气候变化领域集成服务门户(CCPortal): Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran

CCPortal

DOI	10.1016/j.jag.2019.04.002
	Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran
	Mirzaei M.; Marofi S.; Abbasi M.; Solgi E.; Karimi R.; Verrelst J.
发表日期	2019
ISSN	15698432
起始页码	26
结束页码	37
卷号	80
英文摘要	Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classification algorithms (LDA and SVM) were applied in a total of 16 scenarios. Results showed that the grapevine varieties were discriminated with overall accuracy of 89.88%–100% in test sets. Among the data reduction methods, the combination of ANOVA and PCA yielded higher performance as opposed to PLSR. Accordingly, optimal wavelengths in discrimination of studied grapevine varieties were located in vicinity of 695, 752, 1148, 1606 nm and 582, 687, 1154, 1927 nm at leaf and canopy levels, respectively. Optimal spectral indices were R680, WI, SGB and RATIO975_2, DattA, Greenness at leaf and canopy levels, respectively. Also, the importance of spectral regions in discriminating studied grapevine varieties was ranked as near-infrared > mid-infrared and red edge region > visible. As a general conclusion, the canopy-spectral indices-ANOVA-PCA-SVM scenario discriminated the studied species most accurately. © 2019
英文关键词	Field spectroradiometry; Grapevine varieties; Linear discriminant analysis; Optimal wavelength; Spectral indices; Support vector machine; Varieties discrimination
语种	英语
scopus关键词	accuracy assessment; algorithm; canopy; monitoring system; remote sensing; satellite imagery; spectral analysis; vineyard; viticulture; Iran; Vitis
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156450
作者单位	Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Iran; Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Iran; Faculty of Natural Resource and Environment, Malayer University, Iran; Faculty of Natural Resource and Earth Science, Shahrekord University, Iran; Green Space Design group, Faculty of Agriculture, Malayer University, Iran; Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València, 46980, Spain
推荐引用方式 GB/T 7714	Mirzaei M.,Marofi S.,Abbasi M.,et al. Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran[J],2019,80.
APA	Mirzaei M.,Marofi S.,Abbasi M.,Solgi E.,Karimi R.,&Verrelst J..(2019).Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran.International Journal of Applied Earth Observation and Geoinformation,80.
MLA	Mirzaei M.,et al."Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran".International Journal of Applied Earth Observation and Geoinformation 80(2019).