Abstract: To develop a mathematical model for describing the yellowing characteristics of tobacco leaves and predicting the degree and uniformity of leaf yellowing in the curing barn during flue-curing, and to reveal the yellowing patterns under the synergistic regulation of maturity and temperature, this study systematically collected images of under-mature, mature, and over-mature tobacco leaves at 38 ℃, 40 ℃, and 42 ℃ and extracted the corresponding yellowing degree data. Three growth models, Logistic, Gompertz, and Von Bertalanffy, were used to fit the yellowing patterns of tobacco leaves. After selecting the optimal model, the Arrhenius equation was employed to correlate its parameters with temperature, thereby developing a kinetic model of flue-cured tobacco leaves yellowing during curing, which was subsequently validated and applied using the Internet of Things (IoT)-enabled cuing barn. The results showed that the yellowing rate of tobacco leaves increased initially and then decreased as maturity increased, while it consistently increased with rising temperature. Among the three growth models, the Logistic model performed best in fitting the yellowing degree among different treatment combinations, with R² ranging from 0.9971 to 0.9993 and RMSE from 0.0093 to 0.0182). The reaction activation energies for the yellowing process of under-mature, mature, and over-mature tobacco leaves were 89.38, 71.24, and 96.86 kJ/mol, respectively. The proposed kinetic model showed excellent performance in predicting yellowing degree during curing, with R² ranging from 0.9934 to 0.9946. Furthermore, model calculations indicated a significant improvement in the uniformity of leaf yellowing within the curing barn during the 12-36 h stage. The model developed in this study reveals the yellowing dynamics of tobacco leaves, and accurately predicts both the degree and uniformity of tobacco leaves yellowing in the curing barn, provides a theoretical basis for process regulation during the yellowing stage.