Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping

dc.contributor.author Singh, Arti
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Jones, Sarah
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Sarkar, Soumik
dc.contributor.author Mueller, Daren
dc.contributor.author Sandhu, Kulbir
dc.contributor.author Nagasubramanian, Koushik
dc.contributor.department Mechanical Engineering
dc.contributor.department Plant Pathology and Microbiology
dc.contributor.department Electrical and Computer Engineering
dc.contributor.department Agronomy
dc.contributor.department Plant Pathology and Microbiology
dc.date 2020-09-02T23:57:12.000
dc.date.accessioned 2021-02-26T03:15:45Z
dc.date.available 2021-02-26T03:15:45Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-08-20
dc.description.abstract <p>Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.</p>
dc.description.comments <p>This article is published as Singh, Arti, Sarah Jones, Baskar Ganapathysubramanian, Soumik Sarkar, Daren Mueller, Kulbir Sandhu, and Koushik Nagasubramanian. "Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping." <em>Trends in Plant Science</em> (2020). DOI: <a href="https://doi.org/10.1016/j.tplants.2020.07.010" target="_blank">10.1016/j.tplants.2020.07.010</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/me_pubs/434/
dc.identifier.articleid 1436
dc.identifier.contextkey 19226044
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath me_pubs/434
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/96671
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/me_pubs/434/2020_GanapathysubramanianBaskar_ChallengesOpportunities.pdf|||Sat Jan 15 00:16:04 UTC 2022
dc.source.uri 10.1016/j.tplants.2020.07.010
dc.subject.disciplines Agronomy and Crop Sciences
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.disciplines Mechanical Engineering
dc.subject.disciplines Plant Pathology
dc.subject.keywords image-based phenotyping
dc.subject.keywords machine learning
dc.subject.keywords deep learning
dc.subject.keywords biotic stress
dc.subject.keywords abiotic stress
dc.subject.keywords standard area diagram
dc.title Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping
dc.type article
dc.type.genre article
dspace.entity.type Publication
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