Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image
Basal Stem Rot (BSR) disease is one of the most destructive diseases affecting oil palm plantation in Malaysia. The first critical step for a successful control of BSR is its detection and diagnosis. This study presents a new approach of high spatial resolution aerial Red, Green, Blue (RGB) image fo...
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Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia
2018
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Online Access: | http://psasir.upm.edu.my/id/eprint/53274/1/Methods%20of%20reducing%20the%20fate%20and%20transport%20of%20nutrients%20from%20agricultural%20fields.pdf http://psasir.upm.edu.my/id/eprint/53274/7/Basal%20Stem%20Rot%20%28BSR%29%20detection%20using%20iextural%20inalysis%20of%20Unmanned%20Aerial%20Vehicle%20%28UAV%29%20image.pdf http://psasir.upm.edu.my/id/eprint/53274/ http://eproceedings.chemistry.utm.my/index.php/FYP/index |
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my.upm.eprints.532742019-05-09T05:56:26Z http://psasir.upm.edu.my/id/eprint/53274/ Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image Bejo, Siti Khairunniza Jaleni, M. Husin, M. E. Khosrokhani, Maryam Muharam, Farrah Melissa Abu Seman, Idris Anuar, Mohamad Izzuddin Basal Stem Rot (BSR) disease is one of the most destructive diseases affecting oil palm plantation in Malaysia. The first critical step for a successful control of BSR is its detection and diagnosis. This study presents a new approach of high spatial resolution aerial Red, Green, Blue (RGB) image for BSR detection using a low-altitude remote sensing Unmanned Aerial Vehicle (UAV) platform. The co-occurrence measures for textural analysis were performed to the RGB band to determine the best parameter for BSR detection. Descriptive statistical as well as One way ANOVA were executed to indicate which bands are giving significant level (p<0.05). Apparently, the test gave three significant properties which are Correlation taken from R, G and B band. The developed conditional statement of detection was tested for distinguishing between healthy and BSR-infected trees. Total accuracy acquired for healthy trees was 86.21% whereas for BSR-infected trees was 75.00%. Hence, average accuracy assessment was 80.61%. Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia 2018-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/53274/1/Methods%20of%20reducing%20the%20fate%20and%20transport%20of%20nutrients%20from%20agricultural%20fields.pdf text en http://psasir.upm.edu.my/id/eprint/53274/7/Basal%20Stem%20Rot%20%28BSR%29%20detection%20using%20iextural%20inalysis%20of%20Unmanned%20Aerial%20Vehicle%20%28UAV%29%20image.pdf Bejo, Siti Khairunniza and Jaleni, M. and Husin, M. E. and Khosrokhani, Maryam and Muharam, Farrah Melissa and Abu Seman, Idris and Anuar, Mohamad Izzuddin (2018) Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image. eProceedings Chemistry, 3 (1). pp. 1186-1197. ISSN 2550-1453 http://eproceedings.chemistry.utm.my/index.php/FYP/index |
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Basal Stem Rot (BSR) disease is one of the most destructive diseases affecting oil palm plantation in Malaysia. The first critical step for a successful control of BSR is its detection and diagnosis. This study presents a new approach of high spatial resolution aerial Red, Green, Blue (RGB) image for BSR detection using a low-altitude remote sensing Unmanned Aerial Vehicle (UAV) platform. The co-occurrence measures for textural analysis were performed to the RGB band to determine the best parameter for BSR detection. Descriptive statistical as well as One way ANOVA were executed to indicate which bands are giving significant level (p<0.05). Apparently, the test gave three significant properties which are Correlation taken from R, G and B band. The developed conditional statement of detection was tested for distinguishing between healthy and BSR-infected trees. Total accuracy acquired for healthy trees was 86.21% whereas for BSR-infected trees was 75.00%. Hence, average accuracy assessment was 80.61%. |
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Article |
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Bejo, Siti Khairunniza Jaleni, M. Husin, M. E. Khosrokhani, Maryam Muharam, Farrah Melissa Abu Seman, Idris Anuar, Mohamad Izzuddin |
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Bejo, Siti Khairunniza Jaleni, M. Husin, M. E. Khosrokhani, Maryam Muharam, Farrah Melissa Abu Seman, Idris Anuar, Mohamad Izzuddin Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
author_facet |
Bejo, Siti Khairunniza Jaleni, M. Husin, M. E. Khosrokhani, Maryam Muharam, Farrah Melissa Abu Seman, Idris Anuar, Mohamad Izzuddin |
author_sort |
Bejo, Siti Khairunniza |
title |
Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
title_short |
Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
title_full |
Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
title_fullStr |
Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
title_full_unstemmed |
Basal Stem Rot (BSR) detection using iextural inalysis of Unmanned Aerial Vehicle (UAV) image |
title_sort |
basal stem rot (bsr) detection using iextural inalysis of unmanned aerial vehicle (uav) image |
publisher |
Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia |
publishDate |
2018 |
url |
http://psasir.upm.edu.my/id/eprint/53274/1/Methods%20of%20reducing%20the%20fate%20and%20transport%20of%20nutrients%20from%20agricultural%20fields.pdf http://psasir.upm.edu.my/id/eprint/53274/7/Basal%20Stem%20Rot%20%28BSR%29%20detection%20using%20iextural%20inalysis%20of%20Unmanned%20Aerial%20Vehicle%20%28UAV%29%20image.pdf http://psasir.upm.edu.my/id/eprint/53274/ http://eproceedings.chemistry.utm.my/index.php/FYP/index |
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1643835359105122304 |
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13.18916 |