a proof of concept study for the parameters of corn grains using digital images and a multivariate regression model

In this method, a numerical matrix comprised of ten color scales (rgb, hsv, l, and rgb) as independent variables from digitalized images was used as a proof of concept for the prediction of the mass, apparent volume, and bulk density parameters of grains for quality control considering post-harvest purposes. the goal was to develop a high throughput multivariate regression model using partial least squares (pls) combined with the information from color images to assess the raw product. the data set of external samples was successfully evaluated with standard error of cross-validation (secv) values of 1.23 g (16.4–28.9), 2.03 cm3 (20.5–40.5), and 0.018 g cm−3 (0.68–0.85) for the mass, apparent volume, and bulk density, respectively.