Appearance quality is an important indicator in assessing the quality of dried chrysanthemums. To achieve rapid and non-destructive detection of the appearance quality of chrysanthemums during drying， this study applied computer vision technology in the infrared-assisted hot air drying of chrysanthemums and developed a Python-based image processing algorithm to acquire information on the changes in the shrinkage and color of petals and stamens of chrysanthemums at different temperatures （35 ℃， 50 ℃， and 65 ℃）. These parameters serve as evaluation indices for the appearance quality and facilitat precise control of the drying process. The kinetic analysis showed that the drying of chrysanthemums had a consistently decreasing drying rate. High drying temperatures significantly reduced drying time and increased drying rates （p<0.05）. Evaluation of the fit of mathematical models for thin-layer drying， including the Henderson and Pabis model， the Page model， and the Lewis model， showed that these models were in better agreement with the experimental data and thus more accurately described the drying process of chrysanthemums. Furthermore， changes in shrinkage rate and lightness （L*）， red/green （a*）， and yellow/blue （b*） values during drying showed that the morphological and color of chrysanthemums depended on drying temperature and time. Lower temperatures and shorter drying times were favorable for maintaining the appearance quality of the chrysanthemums. Linear regression analysis using zero-order， first-order， and first-order fractional models showed that the first-order fractional model provided more accurate predictions of shrinkage and color changes during the drying process of chrysanthemums.