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Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds
Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249031/ https://www.ncbi.nlm.nih.gov/pubmed/32354139 http://dx.doi.org/10.3390/s20092504 |
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author | Lauwers, Marlies De Cauwer, Benny Nuyttens, David Cool, Simon R. Pieters, Jan G. |
author_facet | Lauwers, Marlies De Cauwer, Benny Nuyttens, David Cool, Simon R. Pieters, Jan G. |
author_sort | Lauwers, Marlies |
collection | PubMed |
description | Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model. |
format | Online Article Text |
id | pubmed-7249031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72490312020-06-10 Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds Lauwers, Marlies De Cauwer, Benny Nuyttens, David Cool, Simon R. Pieters, Jan G. Sensors (Basel) Article Cyperus esculentus (yellow nutsedge) is one of the world’s worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key—a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares–discriminant analysis (PLS–DA). RLR performed better than RF and PLS–DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS–DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model. MDPI 2020-04-28 /pmc/articles/PMC7249031/ /pubmed/32354139 http://dx.doi.org/10.3390/s20092504 Text en © 2020 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 Lauwers, Marlies De Cauwer, Benny Nuyttens, David Cool, Simon R. Pieters, Jan G. Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title | Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title_full | Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title_fullStr | Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title_full_unstemmed | Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title_short | Hyperspectral Classification of Cyperus esculentus Clones and Morphologically Similar Weeds |
title_sort | hyperspectral classification of cyperus esculentus clones and morphologically similar weeds |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249031/ https://www.ncbi.nlm.nih.gov/pubmed/32354139 http://dx.doi.org/10.3390/s20092504 |
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