Age Estimation of Fish Using a Probabilistic Neural Network

Abstract

Age composition data provide fundamental insights into fish biology and stock productivity and allow the estimation of the basic parameters for describing growth, mortality rates and recruitment. Much time and money is spent on the collection and preparation of samples, and skilled technicians labour for many hours at microscopes, counting increments in the prepared structures. It is estimated that over 1 million fish were aged worldwide in 1999, mostly using scales and otoliths (Campana and Thorrold 2001). However, the process is somewhat subjective and there is much interest in automating the process and making estimates more reliable. To date none of the tested methods have been successful. A pilot study by Robertson and Morison (1999) first suggested that neural networks may provide the way forward for this previously intractable problem. In this paper we firstly give a brief account of traditional approaches to age estimation. We then describe the previous attempts to develop automatic or computer-aided methods and the problems they have encountered. Finally we describe the results of a recent application of a probabilistic neural network to the process of age estimation in fish and discuss the strengths of this novel approach.