Chlorophyll is an important indicator for measuring crop photosynthesis, and monitoring leaf chlorophyll content (LCC) of potatoes during critical growth stages. UAV RGB and multispectral images were obtained during the potato tuber formation, tuber growth, and starch accumulation periods. Vegetation indices (VIs) were extracted from UAV multispectral images, and texture information (TIs) was extracted from RGB images by using Gabor filters. Then, the SVR-REF method was used for data dimensionality reduction to obtain the importance ranking of vegetation indices and texture features, and an iterative approach was used to add texture information to the best vegetation index model and observe the dynamic effect of each added texture information on the model. Finally, support vector machine (SVR) and K-nearest neighbor (KNN) algorithms were used for modeling. The results showed that the accuracy and stability of the two models were improved after adding texture features during the critical growth stages of potatoes, and the SVR model performed better than the KNN model. During the tuber formation period, the SVR modeling R2 was increased from 0.61 to 0.71, and RMSE was decreased from 0.20mg/g to 0.17mg/g, with an accuracy improvement of 14.2%. The validation R2 was increased from 0.58 to 0.66, and RMSE was decreased from 0.19mg/g to 0.17mg/g, with an accuracy improvement of 10.5%. During the tuber growth period, the SVR modeling R2 was increased from 0.59 to 0.67, and RMSE was decreased from 0.16mg/g to 0.14mg/g, with an accuracy improvement of 13.3%. The validation R2 was increased from 0.71 to 0.79, and RMSE was decreased from 0.15mg/g to 0.13mg/g, with an accuracy improvement of 13.3%. During the starch accumulation period, the SVR modeling R2 was increased from 0.62 to 0.69, and RMSE was decreased from 0.17mg/g to 0.14mg/g, with an accuracy improvement of 17.6%. The validation R2 was increased from 0.47 to 0.63, and RMSE was decreased from 0.17mg/g to 0.14mg/g, with an accuracy improvement of 17.6%. In addition, the number of vegetation indices involved in SVR modeling during the three periods were 19, 16, and 3, respectively, and the number of texture features were 4, 2, and 9, respectively. When vegetation indices were unable to respond adequately to chlorophyll content, more texture information was involved in modeling, and the model accuracy was improved significantly, further demonstrating the importance of texture features in chlorophyll content inversion in potatoes.