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首页> 外文期刊>Journal of Nematology, with Annual of Applied Nematology >Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps
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Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps

机译：基于自组织图的遥感高光谱数据对棉花轮虫的数量进行分类

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Rotylenchulus reniformis is one of the major nematode pests capable of reducing cotton yields by more than 60%, causing estimated losses that may exceed millions of dollars U.S. Therefore, early detection of nematode numbers is necessary to reduce these losses. This study investigates the feasibility of using remotely sensed hyperspectral data (reflectances) of cotton plants affected with different nematode population numbers with self-organizing maps (SOM) in correlating and classifying nematode population numbers extant in a plant's rhizosphere. The hyperspectral reflectances were classified into three classes based on R renifomis population numbers present in plant's rhizosphere. Hyperspectral data (350-2500 nm) were also sub-divided into Visible, Red Edge + Near Infrared (NIR) and Mid-IR region to determine the sub-region most effective in spectrally classifying the nematode population numbers. Various combinations of different feature extraction and dimensionality reduction methods were applied in different regions to extract reduced sets of features. These features were then classified using a supervised-SOM classification method. Our results suggest that the overall classification accuracies, in general, for most methods in most regions (except visible region) varied from 60% to 80%, thereby, indicating a positive correlation between the nematode numbers present in plant's rhizosphere and the corresponding plant's hyperspectral signatures. Results showed that classification accuracies inthe Mid-IR region were comparable to the accuracies obtained in other sub-regions. Finally, based on our findings, the use of remotely-sensed hyperspectral data with SOM could prove to be extremely time efficient in detecting nematode numbers present inthe soil.
机译：轮状线虫是能够将棉花单产降低60％以上的主要线虫害虫之一，造成的估计损失可能超过数百万美元。因此，必须尽早发现线虫数量以减少这些损失。这项研究调查了利用具有自组织图（SOM）的不同线虫种群数量影响的棉株的遥感高光谱数据（反射率）对植物根际中现存的线虫种群数量进行关联和分类的可行性。基于植物根际中的R renifomis种群数，高光谱反射率可分为三类。高光谱数据（350-2500 nm）也细分为可见，红色边缘+近红外（NIR）和中红外区域，以确定在对线虫种群数量进行光谱分类时最有效的子区域。在不同区域应用不同特征提取和降维方法的各种组合，以提取简化的特征集。然后使用监督SOM分类方法对这些特征进行分类。我们的结果表明，一般而言，大多数区域（可见区域除外）中大多数方法的总体分类准确度在60％至80％之间变化，从而表明植物根际中存在的线虫数量与相应植物的高光谱之间呈正相关签名。结果表明，中红外区的分类精度可与其他分区获得的分类精度相提并论。最后，根据我们的发现，将遥感高光谱数据与SOM结合使用可证明在检测土壤中存在的线虫数量上非常省时。

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