  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 are binary
invariant moments; that is, the features are invariant to shift (position
within the image), scale (induced either by growth or differential image
magnification) and rotation. Images of a sample of scallop and non-scallop
larvae covering a range of maturities have been analysed. In order to overcome
the automatic identification, as well as to allow the system to receive new
unknown samples at any moment, a self-organized and unsupervised ant-like
clustering algorithm based on Swarm Intelligence is proposed, followed by
simple k-NNR nearest neighbour classification on the final map. Results achieve
a full recognition rate of 100% under several situations (k =1 or 3).
