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Pixel classification methods for identifying and quantifying leaf surface injury from digital images.

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Pixel classification methods for identifying and quantifying leaf surface injury from digital images.

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Opstad Kruse, OM.; Prats Montalbán, JM.; Indahl, UG.; Kvaal, K.; Ferrer Riquelme, AJ.; Futsaether, CM. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture. 108:155-165. doi:10.1016/j.compag.2014.07.010

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/50765

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Title: Pixel classification methods for identifying and quantifying leaf surface injury from digital images.
Author:
UPV Unit: Universitat Politècnica de València. Grupo de Ingeniería Estadística Multivariante
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision ...[+]
Subjects: Classification , Feature extraction , Fit to a pattern model approach (FPM) , Linear discriminant analysis (LDA) , K-means clustering , Multivariate image analysis (MIA)
Copyrigths: Reserva de todos los derechos
Source:
Computers and Electronics in Agriculture. (issn: 0168-1699 )
DOI: 10.1016/j.compag.2014.07.010
Publisher:
Elsevier
Publisher version: http://dx.doi.org/10.1016/j.compag.2014.07.010
Type: Artículo

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