Chopsuwe: excellent and informed questions and suggestions. If you have some time I will below answer your questions and respond to suggestions – thanks for bearing with me.
A. On your questions: “ (1) How are you imagining this sensor will work? (2) What quantity of plankton per unit volume of water are you expecting? (3)Is this going to operate at the water surface or need to be taken at various depths? (4) How long does it need to spend deployed? (5)How do you determine it's a plankton passing the sensor not some other flotsam?”
(1) I imagined the sensor to be the diameter that an oval object of around 0.5 mm and if that broke the beam then it would be one hit – but as you asked some interesting questions I realize that only perfect beam breaks would give the result I need. In that regard, I expect to validate and calibrate the data by comparing plankton collections and scaling the beam break data against the actual density of plankton.
(2) I anticipate concentrations of plankton from 4/liter (= approx. a quart) on up to 50+/liter and it is that count that I am trying to get. What is the concentration of plankton here and there over an area of sea surface of about 1 square kilometer where the rare whale I work on is feeding. Basically how much food is a right whale getting and how is it strategizing to get that food; because of our need to get answers to those questions we need a lot more spatial data and that is why I am wanting to deploy a particle counter in a drone.
(3) It would be great to allow the imagined sensor to work at depth but for now I’d expect to have it work in the upper 10 cm (5-6”).
(4) On a calm day I would want the drone to land and take off 10+ times through a period of 15 mins. and if we could get those data that would be spectacular, however longer would be even better and that will depend on battery life.
(5) In the area where whales feed the flotsam problem is right now not too bad, however the issue of other organisms triggering the sensors could confound the data. I imagine that the validation/calibration (#1 above) would solve some of these issues. The counts we would look for would have to be corrected against validation collections analyzed in our lab for flotsam, other organisms, and partial beam breaks.
B. “If it's a break beam sensor (1) how far does the light source need to be from the sensor to give a reasonable chance or a plankton passing through it? (2) If the beam is only 0.5mm diameter, what happens if the plankton misses the beam by a few mm? (3) Can you funnel an area of water through a narrow diameter pipe to improve the chances of the plankton passing through the beam?”
(1)I imagine that the sampling would happen in a “cell”, a unit through which water would be pumped. And perhaps the drone would land for enough time to allow as much as 2 liters of water to be sampled by the laser/sensor pair. Imagining the cell, I am thinking that the beam would pass through 50 cm of water but that would be up for discussion.
(2) The way I conceptualize this is that our actual plankton collections will correct the beam break data, this would be the validation from which we would calibrate the beam break data. So imagine if the beam break said 10 plankton organisms/L and the actual collection said 30/L, after a lot of collections we would calibrate by multiplying the beam breaks by 3. That approach would, one would hope, correct for misses.
(3) Yes, BUT enough water would have to be scanned to give relatively stable results. If the volume is so low that very few beam breaks are available for validation then the statistics related to the calibration will likely be unforgiving.
C. “All a break beam can tell you is how long the beam was broken for. (1) How would you differentiate between a large particle moving fast compared to a small particle moving slowly? Both would obstruct the beam for the same length of time.”
(1) Yes, true. But if the water through the cell is passing the sensor at a set and regular a velocity then the break time should have a relationship to the size of the particle……. except for the fact that the plankton is oblong and the organisms may be tumbling when in the flow (Yikes!). I’d have to see what the relationship looks like I guess.
D. “Maybe you could use a system like a photoelectric smoke detector. It's similar to a break beam but looks for sudden dimming of the sensor as smoke obstructs the light source. (1) How would you differentiate between plankton casting a shadow compared to murky water?”
(1) I believe that the murkiness of the water (turbidity) in the off shore areas where we are sampling is pretty low and not significant over potential beam distances of 50 cm. True the off shore water is murky over several to many meters but over small distances I am pretty sure that it would not be an issue. If collections were in very near shore areas or in rivers the murk would likely be a much bigger issue, but the off shore waters are much less turbid.
E. “Maybe you could use a wide beam of light shining on grid of small sensors.”
Excellent thought that might circumvent some of the issues of sample volume and particle density. In a grid I assume that each sensor would have to be paired with its own laser (I am assuming that laser would be the way to go??) and each would have to be sampled at as high speed as possible to yield the data stream combining the sensor results. If there were a grid that you mention then sensors could have some size discrimination or could be monitored individually by Arduino or RPi to give a usable result (though I can assure you that this more sophisticated sensor package is for me right now beyond both my wiring and programming skills!). This is a great idea because then there is some potential to “tune” results in post processing.
F. “If you're going to go that direction it would be easier to use something like a Raspberry Pi and camera and some sort of image recognition to count the plankton. But that's getting quite complicated, it would be much easier to just to record video for a period then have someone watch the video later and count the number of plankton.”
Again that does make sense to me and let me say, the reason I am going on and on here, taking up people’s time, is because a solution to the problem of measuring plankton density would, truly, be an immense value to the conservation of the very nearly extinct right whales that I work on. So…… I have thought of imaging (though again I am a class 1 novice in that too!). I agree, for me building shape recognition software would be a big hurdle. Given the criticality of getting data on feeding by right whales I’ve thought of using an imaging system and reviewing images by eye having volunteers review the resulting images. I have tried using a GoPro as a video recorder and grabbed and counted particles from frames illuminated by a strobe (every 4-6 frames). That approach seems to work but the images were fuzzy in the closeup configuration. And again I am not confident/don’t know much about photo work with either Arduino or RPi.
??So, with the peripherals now available could one or the other platform (Ard/RPi) be set up to photograph a gridded field in a cell of perhaps 25x25 cm (10x10”)with a depth of 2cm (=1.25 liters)and IMPORTANTLY would the depth of field of the camera be great enough and the width of field be close enough to 25x25 cm to yield visible and in focus particle images so that one could count all particles that look like plankton? To get images are there both lighting (flash?) peripherals and special RPi/Arduino cameras and lenses that I could try?
Enough! Thanks much for your thoughts, they clearly awakened many ideas, which, I know, is what this posting system is intended to do.
-Darksea13