Integrating genotype and weather variables for soybean yield prediction using deep learning

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dc.contributor.author Singh, Asheesh
dc.contributor.author Shook, Johnathon
dc.contributor.author Wu, Linjiang
dc.contributor.author Gangopadhyay, Tryambak
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Sarkar, Soumik
dc.contributor.author Singh, Asheesh
dc.contributor.department Mechanical Engineering
dc.contributor.department Agronomy
dc.date 2018-12-09T15:48:06.000
dc.date.accessioned 2020-06-29T23:06:02Z
dc.date.available 2020-06-29T23:06:02Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-05-25
dc.description.abstract <p>Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing.</p>
dc.description.comments <p>This is a pre-print made available through bioRxiv, doi: <a href="http://dx.doi.org/10.1101/331561" target="_blank">10.1101/331561</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/542/
dc.identifier.articleid 1599
dc.identifier.contextkey 13413908
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/542
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/4908
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/542/2018_Singh_IntegratingGenotypePreprint.pdf|||Sat Jan 15 00:53:47 UTC 2022
dc.source.uri 10.1101/331561
dc.subject.disciplines Agriculture
dc.subject.disciplines Climate
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Plant Breeding and Genetics
dc.subject.keywords machine/deep learning
dc.subject.keywords yield prediction
dc.subject.keywords genotype-environment
dc.title Integrating genotype and weather variables for soybean yield prediction using deep learning
dc.type article
dc.type.genre article
dspace.entity.type Publication
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