Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models

Abstract

Ajowan is a medicinal plant with useful pharmaceutical compounds in its seeds. Seed yield improvement in ajowan through a better understanding of the relationship between seed yield and its components is one of the most important goals of any breeding program. In the present study, artificial neural network (ANN) along with multiple regression model (MLR) were applied to predict the seed yield of ajowan through seed yield components. According to the simple correlation analysis, four characters (number of secondary branches, shoot dry weight, number of umbellets in an inflorescence, and biological yield) were selected as input variables in both artificial neural network and multiple linear regressions models. The network with SigmoidAxon transfer function, Levenberg-Marquart learning algorithm, one hidden layer with four neurons, 1000 training epochs and with a root mean square error (RMSE) of 0.147, a mean absolute error (MAE) of 0.127 and a determination coefficient (R2) of 0.932 was selected as the final ANN model. The performance of ANN was better than MLR with a RMSE of 0.210 and a r2 of 0.792. Biological yield and shoot dry weight were the most important yield components traits that affect the seed yield of ajowan and assigned as selection criteria using both ANN and MLR models.