The required microwave power input and moisture content prediction in microwave- hot air dryer are challenging. In this study, two artificial neural networks (ANN) were investigated for modeling of moisture content of banana slices and required micro- wave power input. The experiments were done in five levels of microwave power density (MPD) (4, 5, 6, 7, and 8 W/g) with fixed mode and two levels (6 and 8 W/g) with variable mode at 40 C hot air temperature. The initial MPD, mode, and time were used as input variables in both ANN models. The ANN with 3–5–5–1 structure had best performance for MC modeling with 0.0148 of RMSE and 0.9998 of R for testing data. Also, the ANN with 3–3–1 structure could predict the microwave power with 14.013 of RMSE and 0.9929 of R for testing data. Real-time verification of developed models showed that developed ANN models could predict microwave power and MC with fixed MPD with good reliability and confidence for online drying control but did not reasonably satisfy for variable MPD. Also, the appearance quality of the banana slices was monitored by machine vision. The results showed that the percentage of banana charring at the fixed MPDs of 6, 7, and 8 is equal to 12%, 24%, and 29%, respectively, whereas no burns were observed at the other power density levels.
