A robotic proximal sensing platform for in-field high-throughput crop phenotyping

dc.contributor.advisor Lie Tang
dc.contributor.author Tuel, Taylor
dc.contributor.department Department of Agricultural and Biosystems Engineering (ENG)
dc.date 2019-11-04T21:59:46.000
dc.date.accessioned 2020-06-30T03:19:29Z
dc.date.available 2020-06-30T03:19:29Z
dc.date.copyright Thu Aug 01 00:00:00 UTC 2019
dc.date.embargo 2020-07-18
dc.date.issued 2019-01-01
dc.description.abstract <p>A rapidly increasing world population and changing climate means plant scientists will need to be able to efficiently develop crop varieties to feed the world. Although the technology for sequencing the genomes of plants has advanced, the technology for characterizing the physical traits of plants has remained relatively static. This gap in technology has become known as the “Phenotyping Bottleneck.” To close this gap, researchers are working to develop robotic systems that can efficiently recognize various physical traits of plants. The goal of this research was to investigate how the design requirements for a vehicle that interacts with a biological system are translated into a working mechanical system. The design requirements include the ability to traverse and image crops in 30-inch wide space between crop rows. This thesis reports the concepts, design decisions, and manufacturing process around the construction of such as a phenotyping robot namely Phenobot 3.0, which stands for the 3rd generation of our phenotyping robot series. Phenobot 3.0 is optimized for phenotyping maize plants in the field but can be adapted for phenotyping other crops. Specifically, Phenobot 3.0 is designed to be narrow to fit between the rows but also tall for sensor placement so that it can gather data from the emergence to the full height of maize plants. To achieve the needed stability of the sensors (LiDAR, stereo cameras), the robot employs a self-leveling mast to cope with the uneven terrain while ensuring proper sensor to plant placement. Unlike many other field-based phenotyping robots, Phenobot 3.0 employs a 4-wheel-drive articulated drivetrain that has differentials on each pair of wheels to ensure maximum steering efficiency and prolonged operational time in the field. Phenobot 3.0 will be a member of PhenoNet, a network of five robots for maize plant phenotyping under different growing environments, a project funded by the National Science Foundation. The scale of this project implies that each design requirement must be carefully evaluated so that the manufacturing process can be easily scaled up to produce multiple units. The results from the preliminary tests of the Phenobot 3.0 prototype have demonstrated satisfactory functionalities and expected performance metrics.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/17587/
dc.identifier.articleid 8594
dc.identifier.contextkey 15681630
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/17587
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/31770
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/17587/Tuel_iastate_0097M_18286.pdf|||Fri Jan 14 21:25:43 UTC 2022
dc.subject.disciplines Engineering
dc.subject.keywords automation
dc.subject.keywords phenomics
dc.subject.keywords phenotyping
dc.subject.keywords phenotyping bottleneck
dc.subject.keywords robotics
dc.title A robotic proximal sensing platform for in-field high-throughput crop phenotyping
dc.type thesis
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
thesis.degree.discipline Agricultural and Biosystems Engineering
thesis.degree.level thesis
thesis.degree.name Master of Science
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