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Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination
Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163850/ https://www.ncbi.nlm.nih.gov/pubmed/30142904 http://dx.doi.org/10.3390/s18092770 |
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author | Tamouridou, Afroditi Alexandra Pantazi, Xanthoula Eirini Alexandridis, Thomas Lagopodi, Anastasia Kontouris, Giorgos Moshou, Dimitrios |
author_facet | Tamouridou, Afroditi Alexandra Pantazi, Xanthoula Eirini Alexandridis, Thomas Lagopodi, Anastasia Kontouris, Giorgos Moshou, Dimitrios |
author_sort | Tamouridou, Afroditi Alexandra |
collection | PubMed |
description | Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage. |
format | Online Article Text |
id | pubmed-6163850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61638502018-10-10 Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination Tamouridou, Afroditi Alexandra Pantazi, Xanthoula Eirini Alexandridis, Thomas Lagopodi, Anastasia Kontouris, Giorgos Moshou, Dimitrios Sensors (Basel) Article Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage. MDPI 2018-08-23 /pmc/articles/PMC6163850/ /pubmed/30142904 http://dx.doi.org/10.3390/s18092770 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tamouridou, Afroditi Alexandra Pantazi, Xanthoula Eirini Alexandridis, Thomas Lagopodi, Anastasia Kontouris, Giorgos Moshou, Dimitrios Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title | Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title_full | Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title_fullStr | Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title_full_unstemmed | Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title_short | Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination |
title_sort | spectral identification of disease in weeds using multilayer perceptron with automatic relevance determination |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163850/ https://www.ncbi.nlm.nih.gov/pubmed/30142904 http://dx.doi.org/10.3390/s18092770 |
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