An integrated approach of field, weather, and satellite data for monitoring maize phenology

Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological...

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Detalles Bibliográficos
Autores principales: Nieto, Luciana, Schwalbert, Raí, Prasad, P. V. Vara, Olson, Bradley J. S. C., Ciampitti, Ignacio A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8333045/
https://www.ncbi.nlm.nih.gov/pubmed/34344979
http://dx.doi.org/10.1038/s41598-021-95253-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8333045/
https://www.ncbi.nlm.nih.gov/pubmed/34344979
http://dx.doi.org/10.1038/s41598-021-95253-7

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