{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T05:33:21Z","timestamp":1768887201050,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,12]],"date-time":"2017-09-12T00:00:00Z","timestamp":1505174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Plant Science Institute (PSI) Innovative Grant, Iowa State University"},{"name":"National Science Foundation, Major Research Instrumentation grant","award":["1428148"],"award-info":[{"award-number":["1428148"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crop breeding plays an important role in modern agriculture, improving plant performance, and increasing yield. Identifying the genes that are responsible for beneficial traits greatly facilitates plant breeding efforts for increasing crop production. However, associating genes and their functions with agronomic traits requires researchers to observe, measure, record, and analyze phenotypes of large numbers of plants, a repetitive and error-prone job if performed manually. An automated seedling phenotyping system aimed at replacing manual measurement, reducing sampling time, and increasing the allowable work time is thus highly valuable. Toward this goal, we developed an automated corn seedling phenotyping platform based on a time-of-flight of light (ToF) camera and an industrial robot arm. A ToF camera is mounted on the end effector of the robot arm. The arm positions the ToF camera at different viewpoints for acquiring 3D point cloud data. A camera-to-arm transformation matrix was calculated using a hand-eye calibration procedure and applied to transfer different viewpoints into an arm-based coordinate frame. Point cloud data filters were developed to remove the noise in the background and in the merged seedling point clouds. A 3D-to-2D projection and an x-axis pixel density distribution method were used to segment the stem and leaves. Finally, separated leaves were fitted with 3D curves for morphological traits characterization. This platform was tested on a sample of 60 corn plants at their early growth stages with between two to five leaves. The error ratios of the stem height and leave length measurements are 13.7% and 13.1%, respectively, demonstrating the feasibility of this robotic system for automated corn seedling phenotyping.<\/jats:p>","DOI":"10.3390\/s17092082","type":"journal-article","created":{"date-parts":[[2017,9,12]],"date-time":"2017-09-12T10:40:04Z","timestamp":1505212804000},"page":"2082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Robotic Platform for Corn Seedling Morphological Traits Characterization"],"prefix":"10.3390","volume":"17","author":[{"given":"Hang","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lie","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, 2346 Elings Hall, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven","family":"Whitham","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s00122-013-2066-0","article-title":"Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype\u2013phenotype relationships and its relevance to crop improvement","volume":"126","author":"Cobb","year":"2013","journal-title":"Theor. Appl. Genet."},{"key":"ref_2","unstructured":"(2017, September 12). Phenomics: Genotype to Phenotype, Available online: https:\/\/www.nsf.gov\/bio\/pubs\/reports\/phenomics_workshop_report.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1145\/1073204.1073253","article-title":"Floral diagrams and inflorescences: Interactive flower modeling using botanical structural constraints","volume":"24","author":"Ijiri","year":"2005","journal-title":"ACM Trans. Graph. TOG"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1093\/aob\/mci136","article-title":"Rice morphogenesis and plant architecture: Measurement, specification and the reconstruction of structural development by 3D architectural modelling","volume":"95","author":"Watanabe","year":"2005","journal-title":"Ann. Bot."},{"key":"ref_5","unstructured":"Klodt, M., and Cremers, D. (2014). High-resolution plant shape measurements from multi-view stereo reconstruction. European Conference on Computer Vision, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1104\/pp.114.248971","article-title":"Automated recovery of three-dimensional models of plant shoots from multiple color images","volume":"166","author":"Pound","year":"2014","journal-title":"Plant Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kumar, P., Connor, J., and Mikiavcic, S. (2014, January 10\u201312). High-throughput 3D reconstruction of plant shoots for phenotyping. Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2014.7064306"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ward, B., Bastian, J., van de Hengel, A., Pooley, D., Bari, R., Berger, B., and Tester, M. (arXiv, 2015). A model-based approach to recovering the structure of a plant from images, arXiv.","DOI":"10.1007\/978-3-319-16220-1_16"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.robot.2011.02.011","article-title":"Plant detection and mapping for agricultural robots using a 3D LIDAR sensor","volume":"59","author":"Weiss","year":"2011","journal-title":"Robot. Auton. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ecolmodel.2006.07.028","article-title":"A method to extract morphological traits of plant organs from 3D point clouds as a database for an architectural plant model","volume":"200","author":"Dornbusch","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.agrformet.2010.01.003","article-title":"Three-dimensional digital model of a maize plant","volume":"150","author":"Krajewski","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aleny\u00e0, G., Dellen, B., and Torras, C. (2011, January 9\u201313). 3D modelling of leaves from color and ToF data for robotized plant measuring. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980092"},{"key":"ref_13","first-page":"937","article-title":"Leaf segmentation, classification, and three-dimensional recovery from a few images with close viewpoints","volume":"50","author":"Teng","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_14","first-page":"12","article-title":"Usability study of 3D time-of-flight cameras for automatic plant phenotyping","volume":"69","author":"Klose","year":"2009","journal-title":"Bornimer Agrartech. Ber."},{"key":"ref_15","unstructured":"Kahn, S., Haumann, D., and Willert, V. (2014, January 5\u20138). Hand-eye calibration with a depth camera: 2D or 3D?. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2014.09.005","article-title":"Automatic morphological trait characterization for corn plants via 3D holographic reconstruction","volume":"109","author":"Chaivivatrakul","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","unstructured":"Li, J. (2014). 3D Machine Vision System for Robotic Weeding and Plant Phenotyping. [Ph.D. Thesis, Iowa State Universtiy]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point cloud library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2082\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:41Z","timestamp":1760208281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/2082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,12]]},"references-count":18,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["s17092082"],"URL":"https:\/\/doi.org\/10.3390\/s17092082","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,12]]}}}