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

dc.contributor.author Bao, Yin
dc.contributor.author Tang, Lie
dc.contributor.author Shah, Dylan
dc.contributor.author Tang, Lie
dc.contributor.department Agricultural and Biosystems Engineering
dc.contributor.department Human Computer Interaction
dc.contributor.department Plant Sciences Institute
dc.date 2018-04-06T00:24:39.000
dc.date.accessioned 2020-06-29T22:43:32Z
dc.date.available 2020-06-29T22:43:32Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-01-01
dc.description.abstract <p>Various instrumentation devices for plant physiology study such as spectrometer, chlorophyll fluorimeter, and Raman spectroscopy sensor 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 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 mock-up showed that the developed robotic leaf probing system achieved an average motion planning time of 0.4 seconds with an average end-effector travel distance of 1.0 meter. 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 degrees, 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.</p>
dc.description.comments <p>This is a manuscript of the article Bao, Yin, Dylan S. Shah, and Lie Tang. "3D Perception-based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping." <em>Transactions of the ASABE (in press) </em>(2018). DOI: <a href="http://dx.doi.org/10.13031/trans.12653" target="_blank">10.13031/trans.12653</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_pubs/899/
dc.identifier.articleid 2182
dc.identifier.contextkey 11911259
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/899
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/1711
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_pubs/899/2018_Tang_3DPerception.pdf|||Sat Jan 15 02:19:46 UTC 2022
dc.source.uri 10.13031/trans.12653
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Ergonomics
dc.subject.disciplines Operational Research
dc.subject.keywords Plant phenotyping
dc.subject.keywords 3D perception
dc.subject.keywords Agricultural robotics
dc.subject.keywords Leaf probing
dc.subject.keywords Motion planning
dc.title 3D Perception-based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication e60e10a5-8712-462a-be4b-f486a3461aea
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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