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Not AvailableAim : Methodology : Results : Interpretation : Approaches to modelling pest populations range from simple empirical models to advanced soft computing techniques that have advantages as well as limitations.A comparative analyses of modelling approaches result in selection of betterpest forecast model with a higher prediction accuracy. Artificial neural network (ANN) techniques , multi-layer perceptron neural network (MLP-NN) and polynomial neural networks (PNN) were used along with the multiple and polynomial regressions to predict the moth population of tobacco caterpillar (Fabricius) in groundnut cropping system. pheromone trap catch and weather data of twenty five years (1990-2014) for season (26 to 44 standard meteorological weeks (SMW)) was used for predictive modelling. The weekly male moth catches of (numbers/trap/week) during maximum severity period (34 SMW) was modelled using weather variables maximum and minimum temperature (°C), rainfall (mm), morning and evening relative humidity (%) lagged by two weeks. The performance of the models was evaluated using coefficient of determination (R ), root mean square error (RMSE) and mean absolute percentage error (MAPE) estimates. The study clearly demonstrated the superiority of MLP-NN (R :0.89) over all other models for predicting the peak severity of . Sensitivity analysis of MLP-NN model indicated that the maximum temperature lagged by two weeks and evening relative humidity of the previous week was two most important factors influencing the peak population of . Validation also demonstrated the effectiveness of MLP-NN followed by PNN in dealing with non-linear relation between population and weather variables. All model equations developed in the present study can be used to predict peak (34 SMW) in conjunction with weather of 32 and 33 SMW during season, and in issuing need based advisories for its effective management on groundnut.Not Availabl