Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision

dc.contributor.author Tang, Lie
dc.contributor.author Bao, Yin
dc.contributor.author Tang, Lie
dc.contributor.author Salas-Fernandez, Maria
dc.contributor.author Schnable, Patrick
dc.contributor.author Salas-Fernandez, Maria
dc.contributor.department Agronomy
dc.contributor.department Agricultural and Biosystems Engineering
dc.contributor.department Human Computer Interaction
dc.contributor.department Plant Sciences Institute
dc.date 2018-05-30T21:23:33.000
dc.date.accessioned 2020-06-29T22:35:24Z
dc.date.available 2020-06-29T22:35:24Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2018-05-30
dc.date.issued 2016-01-01
dc.description.abstract <p>Yield component traits such as plant height and stem diameter are dominant phenotypic data for biomass sorghum yield prediction. Extraction of these traits by machine vision during the growing season significantly reduces labor and time cost for large breeding programs. An automated 3D point cloud processing pipeline was developed to quantify different phenotypic variations in plant architecture of infield biomass sorghum. The input point cloud was generated by three side-view stereo camera heads placed vertically to capture extremely high plants. The features were extracted on a row plot basis instead of individual due to severe occlusion caused by densely populated leaves. Available features include plant height, plant width, vegetation volume index, and vegetation area index. Our strategy was to slice the point cloud along row direction into several equal volume slices and sum up the feature values with weights based on the point population and distribution in each volume slice. Therefore, the results were robust against empty space and abnormal individuals in the row plot. In addition, a semi-automated user interface was developed for users to measure stem diameters from the stereo images according to their specific sampling strategies. Users only need to zoom in on a stem segment and pick four corners of the rectangular segment. Metric measurement is then computed automatically based on image patch stereo matching using normalized cross correlation. The extracted stem diameters were compared to manual measurements in the field and a high correlation was obtained. The extracted features revealed great potential for automated field-based high-throughput phenotyping for plant architecture.</p>
dc.description.comments <p>This proceeding is published as Bao, Yin, Lie Tang, Patrick S. Schnable, and Maria G. Salas Fernandez. "Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision." ASABE Annual International Meeting, Orlando, FL, July 17-20, 2016. Paper No. 162462338. DOI: <a href="http://dx.doi.org/10.13031/aim.20162462338" target="_blank">10.13031/aim.20162462338</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/564/
dc.identifier.articleid 1564
dc.identifier.contextkey 12220323
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/564
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/609
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/564/2016_TangL_InfieldBiomass.pdf|||Sat Jan 15 00:57:43 UTC 2022
dc.source.uri 10.13031/aim.20162462338
dc.subject.disciplines Agriculture
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords biomass sorghum
dc.subject.keywords yield component traits
dc.subject.keywords field phenotyping
dc.subject.keywords stereo vision
dc.subject.keywords 3D point cloud processing
dc.title Infield Biomass Sorghum Yield Component Traits Extraction Pipeline Using Stereo Vision
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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