Visual Characterization of Male and Female Greenshell™ Mussels (<i>Perna canaliculus</i>) from New Zealand Using Image-Based Shape and Color Analysis
2025
Murat O. Balaban | Graham C. Fletcher | Meng Zhou
Machine vision/image analysis is used in the sorting and handling of many aquatic species. Pictures of 474 New Zealand Greenshell™ (<i>Perna canaliculus</i>, Gmelin, 1791) whole unopened mussels (215 females and 259 males) from the top and from the side were analyzed to evaluate if visual attributes (size, shape, and color) can be used to differentiate gender. Size (length, width, height, and view area), color, and shape (by elliptic Fourier analysis and by ray length-ray angle analysis) were analyzed and differences by gender tested. Application of Artificial Neural Networks (ANN), Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Random Forest (RF) to the shape parameters failed to reliably predict gender. Comprehensive morphometric and color characterization of males and females, as well as shape parameters, are presented as a reference for future image-based research. The parasitic crustacean pea crab can change the shape of mussel shells, and elliptic Fourier analysis can quantify this difference.
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