Seed yield prediction of sesame using artificial neural network

The prediction of seed yield is one of the most important breeding objectives in agricultural research. So, in this study, two methods namely artificial neural network (ANN) and multiple regression model (MLR) were employed to estimate the seed yield of sesame (SYS) from readily measurable plant characters (e.g., flowering time of 100% (days), the plant height (cm), the capsule number per plant, the 1000-seed weight (g) and the seed number per capsule). The ANN and MLR were tested using field data. Results showed that the ANN predicts the SYS accurately with a root-mean-square-error (RMSE) of 0.339 t/ha and a determination coefficient (R2) of 0.901. Also, it was found that the ANN model performed better than the MLR model with a RMSE of 0.346 t/ha, and R2 of 0.779. Finally, sensitivity analysis was conducted to determine the most and the least influential characters affecting SYS. It was found that the capsule number per plant and the flowering time of 100% had the most and least significant effects on SYS, respectively.