Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors

Monitoring leaf Chlorophyll (Chl) in-situ is labor-intensive, limiting representative sampling for detailed mapping of Chl variability at field scales across time. Unmanned aeria-l vehicles (UAV) and hyperspectral cameras provide flexible platforms for observing agricultural systems, overcoming this...

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Detalles Bibliográficos
Autores principales: Angel, Yoseline, McCabe, Matthew F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Plant Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963469/
https://www.ncbi.nlm.nih.gov/pubmed/35360313
http://dx.doi.org/10.3389/fpls.2022.722442

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963469/
https://www.ncbi.nlm.nih.gov/pubmed/35360313
http://dx.doi.org/10.3389/fpls.2022.722442

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