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Publication
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| Title of Article |
Use of vegetation indices in monitoring sugarcane white leaf disease symptoms in sugarcane field using multispectral UAV aerial imagery |
| Date of Acceptance |
3 June 2019 |
| Journal |
| Title of Journal |
IOP Conf. Series: Earth and Environmental Science |
| Standard |
SCOPUS |
| Institute of Journal |
IOP Conference Series: Earth and Environmental Science |
| ISBN/ISSN |
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| Volume |
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| Issue |
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| Month |
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| Year of Publication |
2019 |
| Page |
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| Abstract |
Currently, controlling or identifying of the sugarcane white leaf disease infection is not possible since its symptom need to be observed by human walking throughout fields.
Therefore, this research aims to study the ability of vegetation indices to detect white leaf
disease infected sugarcane with images taken by multispectral camera mounted on Unmanned
Aerial Vehicle. Three sub-images infected with white leaf symptom and 3 other sub-images of normal green leaf were selected for this study. The reflectance values of 6 chosen sub-images
were used to calculate 18 vegetation indices, and then these indices were used to compute the
difference percentage of vegetation (green versus white) in order to find vegetation indices that
are the most sensitive to white leaf symptoms. The results show that vegetation indices that
have NIR and Red edge band in their formula (14 vegetation indices) have difference
percentage in the range of 14.66 - 45.10, with NDREI, GNDVI yielding the highest difference
percentage (44.05 - 45.10%), and vegetation indices that have only visible bands in their
formula (4 vegetation indices) have the difference percentage from 14.96 – 26.04%, with GI
and NRI resulting in the highest difference percentage 24.04% and 26.04%, respectively. |
| Keyword |
vegetation indices, white leaf disease, sugarcane and UAV |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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