Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery
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 (Malv...
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/96095/ |
| _version_ | 1848862294445916160 |
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| author | Che’Ya, Nik Norasma Dunwoody, Ernest Gupta, Madan |
| author_facet | Che’Ya, Nik Norasma Dunwoody, Ernest Gupta, Madan |
| author_sort | Che’Ya, Nik Norasma |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:14:44Z |
| format | Article |
| id | upm-96095 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:14:44Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-960952023-02-24T03:49:05Z http://psasir.upm.edu.my/id/eprint/96095/ Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery Che’Ya, Nik Norasma Dunwoody, Ernest Gupta, Madan 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. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed Che’Ya, Nik Norasma and Dunwoody, Ernest and Gupta, Madan (2021) Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery. Agronomy-Basel, 11 (7). art. no. 1435. pp. 1-16. ISSN 2073-4395 https://www.mdpi.com/2073-4395/11/7/1435 10.3390/agronomy11071435 |
| spellingShingle | Che’Ya, Nik Norasma Dunwoody, Ernest Gupta, Madan Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title | Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title_full | Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title_fullStr | Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title_full_unstemmed | Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title_short | Assessment of weed classification using hyperspectral reflectance and optimal multispectral UAV imagery |
| title_sort | assessment of weed classification using hyperspectral reflectance and optimal multispectral uav imagery |
| url | http://psasir.upm.edu.my/id/eprint/96095/ http://psasir.upm.edu.my/id/eprint/96095/ http://psasir.upm.edu.my/id/eprint/96095/ |