Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: El Abidine, Mouad Zine, Merdinoglu-Wiedemann, Sabine, Rasti, Pejman, Dutagaci, Helin, Rousseau, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340913/
http://dx.doi.org/10.1007/978-3-030-51935-3_8
_version_ 1783555120572661760
author El Abidine, Mouad Zine
Merdinoglu-Wiedemann, Sabine
Rasti, Pejman
Dutagaci, Helin
Rousseau, David
author_facet El Abidine, Mouad Zine
Merdinoglu-Wiedemann, Sabine
Rasti, Pejman
Dutagaci, Helin
Rousseau, David
author_sort El Abidine, Mouad Zine
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7340913
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-73409132020-07-08 Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves El Abidine, Mouad Zine Merdinoglu-Wiedemann, Sabine Rasti, Pejman Dutagaci, Helin Rousseau, David Image and Signal Processing Article 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. 2020-06-05 /pmc/articles/PMC7340913/ http://dx.doi.org/10.1007/978-3-030-51935-3_8 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
El Abidine, Mouad Zine
Merdinoglu-Wiedemann, Sabine
Rasti, Pejman
Dutagaci, Helin
Rousseau, David
Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title_full Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title_fullStr Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title_full_unstemmed Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title_short Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves
title_sort machine learning-based classification of powdery mildew severity on melon leaves
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340913/
http://dx.doi.org/10.1007/978-3-030-51935-3_8
work_keys_str_mv AT elabidinemouadzine machinelearningbasedclassificationofpowderymildewseverityonmelonleaves
AT merdinogluwiedemannsabine machinelearningbasedclassificationofpowderymildewseverityonmelonleaves
AT rastipejman machinelearningbasedclassificationofpowderymildewseverityonmelonleaves
AT dutagacihelin machinelearningbasedclassificationofpowderymildewseverityonmelonleaves
AT rousseaudavid machinelearningbasedclassificationofpowderymildewseverityonmelonleaves