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.

现场光谱学是一种准确、快速、无损的农业植物特性监测技术。其中，葡萄品种的鉴定是葡萄栽培和葡萄酒工业中最重要的因素之一。本研究评估了现场高光谱数据和统计技术对伊朗西部五种常见葡萄品种的辨别能力。在叶子和冠层层面总共采集了 3000 个光谱样本。然后，为了确定最佳方法，应用了两种类型的高光谱数据（波长从 350 至 2500 nm 和 32 个光谱指数）、两种数据缩减方法（PLSR 和 ANOVA-PCA）以及两种分类算法（LDA 和 SVM）总共16个场景。结果表明，在测试集中，葡萄品种的总体准确率为 89.88%–100%。在数据缩减方法中，ANOVA 和 PCA 的组合比 PLSR 产生了更高的性能。因此，在叶子和冠层水平上，区分所研究的葡萄品种的最佳波长分别位于 695、752、1148、1606 nm 和 582、687、1154、1927 nm 附近。叶和冠层的最佳光谱指数分别为 R680、WI、SGB 和 RATIO975_2、DattA、绿度。此外，光谱区域在区分所研究的葡萄品种方面的重要性被列为近红外 > 中红外和红边区域 > 可见光。作为一般结论，冠层光谱指数-ANOVA-PCA-SVM 方案能够最准确地区分所研究的物种。