Artificial neural networks (ANP Ts) have the potential to automate routine
ageing of fish with the benefit of increased speed in processing, greater o
bjectivity and repeatability of estimates, and a mechanism for quantifying
uncertainty of age estimates. ANN models were tested as a means of objectiv
ely replicating the age estimates of an experienced human reader. Feed-forw
ard back-propagation ANNs, with three layers of neurons (input, hidden and
output), were trained to classify the age of previously aged samples of thr
ee temperate species. Three ANN structures, where the number of neurons in
the hidden layer was varied, were tested for each species. Inputs to each A
NN were pixel brightness values along transects across images of sectioned
otoliths. The ANN predicted age-class membership by the position of the neu
ron in the output layer with the highest value. After training, at least on
e of the three ANN structures correctly classified the age of fish from uns
een transects for two members of the Sparidae family Acanthopagrus butcheri
and Pagrus auratus at an accuracy level approaching that of an expert read
er. For a member of the Merlucciidae family, Macruronus novaezelandiae, how
ever, which is a species with more complex otolith structure, error rates w
ere high for all three ANN structures tested.