Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis
Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis
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DOI：
10.25165/j.ijabe.20181102.3467
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发表时间：
2018-03-01
影响因子：
2.4
通讯作者：
Shi, Yue
中科院分区：
文献类型：
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作者：
Huang, Wenjiang;Lu, Junjing;Shi, Yue
因此，有必要对不同病害和氮水胁迫进行定量识别，以指导专用杀菌剂的喷洒和施肥。因此，冬小麦病害和氮水胁迫是造成我国冬小麦产量损失的常见原因。白粉病（Blumeria graminis）和条锈病（Puccinia striiformis f.小麦赤霉病是我国冬小麦最常见的两种病害。本研究探讨了连续小波分析在利用冠层高光谱数据识别白粉病、条锈病和氮水分胁迫中的应用潜力。在分析之前应用光谱归一化过程。独立的t检验被用来确定光谱波段和植被指数的敏感性。为了减少小波区域的数量，相关性分析和独立的t检验相结合，以选择最重要的功能。在选取光谱波段、植被指数和小波特征的基础上，利用Fisher线性判别分析（FLDA）和支持向量机（SVM）建立了遥感影像的判别模型。结果表明，小波特征对不同胁迫类型的分类效果上级光谱波段和植被指数，FLDA对白粉病、条锈病和氮水胁迫的分类精度分别为0.91、0.72和0.72，SVM对白粉病、条锈病和氮水胁迫的分类精度分别为0.79、0.67和0.65。FLDA对白粉病、条锈病和氮水胁迫的判别准确率分别为78.1%、95.6%和95.7%。进一步的分析进行，从而小波特征，然后被分成高尺度和低尺度的特征子集进行识别。高尺度和低尺度特征的总体准确度（OA）分别为0.61和0.73的准确度低于所有小波特征的OA为0.88。用该方法检测小麦条锈病的严重度，其可靠性提高（R-2 = 0.828）。
It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications. The winter wheat diseases, in combination with nitrogen-water stress, are therefore common causes of yield loss in winter wheat in China. Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent winter wheat diseases in China. This study investigated the potential of continuous wavelet analysis to identify the powdery mildew, stripe rust and nitrogen-water stress using canopy hyperspectral data. The spectral normalization process was applied prior to the analysis. Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index. In order to reduce the number of wavelet regions, correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance. Based on the selected spectral bands, vegetation indices and wavelet features, the discriminate models were established using Fisher's linear discrimination analysis (FLDA) and support vector machine (SVM). The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses, with overall accuracies of 0.91, 0.72, and 0.72 respectively for powdery mildew, stripe rust and nitrogen-water by using FLDA, and 0.79, 0.67 and 0.65 respectively by using SVM. FLDA was more suitable for differentiating stresses in winter wheat, with respective accuracies of 78.1%, 95.6% and 95.7% for powdery mildew, stripe rust, and nitrogen-water stress. Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification. The accuracies of high-scale and low-scale features with an overall accuracy (OA) of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88. The detection of the severity of stripe rust using this method showed an enhanced reliability (R-2 = 0.828).