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Publication
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Title of Article |
Sugarcane canopy detection using high spatial resolution UAS images and digital surface model |
Date of Acceptance |
9 July 2019 |
Journal |
Title of Journal |
Engineering and Applied Science Research (EASR) Faculty of Engineering, Khon Kaen University |
Standard |
SCOPUS |
Institute of Journal |
Engineering and Applied Science Research (EASR) Faculty of Engineering, Khon Kaen University |
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 |
The use of UAS equipped with multispectral cameras is a potential approach to acquire canopy reflectance to make various correlations with the desired crop parameters. However, the acquired reflectance data are mixed with unwanted data, such as reflectance from soil, which significantly affects some commonly used vegetation indices, such as NDVI. This study compares the performances of three methods for detecting the canopy area of 3-month-old sugarcane crops. These methods extract the canopy areas using 5 NDVI thresholds (0.2, 0.3, 0.4, 0.5, and 0.6), a principal component analysis (PCA) threshold, and a digital surface model (DSM) threshold. The performance assessment will deliberately consider the quality percentage (QP) of each method to correctly detect the canopy area of short sugarcane crops in 10 selected images. The results show that filtration by the PCA threshold method provides the best result with a QP of 65.89-78.72%. The NDVI threshold method at the levels of 0.3 and 0.4 follow with QPs of 58.42-68.81% and 40.80-70.81%, respectively, and the lowest accuracy is obtained by the DSM threshold method, which has QPs of 14.80-30.78%. |
Keyword |
Canopy detection, Unmanned Aerial System (UAS), Digital surface model, Principal component analysis, Normalized vegetation index, Multispectral image. |
Author |
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Reviewing Status |
มีผู้ประเมินอิสระ |
Status |
ได้รับการตอบรับให้ตีพิมพ์ |
Level of Publication |
นานาชาติ |
citation |
true |
Part of thesis |
true |
Attach file |
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Citation |
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