利用高光谱成像技术检测葡萄叶片红斑病

M. Mehrubeoglu, Keith Orlebeck, Michael Zemlan, Wesley Autran
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引用次数: 8

摘要

红斑病是一种影响葡萄藤的病毒性疾病。症状表现为葡萄叶子上不规则的斑点,叶子底部有粉红色和红色的静脉。红斑病导致葡萄中糖的积累减少,影响葡萄的品质并导致收获延迟。早期发现和监测这种疾病对葡萄藤的管理很重要。这项工作的重点是利用高光谱成像检测和绘制葡萄叶片的红斑病。利用便携式高光谱成像系统对已知红斑病的葡萄叶片在藤上和藤下进行了成像,以研究红斑病的光谱特征,并确定叶片上的患病区域。选择566 nm(绿色)和628 nm(红色)对应光谱波段的修正反射率,以及566 nm / 628 nm、680 nm / 738 nm两组光谱波段的修正反射率比作为区分红斑、健康叶和干叶的有效特征。然后使用这两个修正反射率和两个修正反射率值的比值在监督学习方案中训练支持向量机分类器。定义SVM分类器后,对葡萄叶片高光谱图像进行两类分类。本文介绍了利用高光谱成像技术对葡萄叶片红斑病进行鉴定及病害不同阶段的制图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting red blotch disease in grape leaves using hyperspectral imaging
Red blotch disease is a viral disease that affects grapevines. Symptoms appear as irregular blotches on grape leaves with pink and red veins on the underside of the leaves. Red blotch disease causes a reduction in the accumulation of sugar in grapevines affecting the quality of grapes and resulting in delayed harvest. Detecting and monitoring this disease early is important for grapevine management. This work focuses on the use of hyperspectral imaging for detection and mapping red blotch disease in grape leaves. Grape leaves with known red blotch disease have been imaged with a portable hyperspectral imaging system both on and off the vine to investigate the spectral signature of red blotch disease as well as to identify the diseased areas on the leaves. Modified reflectance calculated at spectral bands corresponding to 566 nm (green) and 628 nm (red), and modified reflectance ratios computed at two sets of bands (566 nm / 628 nm, 680 nm / 738 nm) were selected as effective features to differentiate red blotch from healthy-looking and dry leaf. These two modified reflectance and two ratios of modified reflectance values were then used to train the support vector machine classifier in a supervised learning scheme. Once the SVM classifier was defined, two-class classification was achieved for grape leaf hyperspectral images. Identification of the red blotch disease on grape leaves as well as mapping different stages of the disease using hyperspectral imaging are presented in this paper.
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