The leaf area index (LAI) is an important indicator for characterizing crop growth, so an efficient and accurate estimation of crop LAI can guide field production management. Spectral features can provide information about the reflected and absorbed wavelengths of crops, while texture features can provide information about the gray-scale attributes and spatial location relationships of crops. Previous studies have shown some limitations in estimating crop LAI using only spectral features, and at high LAI levels, the “saturation phenomenon” occurs, resulting in an underestimation of LAI. To fully explore the information of multispectral images from UAVs, texture information of multiple bands was combined to obtain multiband combined texture and to explore whether the fusion of spectral features with multiband combined texture can improve the accuracy of LAI estimation. Firstly, we obtained multispectral data and ground-truthed LAI data of three key fertility stages of potato; then we extracted the texture features of each fertility stage using the gray-level co-occurrence matrix (GLCM) and combined the texture features of multiple bands; then we analyzed the correlation between the vegetation index, the texture features, and the multi-band combination of textures and LAI, and synthesized the correlations and correlations with LAI, and investigated whether the fusion of spectral information and multiband combination of textures could improve the accuracy of LAI estimation. Then, we analyzed the correlation between vegetation index, texture features, and multiband combined texture and LAI and combined the correlation and variance expansion factors to select the preferred vegetation index; finally, we integrated the multiband combined texture and used partial least squares regression (PLSR), ridge regression (RR) and K-nearest neighbors regression (KNR) with parameter tuning to determine the correlation between the vegetation index and LAI, and then used KNR to estimate the correlation between the vegetation index and LAI. KNR will estimate potato LAI at each fertility stage and compare it with the model using only the vegetation index to verify the feasibility of inverting LAI using a multiband combined texture. The results showed that: (1) the correlation between single-band texture, two-band combined texture and three-band combined texture and LAI increased sequentially; (2) the preferred multiband combined texture at each fertility stage of potato showed highly significant correlation with LAI, with correlation coefficients ranging from 0.79 to 0.83; and (3) compared with the model using only the vegetation index, the addition of the multiband combined texture could significantly increase the model’s accuracy and stability. The KNR model had the highest accuracy in estimating potato LAI during the tuber formation period, with a modeling R² of 0.83，an RMSE of 0.23 m²·m^-2, and a validation R² of 0.75 and an RMSE of 0.25 m²·m^-2; the PLSR model had the highest accuracy during the tuber growth period, with a modeling R² of 0.73 and an RMSE of 0.26 m²·m^-2, and a validation R² of 0.87 and an RMSE of 0.20 m²·m^-2; and the PLSR model had the highest accuracy during the starch accumulation period, with a modeling R² of 0.73 and an RMSE of 0.26 m²·m^-2; and the PLSR model had the highest accuracy during the starch accumulation period. The PLSR model had the highest estimation accuracy, with modeling R² of 0.73 and RMSE of 0.31 m²·m^-2, and validation R² of 0.84 and RMSE of 0.25 m²·m^-2. This method can provide a reference for the UAV multispectral combination of texture features to estimate potato LAI.