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Article

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

1
School of Agriculture and Food Sciences, The University of Queensland, Gatton Campus, QLD 4343, Australia
2
Department of Agriculture Technology, Faculty of Agriculture, University Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Silvia Arazuri
Agronomy 2021, 11(7), 1435; https://doi.org/10.3390/agronomy11071435
Received: 29 May 2021 / Revised: 7 July 2021 / Accepted: 16 July 2021 / Published: 19 July 2021
Weeds compete with crops and are hard to differentiate and identify due to their similarities in color, shape, and size. In this study, the weed species present in sorghum (sorghum bicolor (L.) Moench) fields, such as amaranth (Amaranthus macrocarpus), pigweed (Portulaca oleracea), mallow weed (Malva sp.), nutgrass (Cyperus rotundus), liver seed grass (Urochoa panicoides), and Bellive (Ipomea plebeian), were discriminated using hyperspectral data and were detected and analyzed using multispectral images. Discriminant analysis (DA) was used to identify the most significant spectral bands in order to discriminate weeds from sorghum using hyperspectral data. The results demonstrated good separation accuracy for Amaranthus macrocarpus, Urochoa panicoides, Malva sp., Cyperus rotundus, and Sorghum bicolor (L.) Moench at 440, 560, 680, 710, 720, and 850 nm. Later, the multispectral images of these six bands were collected to detect weeds in the sorghum crop fields using object-based image analysis (OBIA). The results showed that the differences between sorghum and weed species were detectable using the six selected bands, with data collected using an unmanned aerial vehicle. Here, the highest spatial resolution had the highest accuracy for weed detection. It was concluded that each weed was successfully discriminated using hyperspectral data and was detectable using multispectral data with higher spatial resolution. View Full-Text
Keywords: weed classification; hyperspectral reflectance; discriminant analysis; weed species; weed mapping; site-specific weed management weed classification; hyperspectral reflectance; discriminant analysis; weed species; weed mapping; site-specific weed management
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MDPI and ACS Style

Che’Ya, N.N.; Dunwoody, E.; Gupta, M. Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery. Agronomy 2021, 11, 1435. https://doi.org/10.3390/agronomy11071435

AMA Style

Che’Ya NN, Dunwoody E, Gupta M. Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery. Agronomy. 2021; 11(7):1435. https://doi.org/10.3390/agronomy11071435

Chicago/Turabian Style

Che’Ya, Nik N., Ernest Dunwoody, and Madan Gupta. 2021. "Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery" Agronomy 11, no. 7: 1435. https://doi.org/10.3390/agronomy11071435

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