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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
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

References

Borraz-Martínez, S, Simó, J, Gras, A, Mestre, M, Boqué, R (2019) Multivariate classification of Prunus dulcis varieties using leaves of nursery plants and near infrared spectroscopy. Sci Rep 9:1–9 CrossRefGoogle ScholarPubMed
Braga, AF, Barroso, AAM, Amaral, CL, Nepomuceno, MP, Alves, PLCA (2018) Population interference of glyphosate resistant and susceptible ryegrass on eucalyptus initial development. Planta Daninha 36:1–10 CrossRefGoogle Scholar
Carvalho, S, Schlerf, M, van Der Putten, WH, Skidmore, AK (2013) Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation. Int J Appl Earth Obs 24:32–41 Google Scholar
Cunha Junior, LC, Nardini, V, Khatiwada, BP, Teixeira, GHA, Walsh, KB (2015) Classification of intact açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy. Food Control 50:630–636 CrossRefGoogle Scholar
Cunha Júnior, LC, Teixeira, GHA, Nardini, V, Walsh, KB (2016) Quality evaluation of intact açaí and juçara fruit by means of near infrared spectroscopy. Postharvest Biol Technol 112:64–74 CrossRefGoogle Scholar
Dammer, KH, Wartenberg, G (2007) Sensor-based weed detection and application of variable herbicide rates in real time. Crop Prot 26:270–277 CrossRefGoogle Scholar
Hoagland, DR, Arnon, DJ (1938) The Water Culture Method of Growing Plants without Soil. Berkeley: University of California College of Agriculture. Agricultural Experiment Station Circular 347. 31 p. http://hdl.handle.net/2027/uc2.ark:/13960/t51g1sb8j. Accessed: September 2, 2022Google Scholar
Jamshidi, B, Mohajerani, E, Jamshidi, J (2016) Developing a Vis/NIR spectroscopic system for fast and non-destructive pesticide residue monitoring in agricultural product. Measurement 89:1–6 CrossRefGoogle Scholar
Jenett-Siems, K, Schimming, T, Kaloga, M, Eich, E, Siems, K, Gupta, MP, Witte, L, Hartmann, T (1998) Pyrrolizidine alkaloids of Ipomoea hederifolia and related species. Phytochemistry 47:1551–1560 CrossRefGoogle Scholar
Jintao, X, Yufei, L, Liming, Y, Chunyan, L, Quanwei, Y, Weiying, W, Yun, J, Minxiang, Z, Peng, L (2018) Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy. Spectrochim Acta A 188:611–618 CrossRefGoogle ScholarPubMed
Kennard, RW, Stone, LA (1969) Computer aided design of experiments. Technometrics 11:137–148 CrossRefGoogle Scholar
López-Granados, F (2010) Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res 51:1–11 CrossRefGoogle Scholar
Meira, M, Silva, EPD, David, JM, David, JP (2012) Review of the genus Ipomoea: traditional uses, chemistry and biological activities. Rev Bras Farmacogn 22:682–713 CrossRefGoogle Scholar
Neto, AS, Lopes, DC, Toledo, JV, Zolnier, S, Silva, TGF (2018) Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods. J Agric Sci 156:537–546 Google Scholar
Nicolai, BM, Beullens, K, Bobelyn, E, Peirs, A, Saeys, W, Theron, KI, Lammertyn, J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46:99–118 CrossRefGoogle Scholar
Pasquini, C (2003) Near infrared spectroscopy: fundamentals. Practical aspects and analytical applications. J Braz Chem Soc 14:198–219 CrossRefGoogle Scholar
Piza, CST, Nepomuceno, MP, Alves, PLCA (2016) Period prior to interference of morning glory in sugarcane. Científica 44:543–548 CrossRefGoogle Scholar
Pontes, MJC, Cortez, J, Galvão, RKH, Pasquini, C, Araújo, MCU, Coelho, RM, Chiba, MK, Abreud, MF, Madari, BE (2009) Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain. Anal Chim Acta 642:12–18 CrossRefGoogle ScholarPubMed
Rolim, GDS, Camargo, MBPD, Lania, DG, Moraes, JFLD (2007) Climatic classification of Köppen and Thornthwaite systems and their applicability in the determination of agroclimatic zoning for the state of São Paulo, Brazil. Bragantia 66:711–720 CrossRefGoogle Scholar
Snel, FA, Braga, JW, Silva, D, Wiedenhoeft, AC, Costa, A, Soares, R, Coradin, VTR, Pastore, TC (2018) Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci Technol 52:1411–1427 CrossRefGoogle Scholar
Soares, LF, Silva, DCD, Bergo, MC, Coradin, VT, Braga, JW, Pastore, T (2017) Evaluation of a NIR handheld device and PLS-DA for discrimination of six similar amazonian wood species. Quím Nova 40:418–426 Google Scholar
Souza, MF, Amaral, LR, Medeiros Oliveira, SR, Coutinho, MAN, Netto, CF (2020) Spectral differentiation of sugarcane from weeds. Biosyst Eng 190:41–46 CrossRefGoogle Scholar
Viator, B, Griffin, J, Ellis, J (2002) Red morningglory (Ipomoea coccinea) control with sulfentrazone and azafeniden applied at layby in sugarcane (Saccharum spp.). Weed Technol 16:142–148 Google Scholar
Wang, P, Yu, Z (2015) Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy: a review. J Pharm Anal 5:277–284 CrossRefGoogle ScholarPubMed
Xiaobo, Z, Jiewen, Z, Povey, MJW, Holmes, M, Hanpin, M (2010) Variables selection methods in near-infrared spectroscopy. Anal Chim Acta 667:14–32 CrossRefGoogle ScholarPubMed
Youden, WJ (1950) Index for rating diagnostic tests. Cancer 3:32–35 3.0.CO;2-3>CrossRefGoogle ScholarPubMed