Resumo:
Coffee growers who produce the robusta species (Conilon) have sought to increase productivity and drink quality by improving production techniques. Artificial vision systems can assist in increasing the efficiency of operations associated with crop management. This study aimed to obtain colorimetric characteristics of robusta coffee fruits at different stages of maturity and use them for classifying fruits from digital images. A digital camera with spectral resolution in the visible was used to acquire images from 60 samples of coffee fruits at the green, cherry, and over-ripe stages of maturity. Colorimetric variables were extracted from the RGB, HIS, and L*a*b* color models and correlated with the physicochemical attributes of the fruits. The principal componente analysis associated with the k-means technique was applied to the colorimetric variables that showed a significant correlation with the physical-chemical attributes. The colorimetric variables were reduced to a principal component, which presented na explanatory percentage of the variance of 82.33%. The clustering obtained by the application of the k-means technique showed the feasibility of using images for the automatic classification of robusta coffee fruits, with an overall accuracy of 100%.
