Detection of Flavescence doree Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
Detection of Flavescence doree Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
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DOI：
10.3390/rs9040308
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发表时间：
2017-04-01
期刊：
影响因子：
5
通讯作者：
Dedieu, Gerard
中科院分区：
文献类型：
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作者：
Albetis, Johanna;Duthoit, Sylvie;Dedieu, Gerard
多里黄化病是一种影响欧洲葡萄园的葡萄病害，具有严重的经济后果，因此遏制其传播被认为是葡萄栽培的主要挑战。Flavescence葡萄藤受到强制性虫害控制，包括清除受感染的葡萄藤，在这种情况下，通过无人驾驶航空器（UAV）遥感自动检测Flavescence葡萄藤的症状可能构成种植者的关键诊断工具。本文的目的是评估的可行性，区分黄化症状的红色和白色品种健康的葡萄植被利用无人机多光谱图像。2015年9月，在法国西南部的四个选定的葡萄园获得了详尽的地面实况数据和无人机多光谱图像（可见光和近红外域）。健康和有症状的植物的光谱特征进行了研究与一组20个变量计算的无人机图像（光谱波段，植被指数和生物物理参数），使用单变量和多变量分类方法。最好的结果，实现了红色品种（无论是使用单变量和多变量的方法）。对于白色品种，无论是单变量还是多变量的结果都不令人满意。尽管如此，外部精度评估表明，尽管存在Flavescence图像和健康像素误分类的问题，但仍然可以提出使用基于无人机的图像的操作Flavescence图像映射技术。
Flavescence doree is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dore is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dore symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dore symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dore and healthy pixel misclassification, an operational Flavescence dore mapping technique using UAV-based imagery can still be proposed.