Discriminating Fruit for Robotic Harvest Using Color in Natural Outdoor Scenes

Abstract

ABSTRACT This research investigated the use of chrominance and intensity information from natural outdoor scenes as a means of guidance for a robotic manipulator in the harvest of fruit. A classification model was developed which could discriminate oranges from the natural background of an orange grove using only color information in a digital color image. A Bayesian classifier correctly classified over 75% of the fruit pixels in the natural scenes analyzed. The decision model was simple enough that a real-time search and centroid calculation technique could be implemented to provide guidance information for the robotic manipulator at the 60 Hz video frame rate.