Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

dc.contributor.author Singh, Asheesh
dc.contributor.author Singh, Asheesh Kumar
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.author Sarkar, Soumik
dc.contributor.author Singh, Arti
dc.contributor.author Ganapathysubramanian, Baskar
dc.contributor.department Mechanical Engineering
dc.contributor.department Agronomy
dc.date 2018-12-09T15:56:05.000
dc.date.accessioned 2020-06-29T23:06:06Z
dc.date.available 2020-06-29T23:06:06Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-10-01
dc.description.abstract <p>Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science.</p>
dc.description.comments <p>This article is published as Singh, Asheesh Kumar, Baskar Ganapathysubramanian, Soumik Sarkar, and Arti Singh. "Deep learning for plant stress phenotyping: trends and future perspectives." <em>Trends in plant science</em> 23 (2018): 883-898. doi: <a href="https://doi.org/10.1016/j.tplants.2018.07.004" target="_blank" title="Persistent link using digital object identifier">10.1016/j.tplants.2018.07.004</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/agron_pubs/550/
dc.identifier.articleid 1590
dc.identifier.contextkey 13409654
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath agron_pubs/550
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/4917
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/agron_pubs/550/2018_Singh_DeepLearning.pdf|||Sat Jan 15 00:55:36 UTC 2022
dc.source.uri 10.1016/j.tplants.2018.07.004
dc.subject.disciplines Agriculture
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Plant Sciences
dc.title Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
dc.type article
dc.type.genre article
dspace.entity.type Publication
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