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Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds

(2020) SENSORS. 20(9).
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Abstract
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.
Keywords
reflectance, logistic regression, partial least squares–discriminant analysis, random forest, yellow nutsedge, weed classification, YELLOW NUTSEDGE, RANDOM FOREST, TUBER PRODUCTION, GLYPHOSATE, GROWTH, ROTUNDUS, PURPLE, SCATTERING, HERBICIDES, EFFICACY

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Citation

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MLA
Lauwers, Marlies, et al. “Hyperspectral Classification of Cyperus Esculentus Clones and Morphologically Similar Weeds.” SENSORS, vol. 20, no. 9, 2020, doi:10.3390/s20092504.
APA
Lauwers, M., De Cauwer, B., Nuyttens, D., Cool, S. R., & Pieters, J. (2020). Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds. SENSORS, 20(9). https://doi.org/10.3390/s20092504
Chicago author-date
Lauwers, Marlies, Benny De Cauwer, David Nuyttens, Simon R. Cool, and Jan Pieters. 2020. “Hyperspectral Classification of Cyperus Esculentus Clones and Morphologically Similar Weeds.” SENSORS 20 (9). https://doi.org/10.3390/s20092504.
Chicago author-date (all authors)
Lauwers, Marlies, Benny De Cauwer, David Nuyttens, Simon R. Cool, and Jan Pieters. 2020. “Hyperspectral Classification of Cyperus Esculentus Clones and Morphologically Similar Weeds.” SENSORS 20 (9). doi:10.3390/s20092504.
Vancouver
1.
Lauwers M, De Cauwer B, Nuyttens D, Cool SR, Pieters J. Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds. SENSORS. 2020;20(9).
IEEE
[1]
M. Lauwers, B. De Cauwer, D. Nuyttens, S. R. Cool, and J. Pieters, “Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds,” SENSORS, vol. 20, no. 9, 2020.
@article{8660215,
  abstract     = {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.},
  articleno    = {2504},
  author       = {Lauwers, Marlies and De Cauwer, Benny and Nuyttens, David and Cool, Simon R. and Pieters, Jan},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {reflectance,logistic regression,partial least squares–discriminant analysis,random forest,yellow nutsedge,weed classification,YELLOW NUTSEDGE,RANDOM FOREST,TUBER PRODUCTION,GLYPHOSATE,GROWTH,ROTUNDUS,PURPLE,SCATTERING,HERBICIDES,EFFICACY},
  language     = {eng},
  number       = {9},
  pages        = {18},
  title        = {Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds},
  url          = {http://dx.doi.org/10.3390/s20092504},
  volume       = {20},
  year         = {2020},
}

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