sensitive papers in the ginseng canopy.

Determining rates

The first step was to determine the output rate for each nozzle. Generally, nozzle manufacturers provide rate tables showing how much volume a nozzle emits by time (e.g. US gallons per minute) at a given pressure. Finding these tables for the 1.5 millimetre Arag Microjet proved difficult. When we finally found one, it was discovered the rates were established for 200 to 850 pounds per square inch. This is excessively high pressure for a typical boom sprayer, so tables had to be developed for lower pressures.

Classic Arag microjets have a mixing valve that opens the spray up into a hollow cone, or collapses it into a tight stream. This also changes the rate. It can never be shut off completely, and it's hard to adjust consistently. — Classic Arag microjets have a mixing valve that opens the spray up into a hollow cone (valve handle left or right), or collapses it into a tight stream (valve handle middle). The valve position also changes the rate. It can never be shut off completely, and it’s hard to adjust consistently.

Determining nozzle rate using the Innoquest Spot-On SC-4.

Further, given the odd design of the mixing valve, it was determined that moving the handle ~10 degrees left of centre, or ~10 degrees right of centre, gave a difference of as much as 60%. The table below shows the outputs for a 1.5 millimetre nozzle with the handle in both positions and the two graphs show the results… well… graphically. Outputs were determined using the Innoquest Spot-On SC-4, but the frothing effect created by the nozzles may have created minor errors. Each rate is the average of a minimum of three samples.

Valve Setting	Pressure (psi)	Avg Output (gpm)	Pressure (bar)	Avg Output (L/min)
10 degrees left	40	1.02	2.76	3.86
10 degrees left	50	1.1	3.45	4.16
10 degrees left	60	1.25	4.14	4.73
10 degrees left	70	1.25	4.83	4.73
10 degrees left	80	1.38	5.52	5.22
10 degrees left	90	1.4	6.21	5.3
10 degrees left	100	1.45	6.89	5.49
10 degrees left	110	1.6	7.58	6.06
10 degrees left	120	1.75	8.27	6.62
10 degrees left	150	1.87	10.34	7.08
10 degrees left	200	2.2	13.79	8.33
10 degrees right	40	0.65	2.76	2.46
10 degrees right	50	0.7	3.45	2.65
10 degrees right	60	0.8	4.14	3.03
10 degrees right	70	0.85	4.83	3.22
10 degrees right	80	0.9	5.52	3.41
10 degrees right	90	0.9	6.21	3.41
10 degrees right	100	1	6.89	3.79
10 degrees right	110	1.07	7.58	4.05
10 degrees right	120	1.1	8.27	4.16
10 degrees right	150	1.25	10.34	4.73
10 degrees right	200	1.37	13.79	5.19

Average 1.5 mm ARAG Microjet output at a range of pressures and two valve settings in US Imperial units.

Average 1.5 mm ARAG Microjet output at a range of pressures and two valve settings in Metric units.

Comparing nozzles

Using the grower’s typical ground speed of 5 km/h (~3 mph) and operating pressure of 6.9 bar (100 psi), we found four TeeJet disc-core combinations that emitted a hollow cone pattern and approximately the same output as the Arag Microjets. The five nozzles sets tested were:

ARAG Microjet® 1.5 mm = ~0.95 US g/min avg at 100 psi
TeeJet® D8-DC25= 0.97 US g/min at 100 psi= ~97° cone
TeeJet®D7-DC45= 0.97 US g/min at 100 psi= ~81° cone
TeeJet®D4-DC46= 0.88 US g/min at 100 psi= ~33° cone
TeeJet®D6-DC45= 0.93 US g/min at 100 psi= ~81° cone

We did not use nozzle drop hoses (aka drop arms or hose drops) because it has already been firmly established that they are absolutely required to achieve under leaf coverage See OMAFRA factsheet 10-079 and this article.

Observations

While there were some complications with setting up the papers for the demo, we observed the following:

The output of each Microjet nozzle can be as much as 50% more or less than expected without being visually detectable and output for each nozzle must be confirmed before spraying. Therefore, outputs should be confirmed before every application.
Microjets at 100 psi emitting ~890 L/ha (~95 US gallons per acre) gave satisfactory coverage on all upward facing targets, but unsatisfactory under-leaf coverage. This has been demonstrated many times before.
The TeeJet D7-DC45 combination emitting a similar rate gave satisfactory coverage on all upward facing targets, but unsatisfactory under-leaf coverage. They may be a viable alternative to the Microjets.
Nozzle drops are advised to achieve under-leaf coverage.

The demo also raised some questions:

Did the TeeJet disc-core push the canopy apart as much as the Microjet? The audience noticed there was some leaf-shadowing where the cards did not get complete coverage using disc-core. This might have been coincidence, or it may not have. This question will be addressed in a research trial next season, but for now, the D7-DC45 appeared to give similar coverage to the Microjet.
Can nozzle drops be avoided if pressure is raised to 27.5 bar (400 psi)? Thanks to one grower trying this experiment in his garden after the demo, we saw some under-leaf coverage is possible at such high pressures, but this occurred at the cost of a lot of noise, diesel fuel and considerable wear on the ceramic Microjet discs. The grower tested these tips and discovered they needed replacement after only two years of use. Nozzle drops are cheaper, easier and result in considerably more spray in the under leaf positions.
We saw what minimal and excessive foliar coverage looked like, and determined how much variability there was from one nozzle to another. A significant question was “How much spray can be saved when using a more accurate application?” and the answer is yet to be determined, but could be well in excess of 10% of the typical spray volume. Given that this crop can be sprayed more than 100 times over it’s 3 or four years before harvest, this represents significant savings in pesticides and refill time.

Additional – Newer ARAG Microjet Design

Since this work was performed, growers have been exploring a newer option from ARAG.

They are an improvement over the older version insofar as they are more easily calibrated and held at a given rate thanks to a lock nut. They still employ a 1.5 mm diameter ceramic disc, but this can be changed for a 1.0 or 1.2 quite easily. They are still somewhat finicky when trying to set a consistent spray quality and rate from nozzle to nozzle, but are better than the mixing-valve option.

Learn more in this article.

Custom-made ginseng sprayer. A standard design. — Custom-made ginseng sprayer. A standard design with newer, cheaper and easier-to-use ARAG microjets.

Special thanks to Syngenta Canada for providing lunch, to C&R Atkinson Farms Ltd. for hosting, to TeeJet for supplying the disc-cores and water-sensitive papers, and to Dr. Sean Westerveld, Dr. Melanie Filotas and OMAFRA summer student Megan Leedham for contributing to the workshop.

September 7, 2021

Assessing Water Sensitive Paper – Part 3
This is the final part of our three-part article discussing methods for digitizing and processing water sensitive paper. You can read part one here and part two here.
Morphological operations
We can now move on to the larger shapes, or “morphology” of the objects in our binary image. Our goal is to quantify deposits by interpreting these shapes. Once again, these operations are powerful processing tools, but we must acknowledge three overriding limitations:
1. Inconsistent stains
Sometimes deposits do not create a consistent blue colour – they can get lighter or take on a greenish-yellow hue towards the perimeter of the stain. During thresholding, the outer edge can be accidently eroded, leaving behind an object with a jagged edge. This may lead us to underestimate the percent area actually covered. In the case of tiny stains, it might eliminate them entirely and lead us to underestimate deposit density.
2. Overlaps
It can be difficult to determine if an object represents a stain from a single droplet or is the result of multiple, overlapping deposits. This becomes significant when the surface of the WSP exceeds ~20% total coverage. The resulting objects may or may not have hollow centres where droplets do not overlap entirely. Misidentifying overlaps leads us to falsely conclude that an object is the result of a single, coarser droplet rather than multiple finer droplets.
3. Ellipses
Non-circular stains are formed when droplets scuff along the surface. Two droplets with the same volume encountering a paper at different angles can create stains with significantly different areas. We may wrongly conclude that the droplets that created them were coarser than they truly were. One approach is to use Feret’s Diameter (aka Caliper Diameter) by measuring the widest spans on the X and Y axes and taking the average. Another approach is to interpret the ellipse as a series of circular stains. Or we can decide to only acknowledge these objects when calculating percent area covered, but omit them when calculating deposit density or predicting original droplet size. Each strategy is a compromise, so it is important to be consistent and transparent when reporting results.
Three common problems when analysing water sensitive paper.
We’ll explore two morphological operations that can help us separate fact from fiction: Granulometry and Dilation-and-Erosion. We’re introducing these operations as part of the processing and detection step, but they may also overlap with the measurement step in our three-step process.
Granulometry
We can estimate the range of object sizes and get a sense of how they are distributed on the paper by filtering or “sieving” the image. Imagine pouring a mixture of sand and rocks through a series of ever-finer sieves. Doing so allows you to separate particles based on size exclusion. A granulometry function compares each object to a series of standardized objects with decreasing diameters. This isolates objects of a similar size and bins them in that size range. This is a powerful operation, but accuracy is lost when stains overlap to form larger objects. In this case, we move on to Dilation and Erosion.
Dilation and Erosion
Think of dilation as adding pixels to the boundary of an object. This makes tiny objects bigger, fills in any interior holes and can cause objects to merge. The number of pixel-wide dilations required to make objects contact one another can be used as a measure of deposit density.
Erosion removes pixels from the outer (and sometimes inner) boundaries of an object. This eliminates tiny artifacts that may not actually represent stains. It can also split non-circular objects into multiple parts before shrinking them into multiple nuclei (aka centroids). These last-remaining points are not necessarily the centre of a stain, but the pixels furthest away from the original boundary.
When a non-circular shape has more than one nucleus, they likely represent individual droplets that combined to form the larger stain. We can then use these nuclei to measure deposit density, such as in a Voronoi partition which triangulates each nucleus in relation to the two closest neighbours.
Many image processers use both these operations sequentially. When an image is eroded and then dilated (a process called “Opening”), smaller objects are removed, leaving the area and shape of remaining objects relatively intact. Dilating and then eroding (a process called “Closing”) fills in small holes and merges smaller objects, once again leaving the area and shape of remaining objects relatively intact. We can use both of these functions to help smooth an image prior to measurement.
(Top) Opening operations erode and then dilate the image. Moving left to right, the smaller objects tend to disappear. (Bottom) Closing operations dilate and then erode the image. Moving left to right, smaller objects either disappear or merge and holes are filled in
Distance Transformations
Distance transformations are advanced operations specifically used to separate objects that are densely packed. While not typically used when analyzing WSP, distance transformations are another means of identifying object nuclei. They are another means for teasing apart objects that are likely the result of overlapping deposits and then mapping their relative sizes and positions.
Measurement
The calculation of the area covered by deposits is straightforward. The pixels belonging to objects (the deposits) and those belonging to background are summed and then the fraction is converted to percent area covered. Research has shown that the image resolution does not significantly impact percent coverage assessments and has suggested that all image analysis software tends to produce similar results (+/- 3.5% observed when the same threshold was applied to multiple papers). This is acceptable because it’s within the variability inherent to spraying.
We ran a similar experiment wherein we analyzed the same piece of WSP using four methods. Here are a few facts about the software we used:
- DropScope produces images between 2,100 and 2,300 DPI. Currently, it ignores ellipses and doesn’t count anything spanning less than ~35 µm (3 pixels).
- We set ImageJ to ignore any object spanning less than 3 pixels, which at 2,400 DPI was 30 µm in diameter.
- We are unaware of Snapcard’s processing methods except that the software was benchmarked using ImageJ. Developers note it will underestimate the percent area covered if the image is out of focus. (Note: As of 2026, this app may no longer be supported by the GRDC).
The images shown in the figure below were cropped from screenshots produced by each method. The actual ROI analyzed was ~3 cm² for SnapCard, 3.68 cm² for DropScope and 2.0 cm² for both Epson/ImageJ methods. Our results indicate an +/- 4% difference in percent area coverage. This variability reflects the results of a 2016 journal article that compared SnapCard with ImageJ and other leading analytical software. That study claimed no statistically significant difference in percent coverage detected (standard deviations were about 20%). However, the ImageJ results tended to trend several percent higher than SnapCard. We saw this as well. And so, while resolution may not have a significant impact on percent area covered, there does appear to be some correlation.
Percent area covered as reported by three image analysis systems. Only a minor difference was observed when resolution was doubled using the Epson/ImageJ method.
Resolution definitely affects deposit counts. Particularly in applications that employ finer droplets. Consider the difference between detecting or missing 1,000 30 µm diameter objects. It may only amount to a fraction of a percentage of the surface covered, but +/- 1,000 objects on a 2 cm² area is significant in terms of deposit density.
Output
Once a WSP image (or set of images) has been scanned, pre-processed, processed and measured, we will receive some manner of output. Some software packages create an attractive report with images, graphs and key values. These reports include percent coverage and many provide droplet density. Deposits may be binned by size, or spread factors are used to calculate the original droplet diameters and even estimate the volume applied by area. Other software packages provide raw data that can be imported into a statistical program or spreadsheet program like Excel for further analysis. Some software packages provide both.
How far can we take this?
Blow-by-blow data analysis is beyond the scope of this document, but how much weight should we give to coverage data obtained using WSP? The answer depends on the metric in question, but in all cases we must first acknowledge the three overriding caveats. Take it as said that they apply to everything that follows:
1. Different brands (and even different production runs) of WSP can produce significantly different coverage metrics. When conducting experiments, use a single brand of WSP. Better still, use papers from the same production batch whenever possible.
2. The same of piece of sprayed WSP can produce significantly different results depending on the software and protocol used to analyze it. When conducting experiments, use the same software and assessment protocol and be transparent about the process when communicating results.
3. WSP coverage may not reflect the coverage achieved on an actual plant tissue surface. It is suitable as a relative index (I.e. papers can be compared to papers, but not to tissues) but the spread factor changes with surface wettability and the surface tension of the liquid sprayed. Note the differences in percent area covered in the following experiment with an organosilicone super-spreader:
Difference in deposit spread on water sensitive paper versus a leaf surface using an organosilicone super-spreader and UV dye. The same volume was applied in each case and while the area increased two-fold on WSP it increased ~10-fold on an actual leaf. Image reproduced from work by Robyn Gaskin, Plant Protection Products, New Zealand.
Recall that we started this document by listing the four pieces of information commonly sought using WSP. They were listed in order of reliability, and now we can explain why.
- The percent surface area covered: We have established that this is the most reliable piece of data. Droplets do not spread on WSP the way they do on plant surfaces, so it will underestimate actual coverage. The results vary by analytical method, but it’s likely not dependent on resolution and still falls within the variability inherent to spraying. This metric gives us valuable and actionable information. We can say whether or not we hit a target, and evaluate whether a sprayer change resulted in more or less deposit.
- The density of deposits on the target area: We have established that that there are limits to the reliability of this metric. It is affected by the analytical method used and can be greatly underestimated when resolution is poor or when deposits overlap in high numbers. Also, it will never reliably reflect deposits under 30 µm. Nevertheless, under controlled conditions this information does have value and is of great interest in enquiries about drift and contact fungicides.
- The size of the droplets that left the stains: This metric is highly questionable except under controlled conditions. The many assumptions about surface tension, droplet speed, and droplet evaporation make it impossible to make definitive statements about spray quality. Finer droplets are greatly underestimated in this equation. Therefore, while there may be some value in using WSP as a relative index, this metric is a crude indication at best.
- The dose applied to the target surface: This metric has not been discussed up to this point, but is quickly and easily dismissed. Let’s assume that a droplet with a high concentration of an active ingredient will leave a stain that is the same area as another droplet with a lower concentration. This will lead some to suggest that as long as the original concentration is known, we can back-calculate the dose (which is the amount of active on a given area). However, one droplet has the same volume as eight droplets that are half it’s diameter. This cubic relationship means that if they all deposit, the larger droplet will cover roughly 1/2 the surface area as the eight smaller droplets. Therefore, the smaller droplets spread the same amount of active over a greater area. Spread factor muddies this a bit, but ultimately it means that dose cannot be estimated from area covered. Dose is better assessed using collectors that permit the residue to be removed, such as Petri dishes, Mylar sheets, pipe cleaners, alpha cellulose cards, or glass slides.
And so, the image analysis process described here is powerful and effective when used with water sensitive paper as long as the limitations are acknowledged. The same process can also be used with dyes and specialized collectors such as Kromekote to permit even greater resolution. But that’s another story.
References (Further reading)
Bankhead, P. 2014. Analyzing fluorescence microscopy images with ImageJ.
Cunha, J.P.A.R., Farnese, A.C., Olivet, J.J. 2013. Computer programs for analysis of droplets sprayed on water sensitive papers. Planta Daninha, Viçosa-MG. 31(3): 715-720.
Ferguson, J.C., Chechetto, R.G., O’Donnell, C.C., Fritz, B.K., Hoffmann, W.C., Coleman, C.E., Chauhan, B.S., Adkins, S.W. Kruger, G.R., Hewitt, A.J. 2016. Assessing a novel smartphone application – SnapCard, compared to five imaging systems to quantify droplet deposition on artificial collectors. Computers and Electronics in Agriculture. 128: 193-198.
Ledebuhr, M. 2016. Small Drop Sprays.
Marçal, A.R.S., Cunha, M. 2008. Image processing of artificial targets for automatic evaluation of spray targets. Trans. of the ASABE. 51(3): 811-821.
Moor, A., Langenakens, J., Vereecke, E., Jaeken, P., Lootens, P., Vandecasteele, P. 2000. Image analysis of water sensitive paper as a tool for the evaluation of spray distribution of orchard sprayers. Aspects of Applied Biology. 57.
Panneton, B. 2002. Image analysis of water‐sensitive cards for spray coverage experiments. Applied Eng. in Agric. 18(2): 179‐182.
Salyani, M., Zhu, H., Sweeb, R.D., Pai, N. 2013. Assessment of spray distribution with water-sensitive paper. Agric. Eng. Int.: CIGR Journal. 15(2): 101-111.
SnapCard website. University of Western Australia and the Department of Primary Industries and Regional Development, Western Australia. (Note: As of 2026, may no longer exist).
Syngenta. 2002. Water‐sensitive paper for monitoring spray distributions. CH‐4002. Basle, Switzerland: Syngenta Crop Protection.
Turner, C.R., Huntington, K.A. 1970. The use of a water sensitive dye for the detection and assessment of small spray droplets. J. Agric. Eng. Res. 15: 385-387.
July 27, 2021
Assessing Water Sensitive Paper – Part 2
This is part two of our three-part monster-article on methods for digitizing and processing water sensitive paper. You can read part one here.
Image analysis software
There are many choices of software designed to analyze digitized WSP images (E.g. Optomax, Stainalysis Freeware, DropVision, ImagePro Plus, DropletScan, AgroScan, DepositScan, UTHSCA ImageTool). Some were developed for aerial applicators to evaluate entire swaths and others to focus on single collectors. Some are more user-friendly than others, some cost money, and some are no longer supported. All of them employ algorithms (a set of rules a computer follows when making calculations) that often make image processing decisions. Sometimes these algorithms are pre-set, which can be convenient but may also restrict our analysis.
ImageJ is a free, open-source application developed at the National Institute of Health by Wayne Rasband to adjust and analyze high-resolution images of small structures. There’s a variation called “Fiji” (Fiji Is Just ImageJ) which bundles ImageJ with tools specifically intended for biologists. Happily, they are equally valuable for analyzing WSP. The interface can be intimidating, but only because there are so many functions that we won’t be using. The learning curve is worthwhile because the user has complete control over the analysis.
The ImageJ menu. Version 1.53e.
Three steps to image analysis
No matter the software, the operations used to analyze a digital image tend follow a three-step progression:
1. Pre-processing: We select an ROI (a Region Of Interest) in the image and perform a few preliminary operations to improve image quality and contrast. In selecting a specific region, we can avoid unwanted flaws like drips or fingerprints as well as crop the image to some standard size for scaling purposes.
2. Processing / Detection: Point and Morphological Operations are used to refine the image and establish a threshold so we can differentiate between deposits and the unstained background. The ideal outcome sees the original colour image converted to a binary (typically black and white) image.
3. Measurement: We use ready-made computational routines to quantify some value. Typically, the percent area covered by deposits, but possibly the count and density of those deposits and perhaps even an estimate of the original droplet size.
Let’s explore each of these steps.
1. Pre-processing step
Pre-processing establishes the scale of the image and allows us to isolate the specific region we want to analyze. Perhaps the water sensitive paper was folded during sampling and we want to analyze each half separately. Perhaps we want to avoid obvious imperfections that would interfere with our results. In some cases, pre-processing might also include adjusting the image brightness to improve the contrast between stains and the yellow background.
Flaws and imperfections become obvious when water sensitive paper is examined under magnification. Part of pre-processing is to select a region of interest that represents typical coverage and does not include artifacts that might interfere with the analysis.
2. Processing / Detection step
Processing and detection can take time because of the degree of computation involved. The higher the resolution and the larger the ROI, the longer it will take. Depending on what you want to measure, it might be acceptable to sacrifice some accuracy for speed.
We begin by determining which pixels represent part of a deposit stain and which represent part of the unstained background. We can accomplish this through global point operations called Thresholding and Filtering. If you haven’t already noticed, image analysis includes has a lot of jargon: “global” means the entire image and “point” refers to our focus on individual pixels. Ultimately each pixel is assigned one of two values, reducing the image to a binary (or 1-bit) format.
Once we have a binary image, we explore the shapes of the deposits (which are sometimes referred to as objects) to determine the limitations of what we’re confidently able to measure. Morphological operations are used to refine or modify these shapes in order to smooth jagged edges and identify whether an object is the result of a single deposition or multiple overlapping deposits.
3. Measurement step
Depending on the image analysis software, the user may be limited in what they can measure. The spectrum ranges from a single value (usually percent area covered) to in depth data relating to each object in the image. The latter might appear in a pre-formatted report, or as a CSV (Comma Separated Value) file for further exploration in spreadsheet format.
Thresholding
A thresholding operation sorts all the pixels in an image by some characteristic, and then allows us to set a threshold dividing them into two camps. In our case, we want to divide them into “stained” and “unstained”. The process is almost like taring a scale, where anything above the weight of the container is identified as the weight of the contents.
Thresholding is like taring a scale. Just as the weight of the container is isolated from the total weight of the container and contents, the stain colour is isolated from the background colour.
The HIS thresholding operation
ImageJ’s Colour Threshold operation is only one way to threshold an image, but it serves as a good example. This method uses HIS (Hue, Intensity and Saturation) to separate the deposit stain colours (blue-green) from the background colour (Yellow). As discussed, each pixel is represented by one or more 8-bit values. In this case pixels represent 0 – 255 hues, 0 – 255 intensities and 0 – 255 saturations. That may seem intimidating, but we mostly focus on hue.
When the Colour Threshold operation is selected, ImageJ sorts all the pixel values in the image into a binned histogram (where the Y-axis is the pixel count and the X-Axis is the range of pixel values).
a. Hue
We begin by considering the hue, which is simply another word for colour. An image with no stains would produce a histogram with pixel colours producing a distinct peak in the yellow range. An image with stains would also display peaks in the blue-green range. The user then segments the background from the foreground by manually setting the threshold between these peaks.
(Top) When the threshold overlaps the background yellow hue (set to 30 here), some portion of the background is falsely identified as a stain. (Bottom) When the threshold is adjusted to fall in between background hue and stain hues (set to 36 here), a sharper distinction is made.
The hue thresholding process is less reliable (or can outright fail) when WSP has coverage in excess of 50%. This is because the color of the intermittent unstained areas changes as the distance between stains decreases. Think of it as blue bleeding into the yellow. The result is that the level of contrast between stained and unstained regions is inconsistent, making it difficult to confidently differentiate between “stained pixels” and “unstained pixels”. Similar issues arise when humidity causes the background colour to change but this tends to be more uniform and easier to threshold.
If the hue threshold it is set too low, stains will appear smaller and lose their shapes. If it is set too high, stains will appear larger and the gaps between adjacent, separate stains can disappear. This can have a significant impact on deposit count and distribution assessments, resulting in the loss of thousands of tiny, distinct deposits. Threshold accuracy has less impact on the determination of percent area covered. Research has shown that the use of a single threshold for multiple papers gives an absolute error of +/- 3.5% area covered. This is considered well within the intrinsic variability of spray coverage data.
b. Intensity
Sometimes referred to as “Value”, intensity can be thought of as pixel brightness. No threshold is required here because capturing the entire 256 pixel value range improves the contrast between colours.
c. Saturation
Finally, saturation (a measure of the difference between red, green, and blue levels) is a useful thresholding adjustment when WSP has been exposed to humidity. Humidity does not affect WSP’s ability to resolve stains, but as we mentioned it can cause the background to take on an overall greenish hue. An increase in low end saturation limit can increase the contrast between the stain colour and a less-distinct background colour.
When the HIS thresholds are set, ImageJ converts the pixels values closer to the foreground (stains) to black and those closer to background (yellow) to white. Users can invert this if they wish, or even make the stains red. The important part is that we now have a binary image that makes a clear distinction between the stains and the unstained background. Ideally, thresholding should be performed for each consistent set of samples.
Learn how HIS thresholding is being used to perform weed recognition functions on sprayers in this article.
This 2 cm x 2 cm ROI was HUI thresholded. You can double check your accuracy by having ImageJ “show outlines” which outlines and numbers each distinct object. Zoom in to see if any artifacts remain (or were inadvertently created) and go back to make minor thresholding adjustments if needed.
Pixel filtering
We won’t belabor filtering because it isn’t often required when analyzing WSP. Filtering operations compare pixel values to those of their neighbours and then replace those values with some form of weighted average. This reduces the relative differences between pixel values, smoothing the image and reducing noise (at the cost of lost detail).
Last article –Part three: Morphological Operations and Interpretation.
July 12, 2021
Comparing Water Sensitive Paper Brands
Introduction
Spray coverage describes the degree of contact between spray droplets and the target surface area. This metric can be used to predict the success of an application. One of the easiest methods for visualizing coverage is to use water sensitive paper (WSP), which is a passive, artificial collector that turns from yellow to blue when contacted by water.
WSP is often used to evaluate iterative changes to a spray program. Placed strategically throughout a target canopy, or directly on the ground, achieving uniform, threshold coverage translates into improved efficacy, reduced waste, reduced off-target contamination and reduced risk of pesticide resistance development. WSP were also used to develop a system that measures the area covered by the effective radial distance in an attempt to relate the area covered by a stain to a larger area where sufficient pesticide activity is taking place.
WSP tends to underestimate the spreading effect that can occur on plant surfaces (especially when surfactants are used), but they are effective as a relative index.
A brief history of WSP
In 1970, a journal article described a new method for sampling and assessing spray droplets. Photographic paper treated with bromoethyl blue created a yellow surface that changed colour when it encountered moisture. The pH-based reaction was fast and irreversible, leaving a distinct blue stain to mark the deposition.
Ciba-Geigy Ltd. made water sensitive paper commercially available in 1985 (later as Novartis in 1996 and as Syngenta since 2000). It is produced in several formats, but aluminum foil packages of 50, 76 x 22 mm (1 x 3 in.) papers are the most popular. Odds are if you’ve ever used water sensitive paper, it originated from Syngenta in Switzerland. In 2023 I noticed that the papers now say “made in Germany.”
Change of manufacturing location?
In recent years, two new options have been made commercially available: Innoquest’s SpotOn Paper (United States) and WSPaper (Brazil). At the time of writing, there has been no impartial comparative evaluation of these three products.
Once dry, the blue stains on WSP are irreversible and papers can be stored for long periods of time. However unstained portions will continue to react to moisture from humidity, dew, or fingerprints, so care must be taken in their handling and storage.
Comparing WSP brands
The three commercially-available brands of WSP were subjected to a series of comparisons. The intention was not to rank these products, but to determine if they performed in a similar fashion and to alert users to any significant differences.
Packaging and Appearance
Each package was donated for the study. The SpotOn (SO) papers had a “sell-by” date of November 2023, the Syngenta (SY) papers (provided via Spraying Systems Co.) were dated February 2021 and the WSPaper (WS) was their newest formulation (white package, not silver), received June 2021. The comparison was performed on July 5, 2021.
WSP packages.
Each product was a foil or plasticized bag of 50, 26 x 76 mm papers. SO and WS had a re-sealing feature similar to that of a sandwich bag. SO also included a package of silica gel desiccant to capture moisture and a pair of plastic forceps to facilitate handling.
Users are encouraged to label papers to ensure they know their relative position and sprayer pass for later analysis. It was possible to write in ink on the faces of the SY and SO papers, but not WS. It was possible to write on the back of all brands.
The three papers were different shades of yellow. Further, in the author’s experience, the colour can be visibly different between batches of the same brand. In the case of larger experiments where more than 50 papers are required, it would be prudent to ensure papers are not only from the same manufacturer, but the same production batch. This would not be an issue when subjectively comparing papers, but when using software that employs colour thresholding to identify deposits, it could create artifacts. Presently, only Syngenta has a batch number (found on a sticker on the back of the bag).
Bleed-through
WSP is often placed in foliar canopies which are subject to dew and transpiration that can cause the papers to react prematurely. This can be particularly limiting when moisture soaks through the backs of papers. Each brand of paper was placed face-up on a drop of water to see if the water would bleed through.
Three brands were placed on a single drop of water. Within five minutes, WSPaper and Syngenta brands wicked the water through, causing a colour reaction. SpotOn did not, although the yellow surface darkened. When a drop of water was applied to the face, the SpotOn paper still produced a blue stain.
WS quickly curled as the water wicked in from the edges. Within five minutes the water soaked through from the back as well. Within five minutes SY also curled, but the colour reaction was entirely due to water soaking through and not wicking along the edges of the paper. SO did not curl and there was no colour reaction save a minor wicking reaction at one edge. It did however produce a dark yellow patch. In order to see if a colour reaction was still possible, a single drop of water was placed on the face and the colour reaction was distinct and instantaneous.
Note: Others have since replicated this experiment and reported that the response depends on the amount of water used and how long you leave it. We repeated our experiment with higher volumes and longer wait times (see image below). Ultimately, no brand of WSP is water proof from the back. Nevertheless, with small volumes of water (such as from dew) the original assessment of each brand is still valid.
A replication of the bleed-through experiment with the same batch of papers was performed with higher water volumes and a longer duration. Eventually, all three brands bled through. (SpotOn left, WSPaper middle, Syngenta right).
Deformation and drying time
Users of water sensitive paper may be familiar with its occasional tendency to curl when one side is sprayed. In extreme cases, this movement could create smears if the paper contacted other wetted surfaces in dense foliage. The degree of curling was significantly different by brand, with SY becoming convex when wet and then flexing back into a concave form once dry. WS deformed as well, but only to a minor degree. SO did not appear to deform at all. Syngenta has noted that the degree to which their papers curl depends on the batch. Their manufacturing process has changed over the years in response to regulatory requirements and minor adjustments are still occasionally made.
Once dry, each brand of WSP tended to curl to different degrees. Syngenta curled the most and SpotOn the least if at all.
There was no appreciable difference in the time it took for any brand to dry. This is based on attempts to smear papers every 30 seconds. All were dry in under five minutes.
Experimental design
While there is considerable variability inherent to spraying, every effort was made to maintain consistent conditions. Papers were sprayed in a closed room with no appreciable air currents (21.5 °C and 64% RH). Papers were paired randomly, side-by-side on a plastic sled. The sled was pulled at 2.5 kmh (~1.5 mph) through the centre of a spray swath produced by a TeeJet XR80015 positioned 50 cm (20 in.) above the targets. The nozzle operated at 2.75 bar (40 psi) to produce ~270 L/ha (~29 gpa) with Fine spray quality. Six passes were made, producing four sprayed papers for each brand.
All papers were dry to the touch after two minutes. They were removed to a cooler, low humidity space and were digitized and analyzed using the SprayX DropScope (v.2.3.0) within an hour of spraying. We noted that while WS and SO fit easily into the DropScope port, the SY papers were sometimes slightly wider and had to be forced. Learn more about how to digitize and analyze WSP in this series of articles.
Screen capture from DropScope’s smartphone app.
The “ground” option was selected, and each brand of paper was processed using its specific spread factor. DropScope has a detection threshold of 35 µm. This is appropriate as the smallest droplet diameter that can be resolved by any brand of WSP is ~30 µm (Syngenta, Innoquest, SprayX – Personal Communication).
Percent surface covered
The average percent surface covered was calculated with standard error of the mean for each paper. WS and SO produced similar values between 30 and 35%. While all three brands exhibited similar variability, SY approached saturation at approximately 80% coverage. Therefore, WSPaper exhibited a slightly higher degree of spread than SpotOn, while the Syngenta paper exhibited a significantly higher degree of spread.
For reference, it can be difficult to determine if a stain represents a single deposit or is the result of multiple overlapping deposits. This becomes a problem when the surface of the WSP exceeds 20% total coverage. Further, it becomes increasingly difficult to distinguish a stain from the background, unstained surface when papers exceed 50% total coverage.
Average percent surface coverage by brand.
DropScope-digitized images of three brands of WSP. The Syngenta and SpotOn papers were sprayed simultaneously while the WSPaper was sprayed in a subsequent pass. WSPaper exhibited a slightly higher degree of spread than SpotOn, while the Syngenta paper exhibited a significantly higher degree of spread.
Deposit density
The average deposit density is a count of discrete objects (i.e. stains) per cm². WS appeared to resolve the highest count, followed by SY and then SO. The process for determining what is a discrete object, and not the result of anomalies such as overlapping deposits, elliptical deposits or imperfections in the paper itself is complicated and computationally heavy. The algorithms employed by DropScope treated each paper consistently. So, while some differences are attributed to variations in spraying, they also reflect the paper’s innate ability to resolve individual deposits.
Average deposit density was highest for WSPaper, then Syngenta, then SpotOn. Variability was similar in all cases.
Droplet diameter
It is not the intent of this article to determine if WSP should be used to extrapolate the original droplet size. The many assumptions and inconsistencies inherent to this process are well known. Nevertheless, some researchers do use WSP in this manner, so a comparison was warranted.
DropScope bins deposit diameters by size to produce histograms of deposit size by count. These stain diameters are used to extrapolate DV_0.1, DV_0.5 (VMD), DV_0.9 and NMD, which describe the population of droplets that produced the stains. DV_0.5 is the Volume Median Diameter, or the droplet diameter where half the volume is composed of finer droplets and the other half by coarser droplets. Number Median Diameter (NMD) is the droplet diameter where half the total droplets are finer, and half the total droplets are coarser.
Each brand of WSP will permit a certain degree of spread when a droplet of water contacts the surface. This spread factor is specific to the brand of paper. Further, the spread factor is not constant for all droplet sizes; Finer droplets will spread less than coarser droplets.
When processing data using DropScope, selecting the appropriate spread factor makes a significant difference to the output. For example, here are the same four SY papers processed using the Syngenta-specific spread factor as well as the spread factors intended for SpotOn and WSPaper.
The same four Syngenta papers were processed by DropScope using the Syngenta-specific spread factor as well as the SpotOn and WSPaper spread factors. The resulting VMD and NMD were very different.
Therefore, each brand of water sensitive paper was analyzed using its brand-specific spread factor (according to DropScope), to produce the following graph.
Three brands of WSP processed by DropScope using their specific spread factors. VMD differed by as much as 30%.
SY produced a VMD higher than that of WS, and both were higher than SO. There was less variability in the NMD, but this was expected given the high droplet count on the finer side of a hydraulic nozzle’s droplet size spectrum.
Conclusion
Water sensitive paper has immeasurable value in agricultural spraying. It is far more important to encourage its use than to quibble over brands. However, when these tools are used for more rigorous evaluations of spray coverage, brand-specific variability must be addressed.
The differences in how each brand responds to moisture (i.e. discolouration and deformation) may factor into which brand is most appropriate for a given situation. Further, there appear to be significant differences in how each brand resolves coverage. Once again, this may be irrelevant for those spray operators who occasionally use WSP to inform their spraying practices, but for consultants and researchers it is suggested that they use a single brand for an experiment, with papers produced in the same batch run. Learn more about methods for digitizing and analyzing WSP in this series of three articles.
Syngenta, Spraying Systems Co., SprayX, WSPaper and Innoquest are gratefully acknowledged for their contribution of materials and time informing this article.
July 7, 2021
Greenhouse Foggers
Greenhouse application equipment spans from the humble squirt bottle, to gas-powered foggers, to robots equipped with hydraulic vertical booms. The variety of spray equipment available reflects a variety of needs, just as a carpenter’s toolbox contains different tools designed to do different things. In order to get the most out of foggers and misters, it’s important to understand how they differ from “conventional” hydraulic spraying.
A greenhouse robotic vertical boom or “tree” sprayer
Mechanical and Chemical Spread
For many greenhouses, water is the carrier that dilutes and delivers the chemistry to the target. Water has a high surface tension and tends to bead on target surfaces. Dr. Heping Zhu (USDA, Ohio) created some amazing videos using controlled water droplets and both waxy and hairy leaves. In first video we see how water beads up on a waxy leaf, and as it evaporates, the area touching the leaf surface remains small. In the second, we see the droplet get hung up on a trichome (leaf hair) and evaporate while suspended above the leaf surface.
Neither situation is desirable since the goal of spraying is to maximize the level of contact between droplet and target. Contact can be increased via mechanical spread or chemical spread (see figure below).
The degree of chemical spread can be increased by adding adjuvants such as non-ionic surfactants to reduce surface tension. In the videos below we see the same controlled droplets with the same volume of liquid, but they now include a non-ionic surfactant. In the first video we see a greater degree of contact with the waxy target surface as the droplet spreads. In the second, the droplet does not get caught by the trichome, but splashes down onto the surface. Some product labels advise the inclusion of adjuvants and others are already formulated with them. In the case of surfactants, be aware of the potential for run-off and phytotoxicity.
Mechanical spread requires us to break a single, larger droplet into several smaller volumes to increase the degree of contact. This approach usually comes with a caveat about evaporation, but this is rarely a concern in a humid greenhouse. As for the risk of drift, once again, in greenhouses it is a different story than conventional spraying. Spray drift is desirable! Lateral air movement is very important to encourage plant canopy penetration and prevent droplets from merely settling on upward-facing plant surfaces. While some equipment generates its own air, the air currents in the greenhouse are often the primary means for suspended droplets to circulate throughout the space. In either case, air could be considered the carrier instead of water. Too little air flow, or gaps in circulation, will reduce coverage. Too much air flow (specifically, greenhouse air circulation) may cause plants to exhibit stunting.
Spray Quality (ISO)
Here’s how ISO/DIS (5681:2019 Equipment for crop protection — Vocabulary 3.2.1) defines the spray quality produced by misters and foggers:
- (3.2.1.13) MIST: “Spray with volume median diameter between 50 µm and 100 µm.”
- (3.2.1.14) FOG: “Aerosol spray with volume median diameter under 50 µm where the droplets are effectively suspended in air with little or no settling by gravity.”
These droplets do not behave like coarser droplets. For more information on droplet movement, survivability, and transfer efficiency, download Purdue Extension’s “Adjuvants and the Power of the Spray Droplet”.
Water sensitive paper has limited utility when diagnosing coverage from foggers. Sophisticated optical scanners may be able to detect deposits as small as 25 µm, but this is open to debate. Manufacturers do not support the use of papers when quantifying deposits less than 50 µm , and some draw the line at 100 µm.
In the following image, papers were used to diagnose coverage (from clean water) in a poinsettia greenhouse. The two papers on the right were located in the canopy and sprayed using a thermal pulse fogger and a hardware store style hand pump. The paper on the left was held directly in the path of the fogger while using the smallest nozzle provided with the unit. The spray enveloped the paper (and the person holding it). Close inspection showed tiny deposits, and the SnapCard app detected 4.5% coverage, but this greatly underestimates the actual deposition and does not account for the droplet count.
UV dyes are the preferred method for analyzing coverage from foggers.
Fogging and Misting Equipment
Greenhouse spray equipment can be classified by droplet size, but also by the spray volume they employ.
High Volume (HV)
These applications are performed at pressures ranging from 500 to 4,285 kPa (75 to 700 psi) employing flow rates of 3.9 to 5.7 L/min (1 to 1.5 US g/min). They use standard label rates to accomplish a dilute application by broadcasting droplets larger than 100 microns. The goal is to cover all surfaces without incurring run-off. Examples of HV application equipment include backpack sprayers, trailed sprayers and boom sprayers. Practice and self-calibration are necessary to achieve the desired results when using manual HV sprayers.
Targeted Low Volume (LV)
These applications are performed at high pressures around 20,685 kPa (3,000 psi) employing flow rates approaching 1 L/min (0.26 US g/min), covering 93 m² (1,000 ft²). They apply reduced rates over a given area and create droplets between 25 and 100 µm. These are concentrated sprays that do not result in wet foliage. LV applications are particularly good in high-humidity environments, when it is desirable to minimize the moisture on leaves. Examples of LV application equipment include aerosol cans.
Ultra-Low Volume (ULV)
These applications employ flow rates approaching 2 L/min (0.52 US g/min), covering 930 m² (10,000 ft²). They require concentrated solutions, but apply reduced rates per area using droplets less than 25 µm. ULV applications will not raise greenhouse humidity and are a good choice when days are short and nights are long. They are also an excellent way to apply disinfectants for complete space sanitation before starting a new crop. It is important to ensure vents are closed and fans are off during sanitation. Examples of ULV application equipment include total release cans, auto foggers and thermal pulse foggers.
PulsFOG hand-held ULV cold fogger
Thermal pulse foggers are unlike other ULV equipment and warrant special consideration. The design of the pulse fogger has remained virtually unchanged since the 1940’s. Smaller, 24 hp machines are used in smaller operations but range up to large 175 hp machines. Tank size ranges from 10 to 50 L, where 10 L should be enough to cover 4,645 m² (50,000 ft²) in about 10 minutes, depending on crop density. Their range is about 35 m (115 ft) from the point of release.
Thermal Pulse foggers do not create aerosol using air shear – they use combustion (80 to 100 explosions per second) to shatter spray into a fog and propel it via positive pressure. Heat is a by-product of the engine, making it an unsuitable method for applying biological products.
However, water-cooled foggers such as Dramm’s Bio Pulse Fogger reduce the exhaust temperature below 100 °C to make the application bio-rational. This has the added advantage of making droplet sizes more consistent and preventing spray from evaporating too quickly before it diffuses to the target.
Dramm Bio Pulse Fogger
Using a Fogger
Dramm recommends that operators use approximately 1 L of carrier in 5 L of spray mix, but a higher proportion of carrier would be required for more viscous products. Start with a full tank of clean, high grade gasoline and once the fogger has been started, run it continuously until the application is complete. Leave it running even when moving between Quonset huts (see below).
Know when to use a pulse fogger versus an auto fogger. Auto foggers are convenient because the operator can set them and leave. However, in the case of multiple huts, it is more efficient and timely to use a thermal pulse fogger.
Do not leave the manual fogger running unsupervised as an auto fogger: If they stay stationary, or aim directly at the canopy (as in hydraulic spraying), they could drench and potentially damage nearby plants.
When fogging, aim between the plants, such as the alleys and between hanging plants. This allows the fog to expand and permeate canopies for the best coverage. When spraying is done, be sure to release the pressure created in the spray tank to prevent accidental back flow into the gasoline tank.
And, because it’s convenient to include the math in this article, here are the formulae for calculating greenhouse volume to help you determine rates.
Care and Maintenance
HV, LV and ULV equipment requires model-specific cleaning and maintenance, according to manufacturer’s instructions. Even when sprayers are kept in prime condition, they are only as good as the operator’s understanding. When the wrong product is applied by the wrong machine using the wrong method, operators risk poor control, crop damage and increased potential for pesticide resistance. For more information, read the instructions that came with your sprayer, or contact the manufacturer.
Thanks to Louis Damm and Dr. Heping Zhu for their contributions to this article.
June 11, 2021