Plant potassium content (PKC) of potato plants is an important indicator for monitoring potato nutrition status. Obtaining PKC quickly and accurately has guiding significance for field fertilization and production management. RGB images of potato plants during the tuber formation period, tuber growth period, and starch accumulation period were obtained by using an unmanned aerial vehicle (UAV) remote sensing platform equipped with an RGB sensor, and PKC was measured. Firstly, the average spectral and texture features of each plot were extracted from the RGB images of each growth period. Then vegetation indices and texture indices (NDTI, RTI, and DTI) were constructed based on the spectral and texture features of the canopy, and their correlations with the measured PKC were analyzed. Finally, multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural networks (ANN) were used to construct models for estimating potato PKC. The results showed that the correlations between NDTI, RTI, DTI and PKC were higher than those of single texture features during each growth period. Combining vegetation and texture indices can improve the reliability and stability of the model. MLR and PLSR models were superior to ANN. The research result can provide scientific references for monitoring PKC in potato plants.