New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale
Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in t...
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MDPI AG
2024-12-01
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| author | Qiong Zheng Yihao Chen Qing Xia Yunfei Zhang Dan Li Hao Jiang Chongyang Wang Longlong Zhao Wenjiang Huang Yingying Dong Chuntao Wang |
| author_facet | Qiong Zheng Yihao Chen Qing Xia Yunfei Zhang Dan Li Hao Jiang Chongyang Wang Longlong Zhao Wenjiang Huang Yingying Dong Chuntao Wang |
| author_sort | Qiong Zheng |
| collection | DOAJ |
| description | Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI<sub>RB</sub>) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI<sub>RB</sub> was designed. GRVI<sub>RB</sub> demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI<sub>RB</sub> has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales. |
| format | Article |
| id | doaj-art-476a93034cb04cf9ac65ebcbd24e8581 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-476a93034cb04cf9ac65ebcbd24e85812025-08-20T02:01:21ZengMDPI AGRemote Sensing2072-42922024-12-011624468110.3390/rs16244681New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy ScaleQiong Zheng0Yihao Chen1Qing Xia2Yunfei Zhang3Dan Li4Hao Jiang5Chongyang Wang6Longlong Zhao7Wenjiang Huang8Yingying Dong9Chuntao Wang10Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, ChinaEngineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, ChinaEngineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science & Technology, Changsha 410114, ChinaDepartment of Geomatics Engineering, School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510642, ChinaRice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVI<sub>RB</sub>) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVI<sub>RB</sub> was designed. GRVI<sub>RB</sub> demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVI<sub>RB</sub> has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales.https://www.mdpi.com/2072-4292/16/24/4681hyperspectralmonitoringrice blastthe geometry ratio vegetation index (GRVI<sub>RB</sub>)different scales |
| spellingShingle | Qiong Zheng Yihao Chen Qing Xia Yunfei Zhang Dan Li Hao Jiang Chongyang Wang Longlong Zhao Wenjiang Huang Yingying Dong Chuntao Wang New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale Remote Sensing hyperspectral monitoring rice blast the geometry ratio vegetation index (GRVI<sub>RB</sub>) different scales |
| title | New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale |
| title_full | New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale |
| title_fullStr | New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale |
| title_full_unstemmed | New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale |
| title_short | New Hyperspectral Geometry Ratio Index for Monitoring Rice Blast Disease from Leaf Scale to Canopy Scale |
| title_sort | new hyperspectral geometry ratio index for monitoring rice blast disease from leaf scale to canopy scale |
| topic | hyperspectral monitoring rice blast the geometry ratio vegetation index (GRVI<sub>RB</sub>) different scales |
| url | https://www.mdpi.com/2072-4292/16/24/4681 |
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