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ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » People & Locations » Daniel Kick
Daniel Kick
Plant Genetics Research
Research Geneticist (Plants)

Phone: (573) 882-2033
Fax:

UNIVERSITY OF MISSOURI
CURTIS HALL
COLUMBIA, MO 65211

(Employee information on this page comes from the REE Directory. Please contact your front office staff to update the REE Directory.)

Projects
Environmentally Aware Deep Learning Based Genomic Selection And Management Optimization For Maize Yield
Interagency Reimbursable Agreement (I)
  Accession Number: 444154

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)
RootBot: high-throughput root stress phenotyping robot Reprint Icon - (Peer Reviewed Journal)
Ruppel, M., Nelson, S., Sidberry, G., Mitchell, M., Kick, D.R., Thomas, S., Guill, K., Oliver, M., Washburn, J.D. 2023. RootBot: high-throughput root stress phenotyping robot. Applications in Plant Sciences. 11(6): Article e11541. https://doi.org/10.1002/aps3.11541.
Ensemble of BLUP, machine learning, and deep learning models predict maize yield better than each model alone Reprint Icon - (Pre-print Publication)
Kick, D.R., Washburn, J.D. 2023. Ensemble of BLUP, machine learning, and deep learning models predict maize yield better than each model alone. bioRxiv. Article bioRxiv 2023.03.30.532932. https://doi.org/10.1101/2023.03.30.532932.
Yield prediction through integration of genetic, environment, and management data through deep learning Reprint Icon - (Peer Reviewed Journal)
Kick, D.R., Wallace, J.G., Schnable, J.C., Kolkmann, J.M., Alaca, B., Beissinger, T.M., Edwards, J.W., Ertl, D., Flint-Garcia, S.A., Gage, J.L., Hirsch, C.N., Knoll, J.E., de Leon, N., Lima, D.C., Moreta, D., Singh, M.P., Thompson, A., Weldekidan, T., Washburn, J.D. 2023. Yield prediction through integration of genetic, environment, and management data through deep learning. G3, Genes/Genomes/Genetics. 13(4). Article jkad006. https://doi.org/10.1093/g3journal/jkad006.
Yield prediction through integration of genetic, environment, and management data through deep learning Reprint Icon - (Pre-print Publication)
Kick, D.R., Wallace, J.G., Schnable, J.C., Kolkmann, J.M., Boris, A., Beissinger, T.M., Irtl, D., Flint Garcia, S.A., Gage, J.L., Hirsch, C.N., Knoll, J.E., De Leon, N., Lima, D.C., Moreta, D., Singh, M.P., Weldekidan, T., Washburn, J.D. 2022. Yield prediction through integration of genetic, environment, and management data through deep learning. bioRxiv. Article bioRxiv 2022.07.29.502051. https://doi.org/10.1101/2022.07.29.502051.