Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic
Paddy planting becomes the primary source of income and livelihood for paddy farmers, especially small-scale farmers and landless laborers. Unfortunately, rice production has been threatened by paddy disease. The bacteria leaf blight disease (BLB) is one of Malaysia’s most significant paddy diseases...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
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IEEE
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37737/ |
| _version_ | 1848848688057679872 |
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| author | Aziz, Nor Hafiza Haron Narashid, Rohayu Razak, Tajul Rosli Anshah, Siti Aminah Talib, Noorfatekah Abd Latif, Zulkiflee Hashim, Norhashila Zainuddin, Khairulazhar |
| author_facet | Aziz, Nor Hafiza Haron Narashid, Rohayu Razak, Tajul Rosli Anshah, Siti Aminah Talib, Noorfatekah Abd Latif, Zulkiflee Hashim, Norhashila Zainuddin, Khairulazhar |
| author_sort | Aziz, Nor Hafiza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Paddy planting becomes the primary source of income and livelihood for paddy farmers, especially small-scale farmers and landless laborers. Unfortunately, rice production has been threatened by paddy disease. The bacteria leaf blight disease (BLB) is one of Malaysia’s most significant paddy diseases, causing substantial harm to rice production. This study aims to determine the bacteria leaf blight (BLB) disease from the utilized techniques of RGB-Based Vegetation Indices and Fuzzy Logic on the Unmanned Aerial Vehicle (UAV) images during the first paddy season 2022 in Perlis. In this study, the RGB-based indices of Normalized Green Red Different Index (NGRDI) and Green Leaf Index (GLI) were applied to the UAV Images captured at 20m altitudes. Then the fuzzy logic classification technique was applied to identify the BLB disease severity which consists of healthy and infected paddy leaves with the acceptable accuracy of 90.16%. Based on the classified BLB severeness with fuzzy logic, the result shows that the NGRDI was more significant to identify paddy disease in the area. In contrast, the GLI index is more significant to identify the non-paddy area. The NGRDI and GLI index ranges for BLB were found between -0.054 to 0.092 and 0.005 to 0.222. For more improvement of the study, the multispectral UAV Image should be applied to increase the accuracy of paddy disease detection like BLB and the images will also be taken and verified in other paddy plots with the aid of a spectroradiometer. |
| first_indexed | 2025-11-15T09:38:28Z |
| format | Conference or Workshop Item |
| id | upm-37737 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:38:28Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-377372023-09-28T06:14:44Z http://psasir.upm.edu.my/id/eprint/37737/ Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic Aziz, Nor Hafiza Haron Narashid, Rohayu Razak, Tajul Rosli Anshah, Siti Aminah Talib, Noorfatekah Abd Latif, Zulkiflee Hashim, Norhashila Zainuddin, Khairulazhar Paddy planting becomes the primary source of income and livelihood for paddy farmers, especially small-scale farmers and landless laborers. Unfortunately, rice production has been threatened by paddy disease. The bacteria leaf blight disease (BLB) is one of Malaysia’s most significant paddy diseases, causing substantial harm to rice production. This study aims to determine the bacteria leaf blight (BLB) disease from the utilized techniques of RGB-Based Vegetation Indices and Fuzzy Logic on the Unmanned Aerial Vehicle (UAV) images during the first paddy season 2022 in Perlis. In this study, the RGB-based indices of Normalized Green Red Different Index (NGRDI) and Green Leaf Index (GLI) were applied to the UAV Images captured at 20m altitudes. Then the fuzzy logic classification technique was applied to identify the BLB disease severity which consists of healthy and infected paddy leaves with the acceptable accuracy of 90.16%. Based on the classified BLB severeness with fuzzy logic, the result shows that the NGRDI was more significant to identify paddy disease in the area. In contrast, the GLI index is more significant to identify the non-paddy area. The NGRDI and GLI index ranges for BLB were found between -0.054 to 0.092 and 0.005 to 0.222. For more improvement of the study, the multispectral UAV Image should be applied to increase the accuracy of paddy disease detection like BLB and the images will also be taken and verified in other paddy plots with the aid of a spectroradiometer. IEEE 2023 Conference or Workshop Item PeerReviewed Aziz, Nor Hafiza and Haron Narashid, Rohayu and Razak, Tajul Rosli and Anshah, Siti Aminah and Talib, Noorfatekah and Abd Latif, Zulkiflee and Hashim, Norhashila and Zainuddin, Khairulazhar (2023) Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic. In: 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), 3-4 Mar. 2023, Langkawi, Kedah, Malaysia. (pp. 134-139). https://ieeexplore.ieee.org/document/10087429 10.1109/CSPA57446.2023.10087429 |
| spellingShingle | Aziz, Nor Hafiza Haron Narashid, Rohayu Razak, Tajul Rosli Anshah, Siti Aminah Talib, Noorfatekah Abd Latif, Zulkiflee Hashim, Norhashila Zainuddin, Khairulazhar Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title | Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title_full | Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title_fullStr | Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title_full_unstemmed | Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title_short | Detection of bacterial leaf blight disease using RGB-based vegetation indices and fuzzy logic |
| title_sort | detection of bacterial leaf blight disease using rgb-based vegetation indices and fuzzy logic |
| url | http://psasir.upm.edu.my/id/eprint/37737/ http://psasir.upm.edu.my/id/eprint/37737/ http://psasir.upm.edu.my/id/eprint/37737/ |