1. In-Situ Plankton Imaging
(858)
Charles Cousin, M.S. eng.
President
Bellamare, LLC

2. What is Plankton?
Much of the living matter in the ocean is plankton - small animals, plants, and microbes that drift passively with currents.
Among them are permanent members of the plankton, called holoplankton (krill, copepods, salps, etc.), and temporary members (such as most larvae), which are called meroplankton.
An example of Holoplankton: Copepods, a critical link in the food chain.
An example of Meroplankton is the Ichtyoplankton category. They are the eggs and larvae of fish found mainly in the upper 200 meters of the water column.
To date, we manufacture 200m rated ISIIS-1 and ISIIS-2 ROTVs
ISIIS means “In-Situ Ichthyoplankton Imaging System”

3. Plankton?! Really… Why do we study Plankton?
Plankton is the bottom of the ocean’s Food Chain
No plankton, no fish, no whales….
It is also a very important part of the Carbon Cycle.
Climate Change: since plankton is not harvested or exploited by humans, adjustments in distribution and abundance can be attributed to changing environmental factors.
Fish Stocks: the abundance of eggs and larvae of several species has been demonstrated to be a good indicator of population abundance of adults.
Pollution: for species that aren’t captured by a fishery, monitoring their population trends by monitoring their eggs or larvae can provide an indication of a healthy or stressed ecosystem.

4. Traditional Techniques
Mostly Net Fishing!
Hard work & lots of microscope time…
2 days at sea = 1 man-year of microscope work!

5. Imaging is a modern solution
Oceanography Paradigm
Physical & Chemical oceanography rely mainly on High Speed Digital Output instruments while Biological oceanography relies on net sampling…
Imaging Techniques provide great data for great monitoring
Locate precise position and time of each organism
Inform about spatial and vertical distribution of critters (fine-scale distributions of plankton from centimeter- to basin- wide volumes)
Environmental data of the organisms’ surroundings are sampled in sync.
Imaging does not destroy organisms - easier to recognize!
The sky is the limit…
If high data analysis of collected images is feasible, we can increase sampling frequency which leads to better monitoring and leads to a greater capacity for improved scientific inquiries.

6. Why has it not been done? ISIIS Imaging Systems BUT the challenge is:
Well, it has been done!
BUT the challenge is:
To provide an instrument able to sample large amounts of water AT ONCE to adequately quantify a broader range of plankton species.
Solution
ISIIS Imaging Systems

7. ISIIS Optical System
Well Controlled Optical System: Shadowgraph BIG, REALLY BIG Depth of Field & High Resolution
Line Scan Camera (2048 pixel line scanning at 35Khz) Continuous imaging with 70 micron resolution 80 MB/sec imaging data transfer rate
Scanning Rate: Approx. 162 Liters/second since we tow at 5 knots. Volume sampled is equivalent to a 1m x 1m plankton net opening
Water Flow
The Cowen Laboratory
Light
Imaging area
Water Flow
Line Scan Camera

9. Video San Diego: NOAA’s Bell. M Shimada leg
Images taken from 20m to 180m, diving down
Focus on jelly fish larvae

10. Payload is an under-carriage
ISIIS-2 ROTV SYSTEM
A novel Remotely Operated Towed Vehicle, designed to:
Carry the Optical System payload in undisturbed waters
Navigate on Auto-Pilot (programmed profiles, auto-depth, altitude-cruise)
Off the Side Towing
Payload is an under-carriage

11. ISIIS-2 ROTV SYSTEM

12. ISIIS-1 ROTV SYTEM Custom Order to be delivered in 5 months
Half the Size
Same imaging capabilities than ISIIS-2
Passive Tow

13. What we want to do… Short Term
Small towed sled (small boat) Coastal constructions impact on the environment.
Mooring Buoys Solution Long term monitoring
Long Term
ROV & AUV Skid

14. Automated Image Analysis?
A demo within the end of the year? Yes, this is THE goal
We already know how to segment ROIs & are making great steps towards Recognition and Classification.
The Approach
We are developing features set based on: Solidity computation, hierarchy of image moments, 2D-contour description, overall feature selection and its potential hierarchy, scaling (feature weights participating in distance computation)
Our Strength: The ability to use Support-Vector-Machines clustering in a very non linear feature space thanks to the use of a very novel hardware approach.

15. Thank You!