Click on “Download PDF” for the PDF version or on the title for the HTML version.


If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

3D Perception-Based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 61(3): 859-872. (doi: 10.13031/trans.12653) @2018
Authors:   Yin Bao, Dylan S. Shah, Lie Tang
Keywords:   3D perception, Agricultural robotics, Leaf probing, Motion planning, Plant phenotyping.

Abstract. Various instrumentation devices for plant physiology studies, such as spectrometers, chlorophyll fluorometers, and Raman spectroscopy sensors, require accurate placement of their sensor probes toward the leaf surface to meet specific requirements of probe-to-target distance and orientation. In this work, a Kinect V2 sensor, a high-precision 2D laser profilometer, and a six-axis robotic manipulator were used to automate the leaf probing task. The relatively wide field of view and high resolution of the Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. The location and size of each plant were estimated by k-means clustering where k was the user-defined number of plants. A real-time collision-free motion planning framework based on probabilistic roadmaps was adapted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from the top with the short-range profilometer to obtain high-precision 3D point cloud data. Potential leaf clusters were extracted by a 3D region growing segmentation scheme. Each leaf segment was further partitioned into small patches by a voxel cloud connectivity segmentation method. Only the patches with low root mean square errors of plane fitting were used to compute leaf probing poses of the robot. Experiments conducted inside a growth chamber mockup showed that the developed robotic leaf probing system achieved an average motion planning time of 0.4 s with an average end-effector travel distance of 1.0 m. To examine the probing accuracy, a square surface was scanned at different angles, and its centroid was probed perpendicularly. The average absolute probing errors of distance and angle were 1.5 mm and 0.84°, respectively. These results demonstrate the utility of the proposed robotic leaf probing system for automated non-contact deployment of spectroscopic sensor probes for indoor plant phenotyping under controlled environmental conditions.

(Download PDF)    (Export to EndNotes)