About: Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
Creator	Abidine, El Dutagaci, Helin Merdinoglu-Wiedemann, Sabine Rasti, Pejman Rousseau, David Zine, Mouad
Source	PMC
abstract	Precision agriculture faces challenges related to plant disease detection. Plant phenotyping assesses the appearance to select the best genotypes that resist to varying environmental conditions via plant variety testing. In this process, official plant variety tests are currently performed in vitro by visual inspection of samples placed in a culture media. In this communication, we demonstrate the potential of a computer vision approach to perform such tests in a much faster and reproducible way. We highlight the benefit of fusing contrasts coming from front and back light. To the best of our knowledge, this is illustrated for the first time on the classification of the severity of the presence of a fungi, powdery mildew, on melon leaves with 95% of accuracy.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51935-3_8
has license	no-cc
sha1sum (hex)	733978d99f40bb1f049d0210fbeed24dbc93f7e3
schema:url	https://doi.org/10.1007/978-3-030-51935-3_8
resource representing a document's title	Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
has PubMed Central identifier	PMC7340913
schema:publication	Image and Signal Processing
resource representing a document's body	covid:733978d99f40bb1f049d0210fbeed24dbc93f7e3#body_text
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