Cargando…
Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data
Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe an...
Autores principales: | Wiesner-Hanks, Tyr, Wu, Harvey, Stewart, Ethan, DeChant, Chad, Kaczmar, Nicholas, Lipson, Hod, Gore, Michael A., Nelson, Rebecca J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927297/ https://www.ncbi.nlm.nih.gov/pubmed/31921228 http://dx.doi.org/10.3389/fpls.2019.01550 |
Ejemplares similares
-
Image set for deep learning: field images of maize annotated with disease symptoms
por: Wiesner-Hanks, Tyr, et al.
Publicado: (2018) -
Aerial photographic interpretation :principles and applications
por: Lueder, Donald R.
Publicado: (1959) -
Rapid Grading of Fundus Photographs for Diabetic Retinopathy Using Crowdsourcing
por: Brady, Christopher J, et al.
Publicado: (2014) -
An analysis of companion animal tick encounters as revealed by photograph‐based crowdsourced data
por: Kopsco, Heather L., et al.
Publicado: (2021) -
Maize Introgression Library Provides Evidence for the Involvement of liguleless1 in Resistance to Northern Leaf Blight
por: Kolkman, Judith M., et al.
Publicado: (2020)