Prediction of shrimp growth using an artificial neural network and regression models

Uncertainty and lack of information about the future make it difficult for shrimp farmers to develop and plan harvesting schedules. To do this effectively, farmers should be able to predict shrimp growth. A reliable prediction of growth and survival would also give farmers a better insight into future productivity and profitability. Linear and nonlinear regression models have been used to estimate growth of different types of animals. These models include theoretic guesses and hypotheses about the underlying laws that govern the system from which data are generated. Compared to such models, artificial neural networks (ANNs) make a few priori assumptions about the models and suited for predicting animal growth. This study evaluated the potential of an ANN as an alternative to regressions models for predicting shrimp growth. Empirical data were collected from 9 commercial shrimp farms in the Bushehr Province of Iran. The results showed that the ANN performed better compared to linear and nonlinear regression models for predicting the growth of farmed shrimp.