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Year Number of Results
2018 1
2019 2
2020 2
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Page 1
Image set for deep learning: field images of maize annotated with disease symptoms.
Wiesner-Hanks T, Stewart EL, Kaczmar N, DeChant C, Wu H, Nelson RJ, Lipson H, Gore MA. Wiesner-Hanks T, et al. Among authors: kaczmar n. BMC Res Notes. 2018 Jul 3;11(1):440. doi: 10.1186/s13104-018-3548-6. BMC Res Notes. 2018. PMID: 29970178 Free PMC article.
Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data.
Wiesner-Hanks T, Wu H, Stewart E, DeChant C, Kaczmar N, Lipson H, Gore MA, Nelson RJ. Wiesner-Hanks T, et al. Among authors: kaczmar n. Front Plant Sci. 2019 Dec 12;10:1550. doi: 10.3389/fpls.2019.01550. eCollection 2019. Front Plant Sci. 2019. PMID: 31921228 Free PMC article.
Genome-Wide Association and Genomic Prediction Models of Tocochromanols in Fresh Sweet Corn Kernels.
Baseggio M, Murray M, Magallanes-Lundback M, Kaczmar N, Chamness J, Buckler ES, Smith ME, DellaPenna D, Tracy WF, Gore MA. Baseggio M, et al. Among authors: kaczmar n. Plant Genome. 2019 Mar;12(1). doi: 10.3835/plantgenome2018.06.0038. Plant Genome. 2019. PMID: 30951088 Free article.
Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.
Anche MT, Kaczmar NS, Morales N, Clohessy JW, Ilut DC, Gore MA, Robbins KR. Anche MT, et al. Among authors: kaczmar ns. Theor Appl Genet. 2020 Oct;133(10):2853-2868. doi: 10.1007/s00122-020-03637-6. Epub 2020 Jul 1. Theor Appl Genet. 2020. PMID: 32613265
Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development.
Lin M, Matschi S, Vasquez M, Chamness J, Kaczmar N, Baseggio M, Miller M, Stewart EL, Qiao P, Scanlon MJ, Molina I, Smith LG, Gore MA. Lin M, et al. Among authors: kaczmar n. G3 (Bethesda). 2020 May 4;10(5):1671-1683. doi: 10.1534/g3.119.400884. G3 (Bethesda). 2020. PMID: 32184371 Free PMC article.