Modeling the final fruit yield of coriander (Coriandrum sativum L.) using multiple linear regression and artificial neural network models

Abstract

ABSTRACT The prediction of fruit yield in the next generation is one of the most important breeding objectives in agricultural research. For this purpose, different generations of coriander consisted of six quietly divergent parents, their 15 F1 hybrids and 15 F2 families were evaluated during the 2014–2017 growing seasons. The artificial neural network (ANN) models were constructed to predict the fruit yield using morphological and agronomic factors, and compare the performance of ANN models with multiple linear regression (MLR) models. According to the principal component analysis (PCA) and stepwise regression (SWR), four traits of days to flowering, thousand fruit weight, fertile umbel number per plant and branch number per plant were selected as input variables in both ANN and MLR models. A network with Levenberg–Marquart learning algorithm, SigmoidAxon transfer function, one hidden layer with four neurons and having 0.461 root-mean-square error (RMSE), 0.335 mean absolute error (MAE) and 0.938 determination coefficient (R2) selected as the final ANN model. The ANN model was a more accurate tool rather than MLR for predicting fruit yield in coriander. According to sensitivity analysis, days to flowering and thousand fruit weight traits were identified as the most effective characters in fruit yield.