Comparative analysis of RGB and multispectral UAV image data for leaf area index estimation of sweet potato

• In estimating LAI, the combination of image types affects the accuracy. • EVI2 outperforms other indices for LAI estimation in a mono-regression. • Two or three image types provide more accurate estimates than a single image type. • Increasing the number of image types further does not appear to be effective. This study aims to evaluate whether the utilization of a broader array of image types and the extraction of plant canopy using RGB and multispectral cameras mounted on unmanned aerial vehicles (UAVs) can improve leaf area index (LAI) estimation accuracy. The accuracy of LAI estimation for sweet potato was compared across different model types—mono-regression models based on a single image, multivariate regression models based on a single type of image, and multivariate regression models based on multiple image types—by employing 10 types of images based on color, morphological indices, vegetation indices (VIs) from RGB cameras, reflectance, VIs from multispectral cameras, and each of their canopy part image. For each regression model, we compared the estimation accuracy based on the whole image with that based on the canopy part image. For the mono-regression model, EVI2 from the whole image exhibited the lowest test root mean squared error (RMSE) of 0.403, which is attributed to EVI2’s capacity to mitigate the effects of spectral saturation. Contrary to the models with multiple image types that demonstrated improved accuracy, the multivariate regression models based on a single image type did not enhance estimation accuracy; this shows that the use of multiple image types improves the LAI estimation accuracy owing to the synergy of different spectral information from various image types. Plant canopy extraction did not enhance the estimation accuracy in mono-regression models or multivariate regression models based on a single image type; however, it improved the accuracy of multivariate regression models based on multiple image types, provided the image types were appropriately combined. The highest estimation accuracy was achieved by a partial least squares regression model based on VI (from the RGB camera) and reflectance (from the multispectral camera) of the canopy part (test R 2 = 0.887, test RMSE = 0.351), suggesting that this is an effective approach for LAI estimation in sweet potato (cultivar Beniharuka ). Overall, this study demonstrates that an optimal combination of image type and plant canopy extraction can enhance LAI estimation accuracy in UAV-based monitoring.