Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics
This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objec...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003708 |
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| author | Arif K Wijayanto Lilik B Prasetyo Sahid Agustian Hudjimartsu Gunardi Sigit Chiharu Hongo |
| author_facet | Arif K Wijayanto Lilik B Prasetyo Sahid Agustian Hudjimartsu Gunardi Sigit Chiharu Hongo |
| author_sort | Arif K Wijayanto |
| collection | DOAJ |
| description | This study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance. |
| format | Article |
| id | doaj-art-e4dd39b94e7d491fbb0f318e239b980e |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-e4dd39b94e7d491fbb0f318e239b980e2025-08-20T02:52:23ZengElsevierSmart Agricultural Technology2772-37552025-03-011010076610.1016/j.atech.2024.100766Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metricsArif K Wijayanto0Lilik B Prasetyo1Sahid Agustian Hudjimartsu2Gunardi Sigit3Chiharu Hongo4Department of Forest Resource Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor 16680, Indonesia; Japan Society for Promotion of Science (JSPS) Ronpaku Fellow, Tokyo 102-0083, Japan; Environmental Research Center, IPB University, Bogor 16680, Indonesia; Corresponding author.Department of Forest Resource Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor 16680, IndonesiaStudy Program of Information Technology, Faculty of Science and Engineering, Ibn Khaldun University, Bogor 16129, Indonesia; Department Agriculture, Forestry, and Bioresources, Seoul National University, Seoul 08826, Republic of KoreaRegional Office of Food Crops Service West Java Province, Cianjur 43283, IndonesiaCenter for Environmental Remote Sensing (CEReS), Chiba University, Chiba 263-8522, JapanThis study introduces an innovative method for assessing bacterial leaf blight (BLB) in paddy fields using multispectral UAV (Unmanned Aerial Vehicle) data and patch fragmentation analysis. Unlike traditional pixel-based approaches, which often lack spatial context, our method treats pixels as objects and evaluates their spatial relationships to determine BLB severity. Seven patch fragmentation metrics were derived from binarized vegetation indices to quantify BLB damage scores, carefully selected for their ability to describe the spatial arrangement and connectivity of potentially affected patches. This metric-driven approach captures the scale and intensity of BLB damage, facilitating precise assessment. The method demonstrated high accuracy, achieving an AUC of 0.938 with a 0.5-meter sampling window. This advancement enhances the precision of BLB damage assessment, particularly for applications such as crop insurance.http://www.sciencedirect.com/science/article/pii/S2772375524003708Crop diseaseDroneFragmented patchesLandscapemetricsLandscape-scale analysisPest management |
| spellingShingle | Arif K Wijayanto Lilik B Prasetyo Sahid Agustian Hudjimartsu Gunardi Sigit Chiharu Hongo Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics Smart Agricultural Technology Crop disease Drone Fragmented patches Landscapemetrics Landscape-scale analysis Pest management |
| title | Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics |
| title_full | Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics |
| title_fullStr | Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics |
| title_full_unstemmed | Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics |
| title_short | Advanced BLB disease assessment in paddy fields using multispectral UAV data and patch fragmentation metrics |
| title_sort | advanced blb disease assessment in paddy fields using multispectral uav data and patch fragmentation metrics |
| topic | Crop disease Drone Fragmented patches Landscapemetrics Landscape-scale analysis Pest management |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003708 |
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