Swarming around Shellfish Larvae
Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan, F. Bendezu, Fionn Murtagh

TL;DR
This paper presents a computational pattern recognition system using shape features and swarm intelligence for automated identification and size analysis of scallop larvae, improving accuracy in shellfish aquaculture monitoring.
Contribution
It introduces an innovative combination of invariant shape features and an unsupervised swarm intelligence clustering method for larval identification.
Findings
Achieved 100% recognition rate in tests
Effective in handling various larval maturities
Automates identification process
Abstract
The collection of wild larvae seed as a source of raw material is a major sub industry of shellfish aquaculture. To predict when, where and in what quantities wild seed will be available, it is necessary to track the appearance and growth of planktonic larvae. One of the most difficult groups to identify, particularly at the species level are the Bivalvia. This difficulty arises from the fact that fundamentally all bivalve larvae have a similar shape and colour. Identification based on gross morphological appearance is limited by the time-consuming nature of the microscopic examination and by the limited availability of expertise in this field. Molecular and immunological methods are also being studied. We describe the application of computational pattern recognition methods to the automated identification and size analysis of scallop larvae. For identification, the shape features used…
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Insect and Arachnid Ecology and Behavior · Cephalopods and Marine Biology
