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 dis...
Published in: | 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings |
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2-s2.0-85153767821 Aziz N.H.; Narashid R.H.; Razak T.R.; Anshah S.A.; Talib N.; Latif Z.A.; Hashim N.; Zainuddin K. Detection of Bacterial Leaf Blight Disease Using RGB-Based Vegetation Indices and Fuzzy Logic 2023 2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings 10.1109/CSPA57446.2023.10087429 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153767821&doi=10.1109%2fCSPA57446.2023.10087429&partnerID=40&md5=b46125e1c6b9b2a33131c9b65412f01e 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. © 2023 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
author |
Aziz N.H.; Narashid R.H.; Razak T.R.; Anshah S.A.; Talib N.; Latif Z.A.; Hashim N.; Zainuddin K. |
spellingShingle |
Aziz N.H.; Narashid R.H.; Razak T.R.; Anshah S.A.; Talib N.; Latif Z.A.; Hashim N.; Zainuddin K. Detection of Bacterial Leaf Blight Disease Using RGB-Based Vegetation Indices and Fuzzy Logic |
author_facet |
Aziz N.H.; Narashid R.H.; Razak T.R.; Anshah S.A.; Talib N.; Latif Z.A.; Hashim N.; Zainuddin K. |
author_sort |
Aziz N.H.; Narashid R.H.; Razak T.R.; Anshah S.A.; Talib N.; Latif Z.A.; Hashim N.; Zainuddin K. |
title |
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_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_sort |
Detection of Bacterial Leaf Blight Disease Using RGB-Based Vegetation Indices and Fuzzy Logic |
publishDate |
2023 |
container_title |
2023 19th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2023 - Conference Proceedings |
container_volume |
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container_issue |
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doi_str_mv |
10.1109/CSPA57446.2023.10087429 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153767821&doi=10.1109%2fCSPA57446.2023.10087429&partnerID=40&md5=b46125e1c6b9b2a33131c9b65412f01e |
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. © 2023 IEEE. |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
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English |
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Conference paper |
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scopus |
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Scopus |
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1809677591763746816 |