Segmentation of Apple Fruit from Video via Background Modeling

A method for locating apples was developed to process real-time video image sequences captured with an over-the-row harvester. The concepts of background modeling in RGB color were used, which is a novel approach to the apple segmentation problem. In background modeling, the distributions of background colors are approximated from real data. The algorithm developed for this task, Global Mixture of Gaussians (GMOG), is based on the principles of Mixture of Gaussians (MOG), which is used for motion-detection applications. The algorithm correctly identified ~85-96% of both red and yellow apples and performed at ~14-16 frames per second. This is the first work to our knowledge that uses video sequences to detect apple fruit. The potential advantages of using video include allowing harvesting on-the-go, detecting occluded fruit via camera movement to the occluded areas, using visual servoing of robotic grippers to grasp fruit, and achieving a faster harvest time.