Otolith Database Analysis For Fish Age Estimation Using Neural Networks Methods

Abstract

Otoliths are calcified structures in the inner ear of fish. The otolith shape changes during a fish's lifetime are particular to individual species. Then, otolith shape can be used to differentiate between species and between fish of the same species. Fishery research has used the growth patterns (i.e. rings) found in these calcified structures to estimate the age of individual fish. However, many factors, such as seasonal variations, temperature, habitat and food, may influence otolith growth. Then, the manual classification of otoliths remains a difficult task, and even experienced examiners can give inaccurate age estimation. We propose to use statistical learning techniques (artificial neural networks) to improve and automate the process. ANN classification methods are evaluated and used with some real otolith databases, giving significant results