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首页> 外文期刊>Journal of Nematology >Classi?cation of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps
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Classi?cation of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps

机译：利用自组织图谱上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 (re?ectances) 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 re?ectances were classi?ed into three classes based on R.renifomis populationnumberspresentin plant’s rhizosphere.Hyperspectral data(350-2500nm)werealsosub-dividedintoVisible,RedEdge+ 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 classi?ed using a supervised-SOM classi?cation method.Our results suggest that the overall classi?cation 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 classi?cation accuracies in the Mid-IR region were comparable to the accuracies obtained in other sub-regions.Finally, based on our ?ndings, the use of remotely-sensed hyperspectral data with SOM could prove to be extremely time ef?cient in detecting nematode numbers present in the soil.
机译：轮虫（Roylylulus reniformis）是主要的线虫害虫之一，能够使棉花单产降低60％以上，造成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。使用具有自组织图（SOM）的，受不同线虫种群数量影响的棉花植物的遥感高光谱数据（反射），对植物根际中现有的线虫种群数量进行关联和分类。根据植物根际中的R.renifomis种群数量分为三类。还将高光谱数据（350-2500nm）细分为可见，RedEdge +近红外（NIR）和中红外区域，以确定对线虫种群数量进行光谱分类最有效的子区域。将不同特征提取和降维方法的组合应用于dif区域提取特征集，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，大多数区域中大多数方法（可见区域除外）的总体分类精度）的变化范围从60％到80％，表明植物根际中存在的线虫数量与相应植物的高光谱特征呈正相关。结果表明，中红外区的分类准确度可与最后，根据我们的发现，将遥感高光谱数据与SOM结合使用可能被证明在检测土壤中存在的线虫数量上非常省时。

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