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...
Autores principales: | , , , , |
---|---|
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 |