New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models
The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10107/1/J16248_dfba6cf89b35312a27fbc7fff1ce39b0.pdf |
| _version_ | 1825638291529007104 |
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| author | Pauline Ong, Pauline Ong Jinbao Jian, Jinbao Jian Xiuhua Li, Xiuhua Li Chengwu Zou, Chengwu Zou Jianghua Yin, Jianghua Yin Guodong Ma, odong Ma |
| author_facet | Pauline Ong, Pauline Ong Jinbao Jian, Jinbao Jian Xiuhua Li, Xiuhua Li Chengwu Zou, Chengwu Zou Jianghua Yin, Jianghua Yin Guodong Ma, odong Ma |
| author_sort | Pauline Ong, Pauline Ong |
| collection | UTHM |
| description | The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380–1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm. |
| first_indexed | 2024-03-05T22:04:34Z |
| format | Article |
| id | uthm.eprints-10107 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:04:34Z |
| publishDate | 2023 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | uthm.eprints-101072023-10-17T06:55:54Z http://eprints.uthm.edu.my/10107/ New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models Pauline Ong, Pauline Ong Jinbao Jian, Jinbao Jian Xiuhua Li, Xiuhua Li Chengwu Zou, Chengwu Zou Jianghua Yin, Jianghua Yin Guodong Ma, odong Ma TA170-171 Environmental engineering The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380–1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm. Elsevier 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10107/1/J16248_dfba6cf89b35312a27fbc7fff1ce39b0.pdf Pauline Ong, Pauline Ong and Jinbao Jian, Jinbao Jian and Xiuhua Li, Xiuhua Li and Chengwu Zou, Chengwu Zou and Jianghua Yin, Jianghua Yin and Guodong Ma, odong Ma (2023) New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 302. pp. 1-11. https://doi.org/10.1016/j.saa.2023.123037 |
| spellingShingle | TA170-171 Environmental engineering Pauline Ong, Pauline Ong Jinbao Jian, Jinbao Jian Xiuhua Li, Xiuhua Li Chengwu Zou, Chengwu Zou Jianghua Yin, Jianghua Yin Guodong Ma, odong Ma New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title | New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title_full | New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title_fullStr | New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title_full_unstemmed | New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title_short | New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models |
| title_sort | new approach for sugarcane disease recognition through visible and near infrared spectroscopy and a modified wavelength selection method using machine learning models |
| topic | TA170-171 Environmental engineering |
| url | http://eprints.uthm.edu.my/10107/1/J16248_dfba6cf89b35312a27fbc7fff1ce39b0.pdf |
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