Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms

Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was...

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Detalles Bibliográficos
Autores principales: DeSalvio, Aaron J., Adak, Alper, Murray, Seth C., Wilde, Scott C., Isakeit, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9085875/
https://www.ncbi.nlm.nih.gov/pubmed/35534655
http://dx.doi.org/10.1038/s41598-022-11591-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9085875/
https://www.ncbi.nlm.nih.gov/pubmed/35534655
http://dx.doi.org/10.1038/s41598-022-11591-0

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