*In Food science & nutrition *

*p*< .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R

^{2}), root mean squared error (RMSE), and chi-squared error (χ

^{2}) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best-fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous-flow IR-assisted hybrid solar dryer.

*Bakhshipour Adel, Zareiforoush Hemad, Bagheri Iraj*

*2021-Jan*

**drying kinetics, infrared radiation, intelligent modeling, medicinal plant, solar energy**