PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping-with an application in sorghum stem diameter estimation

dc.contributor.author Xiang, Lirong
dc.contributor.author Tang, Lie
dc.contributor.author Tang, Lie
dc.contributor.author Gai, Jingyao
dc.contributor.author Wang, Le
dc.contributor.department Agricultural and Biosystems Engineering
dc.contributor.department Human Computer Interaction
dc.contributor.department Plant Sciences Institute
dc.date 2020-07-16T19:11:27.000
dc.date.accessioned 2021-02-24T17:13:48Z
dc.date.available 2021-02-24T17:13:48Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2019-01-01
dc.date.issued 2020-01-01
dc.description.abstract <p>In recent years, three-dimensional (3D) sensing has gained a great interest in plant phenotyping because it can represent the 3D nature of plant architecture. Among all available 3D imaging technologies, stereo vision offers a viable solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping has been adversely affected by textureless regions and occlusions of plants, and variable outdoor lighting and wind conditions. In this research, a portable stereo imaging module namely PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo featured a self-contained embedded design, which made it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights was integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blurs. The stem diameter of sorghum plants is an important trait for stalk strength and biomass potential evaluation but has been identified as a challenging sensing task to automated in the field due to the complexity of the imaging object and the environment. To that connection, PhenoStereo was used to acquire a set of sorghum plant images and an automated point cloud data processing pipeline was also developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a Mask R-CNN deep learning network for detecting stalk contours and a Semi-Global Block Matching stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approaches. These results demonstrated that with proper customization stereo vision can be a highly desirable sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. With the proving results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad spectrum of plant phenotypes such as leaf angle and tassel shape of maize plants and seed pods and stem nodes of soybean plants.</p>
dc.description.comments <p>This proceeding is published as Xiang, Lirong, Lie Tang, Jingyao Gai, and Le Wang. "PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping-with an application in sorghum stem diameter estimation." Paper no. 2001190. 2020 ASABE Annual International Virtual Meeting. July 13-15, 2020. DOI: <a href="https://doi.org/10.13031/aim.202001190" target="_blank">10.13031/aim.202001190</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/abe_eng_conf/596/
dc.identifier.articleid 1601
dc.identifier.contextkey 18546504
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_conf/596
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/92929
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/abe_eng_conf/596/2020_TangLie_PhenoStereo.pdf|||Sat Jan 15 01:04:24 UTC 2022
dc.source.uri 10.13031/aim.202001190
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.disciplines Plant Sciences
dc.subject.keywords field-based high-throughput phenotyping
dc.subject.keywords point cloud
dc.subject.keywords stem diameter
dc.subject.keywords stereo vision
dc.title PhenoStereo: a high-throughput stereo vision system for field-based plant phenotyping-with an application in sorghum stem diameter estimation
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication e60e10a5-8712-462a-be4b-f486a3461aea
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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