Leaf segmentation from ToF data for robotized plant probing

Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-ofFlight (ToF) cameras achieve a good compromise between both demands. A new method is proposed to segment plant images into their composite surface patches by combining a hierarchical segmentation of the infrared intensity image, provided by the ToF camera, with quadratic surface fitting using ToF depth data. Leaf models are fitted to the segments and used to find candidate leaves for probing. The candidate leaves are ranked, and then the robot-mounted camera moves closer to selected leaves to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. Suitable leaves are then probed using a special cutting tool also mounted on the robot arm. The work is a proof-of-concept that dense infrared data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated cutting of leaf discs for experimentation purposes.