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Discrimination of morningglory species (Ipomoea spp.) using near-infrared spectroscopy and multivariate analysis

Published online by Cambridge University Press:  15 February 2023

Andreísa Flores Braga Open the ORCID record for Andreísa Flores Braga [Opens in a new window] ,
Leandro Aparecido Chiconi Open the ORCID record for Leandro Aparecido Chiconi [Opens in a new window] ,
Allan Lopes Bacha Open the ORCID record for Allan Lopes Bacha [Opens in a new window] ,
Gustavo Henrique de Almeida Teixeira Open the ORCID record for Gustavo Henrique de Almeida Teixeira [Opens in a new window] ,
Luis Carlos Cunha Junior Open the ORCID record for Luis Carlos Cunha Junior [Opens in a new window]  and
Pedro Luis da Costa Aguiar Alves Open the ORCID record for Pedro Luis da Costa Aguiar Alves [Opens in a new window]
Andreísa Flores Braga
Affiliation:
Research Scientist, Support Foundation for the Technological Research Institute of the São Paulo State, São Paulo-SP, Brazil
Leandro Aparecido Chiconi
Affiliation:
Research Assistant, ICL Brasil, São Paulo-SP, Brazil
Allan Lopes Bacha*
Affiliation:
Postdoctoral Researcher, Weed Sciences Laboratory (LAPDA), Department of Biology, Sao Paulo State University (Unesp/FCAV), Jaboticabal-SP, Brazil
Gustavo Henrique de Almeida Teixeira
Affiliation:
Professor, Department of Agricultural Production, Sao Paulo State University (Unesp/FCAV), Jaboticabal-SP, Brazil
Luis Carlos Cunha Junior
Affiliation:
Professor, Department of Horticulture, Federal University of Goias, Goiânia-GO, Brazil
Pedro Luis da Costa Aguiar Alves
Affiliation:
Professor, Department of Biology, Sao Paulo State University (Unesp/FCAV), Jaboticabal-SP, Brazil
*
Author for correspondence: Allan Lopes Bacha, Sao Paulo State University, School of Agricultural and Veterinary Studies (Unesp/FCAV), Jaboticabal-SP 14884-900, Brazil. Email: allan.bacha@unesp.br
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Abstract

The occurrence of weeds is one of the main factors limiting agricultural productivity. Studies on new techniques for the identification of these species can contribute to the development of proximal sensors, which in the future might be coupled to machines to optimize the performance of species-specific weed management. Thus, the objective of this study was to use near-infrared (NIR) spectroscopy and multivariate analysis to discriminate three morningglory species (Ipomoea spp.). The NIR spectra were collected from the leaves of the three weed species at the vegetative stage (up to five leaves), within the spectral band of 4,000 to 10,000 cm−1. The discrimination models were selected according to accuracy, sensitivity, specificity, and Youden’s index and were analyzed with a validation data set (n = 135). The best results occurred when the selection of spectral bands associated with the use of preprocessing was performed. It was possible to obtain an accuracy of 99.3%, 98.5%, and 98.7% for ivyleaf morningglory (Ipomoea hederifolia L.), Japanese morningglory [Ipomoea nil (L.) Roth], and hairy woodrose [Merremia aegyptia (L.) Urb.], respectively. NIR spectroscopy associated with principal component analysis and linear discriminant analysis (PC-LDA) or partial least-squares regression with discriminant analysis (PLS-DA) can be used to discriminate Ipomoea spp.

Keywords

Ipomoea hederifoliaIpomoea nilMerremia aegyptiaPC-LDAPLS-DAweed management

Information

Type
Research Article
Information
Weed Science , Volume 71 , Issue 2 , March 2023 , pp. 104 - 111
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Weed Science Society of America

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Footnotes

Associate Editor: Prashant Jha, Iowa State University

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