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 Plant Genetics Research: Columbia, MO
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ARS Home Midwest Area Columbia, Missouri Plant Genetics Research People & Locations







Daniel  Kick
                        Plant Genetics Research

                        




                        
                        Research Geneticist (Plants)
                        Phone: (573) 882-2033
                        
                        
                        Fax: (Employee information on this page comes from the REE Directory. Please contact your front office staff to update the REE Directory.)
                            ProjectsEnvironmentally 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.)Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone
                                                        

- (Peer Reviewed Journal)Kick, D.R., Washburn, J.D. 2023. Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone. in silico Plants. 5(2). Article diad015. https://doi.org/10.1093/insilicoplants/diad015.RootBot: high-throughput root stress phenotyping robot
                                                        

- (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
                                                        

- (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
                                                        

- (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
                                                        

- (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.


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