Effect of lighting conditions on grape quality control by artificial vision

Abstract

In order to develop a new device for automatic quality control of grapes stored in crates just before pressing, it is necessary to specify many parameters. Among these, lighting is particularly important, both for the recognition methods and for the control system physical design and cost. This study introduces a database of images of grapes in crates, created specifically for the study, and investigates the possibility of recognizing healthy grapes from other visible elements (diseases, leaves. . . ) with four different lighting conditions and two classifiers (SVM and CNN). The experimental results show the feasibility of the system and provide objective and quantified elements to guide its design.