A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle
IntroductionThe Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.MethodsThis paper proposes a method for detecting CCB pests...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1464723/full |
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| author | Keliang Hu Keliang Hu Junchen Liu Hai Xiao Qiangguo Zeng Jun Liu Jun Liu Lei Zhang Lei Zhang Man Li Man Li Zhihui Wang Zhihui Wang |
| author_facet | Keliang Hu Keliang Hu Junchen Liu Hai Xiao Qiangguo Zeng Jun Liu Jun Liu Lei Zhang Lei Zhang Man Li Man Li Zhihui Wang Zhihui Wang |
| author_sort | Keliang Hu |
| collection | DOAJ |
| description | IntroductionThe Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.MethodsThis paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data. To tackle the high cost and challenging data processing issues associated with traditional hyper-spectral/multi-spectral sensors, this method only relies on UAV visible light RGB bands. The process first involves calculating and normalizing 24 visible light vegetation indices from the UAV RGB images of the monitoring area, along with the original RGB bands. To account for the collinearity relationship between indices, the random forest variable importance and correlation coefficient iterative analysis algorithm are employed to select indices, retaining the most important or lowest collinearity multiple vegetation indices. Subsequently, the Beluga Whale Optimization (BWO) algorithm is utilized to generate a new vegetation index, which is then combined with the multi-threshold segmentation method to propose a BWO-weighted ensemble strategy for obtaining the final pests and diseases detection results.Results and discussionThe experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees. |
| format | Article |
| id | doaj-art-f1b81937c2b94e77be3a632621befa42 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-f1b81937c2b94e77be3a632621befa422024-12-11T04:32:16ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14647231464723A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicleKeliang Hu0Keliang Hu1Junchen Liu2Hai Xiao3Qiangguo Zeng4Jun Liu5Jun Liu6Lei Zhang7Lei Zhang8Man Li9Man Li10Zhihui Wang11Zhihui Wang12School of Informatics, Hunan University of Chinese Medicine, Changsha, ChinaAI TCM Lab Hunan, Changsha, ChinaTianjin Institute of Surveying and Mapping Co., Ltd., Tianjin, ChinaThe Second Surveying and Mapping Institute of Hunan Province, Changsha, ChinaThe Second Surveying and Mapping Institute of Hunan Province, Changsha, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha, ChinaAI TCM Lab Hunan, Changsha, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha, ChinaAI TCM Lab Hunan, Changsha, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha, ChinaAI TCM Lab Hunan, Changsha, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha, ChinaAI TCM Lab Hunan, Changsha, ChinaIntroductionThe Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.MethodsThis paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data. To tackle the high cost and challenging data processing issues associated with traditional hyper-spectral/multi-spectral sensors, this method only relies on UAV visible light RGB bands. The process first involves calculating and normalizing 24 visible light vegetation indices from the UAV RGB images of the monitoring area, along with the original RGB bands. To account for the collinearity relationship between indices, the random forest variable importance and correlation coefficient iterative analysis algorithm are employed to select indices, retaining the most important or lowest collinearity multiple vegetation indices. Subsequently, the Beluga Whale Optimization (BWO) algorithm is utilized to generate a new vegetation index, which is then combined with the multi-threshold segmentation method to propose a BWO-weighted ensemble strategy for obtaining the final pests and diseases detection results.Results and discussionThe experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees.https://www.frontiersin.org/articles/10.3389/fpls.2024.1464723/fullpests and diseases monitoringBeluga Whale Optimization algorithmBWO-based vegetation indexBWO-based ensemble strategyunmanned aerial vehicle |
| spellingShingle | Keliang Hu Keliang Hu Junchen Liu Hai Xiao Qiangguo Zeng Jun Liu Jun Liu Lei Zhang Lei Zhang Man Li Man Li Zhihui Wang Zhihui Wang A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle Frontiers in Plant Science pests and diseases monitoring Beluga Whale Optimization algorithm BWO-based vegetation index BWO-based ensemble strategy unmanned aerial vehicle |
| title | A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle |
| title_full | A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle |
| title_fullStr | A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle |
| title_full_unstemmed | A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle |
| title_short | A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle |
| title_sort | new bwo based rgb vegetation index and ensemble learning strategy for the pests and diseases monitoring of ccb trees using unmanned aerial vehicle |
| topic | pests and diseases monitoring Beluga Whale Optimization algorithm BWO-based vegetation index BWO-based ensemble strategy unmanned aerial vehicle |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1464723/full |
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