Leaf potassium content (LKC) is an important indicator to characterize the potassium nutritional status of crops, and efficient and accurate acquisition of potato LKC can help precision agriculture fertilization management. The aim was to improve the accuracy of potato LKC estimation by combining vegetation indices (VIs) and vegetation cover (FVC) extracted from RGB images during the critical fertility period of potatoes. Firstly, VIs and FVC were extracted from the RGB images of potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then the correlation between VIs and FVC and potato LKC was analyzed for each fertility period separately. Finally, the correlation between VIs and FVC, and LKC was analyzed by using a support vector machine (SVM), least absolute shrinkage and selection operator regression (Lasso), and ridge regression used to construct the estimation model of potato LKC. The results showed that the accuracy of potato FVC extracted based on RGB images was high, and the first two fertility periods were higher than that of the third; the estimation of potato LKC using VIs was feasible, but the accuracy needed to be further improved; and the combination of VIs with FVC can improve the estimation accuracy of potato LKC. The research result can provide technical references for crop growth and potassium nutrient status monitoring.