Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat

Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluoresce...

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Detalles Bibliográficos
Autores principales: Odilbekov, Firuz, Armoniené, Rita, Henriksson, Tina, Chawade, Aakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Plant Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974968/
https://www.ncbi.nlm.nih.gov/pubmed/29875788
http://dx.doi.org/10.3389/fpls.2018.00685

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974968/
https://www.ncbi.nlm.nih.gov/pubmed/29875788
http://dx.doi.org/10.3389/fpls.2018.00685

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