Identification of combined vegetation indices for the early detection of plant diseases

机译：识别组合植被指数以及早发现植物病害

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The aim of this research is the early detection of plant diseases based on the combination of vegetation indices. We have seen that an individual index such as the most popular one, namely NDVI, does not discriminate adequately between healthy and diseased plants, e.g. Cercospora beticola, Erysiphe betae, and Uromyces betae. However, by combining vegetation indices, which are usually called features in classification, very reliable results can be achieved. We use Support Vector Machines for classification. By this we receive a classification accuracy of almost 95% for Cercospora beticola and Uromyces betae and still over 92% for Erysiphe betae. Depending on the different plant diseases we have found that different vegetation indices are important, too. Consequently, the question how to find the best index for every plant disease and the choice of the best subset arise. Both questions are not the same, because different indices contain similar information which can already be seen from the formula of the calculation of the vegetation index. These dependencies do not have to be linear. In order to identify optimal subsets of features for the different pathogens already at an early stage of infestation, we have found that entropy and mutual information are adequate concepts. Accordingly we use the minimum redundancy - maximum relevance (mRMR) criterion to evaluate the features. We have found that we need different indices and feature subsets of different sizes for different diseases.

机译：这项研究的目的是基于植被指数的组合来及早发现植物病害。我们已经看到，诸如最流行的指数，即NDVI，这样的单个指数不能充分区分健康植物和患病植物，例如植物。 Cercospora beticola，Erysiphe betae和Uromyces betae。但是，通过组合通常在分类中称为特征的植被指数，可以获得非常可靠的结果。我们使用支持向量机进行分类。这样，我们就可以将锥孔孢子虫和Uromyces betae的分类准确率提高到95％，而Erysiphe betae的分类准确率仍然超过92％。根据不同的植物病害，我们发现不同的植被指数也很重要。因此，出现了如何为每种植物病害找到最佳指标以及如何选择最佳子集的问题。这两个问题并不相同，因为不同的指标包含相似的信息，这可以从植被指数的计算公式中看出。这些依赖性不必是线性的。为了在侵染早期就为不同病原体识别特征的最佳子集，我们发现熵和互信息是适当的概念。因此，我们使用最小冗余-最大相关性（mRMR）准则来评估功能。我们发现，对于不同的疾病，我们需要不同的索引和不同大小的特征子集。

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《Remote sensing for agriculture, ecosystems, and hydrology XI》|2009年|P.747217.1-747217.10|共10页
会议地点 Berlin(DE)
作者
T. Rumpf; rnA. Mahlein; rnD. Doerschlag; rnL. Pluemer;
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作者单位

Dep. of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Germany;

rnInstitute of Crop Science and Resource Conservation, (INRES) - Phytomedicine, University of Bonn, Germany;

rnDep. of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Germany;

rnDep. of Geoinformation, Institute of Geodesy and Geoinformation, University of Bonn, Germany;

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原文格式 PDF
正文语种 eng
中图分类环境遥感;
关键词
plant diseases; feature selection; correlation; entropy; mutual information; minimum redundancy - maximum relevance criterion; combination of vegetation indices; classification; support vector machines;

机译：植物病害；特征选择；相关性熵;相互信息；最小冗余-最大相关性标准；植被指数组合；分类;支持向量机;

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5. La teledetection et les indices de vegetation pour la detection de la vegetation eparse et moyennement dense: Cas de l'environnement urbain [D] . Bannari, Abderrazak. 1996

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Identification of combined vegetation indices for the early detection of plant diseases

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