Abstract: Despite extensive research conducted in machine vision for harvesting robots, practical success in this field of agrobotics is still limited. This article presents a comprehensive review of classical and state-of-the-art machine vision solutions employed in such systems, with special emphasis on the visual cues and machine vision algorithms used. We discuss the advantages and limitations of each approach and we examine these capacities in light of the challenges ahead. We conclude with suggested directions from the general computer vision literature which could assist our research community meet these challenges and bring us closer to the goal of practical selective fruit harvesting robots. Keywords: agricultural computer vision; agrovision; agrobotics; fruit harvesting robots. Computer vision for fruit harvesting robots Reference to this paper should be made as follows: Kapach, K., Barnea, E., Mairon, R., Edan, Y. and Ben-Shahar, O. (2012) Her research includes robotic and sensory performance analysis; systems engineering of robotic systems; robotic control of dynamic tasks; sensor fusion and selection algorithms; robotic gripper analysis and design; and multi-robot and human-robot control and collaboration models. In addition, she has made major contributions in the introduction and application of intelligent automation and robotic systems to the field of agriculture with several patents. Ohad Ben-Shahar received his BSc and MSc from Technion and his PhD from Yale University, all in Computer Science. He is an Associate Professor in the Department of Computer Science at Ben-Gurion University and the Director of the Interdisciplinary Computational Vision Lab. His main area of research is in computational vision and image analysis, where he is focusing primarily on issues related to the differential geometrical foundations of perceptual organisation and early vision. His work is explicitly multidisciplinary and his computational research is often endowed by investigations into human perception, visual psychophysics, and computational neuroscience of biological vision.
In REF the authors compile a list of the available state of the art methods for harvesting with the aid of robots.
Computer vision for fruit harvesting robots – state of the art and challenges ahead
IJCVR
IJCVR
Computer Science