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Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 145-152页

作者： Wenjiang Huang Junjing Lu Huichun Ye Weiping Kong A.Hugh Mortimer Yue Shi State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Institute of Geographical Science Hebei Academy of SciencesShijiazhuang 050021China Hainan Provincial Key Laboratory of Earth Observation Sanya 572029HainanChina Rutherford Appleton Laboratory Harwell Science and Innovations CampusOxfordshire OX110QXUK

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer *** winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in *** mildew(Blumeria graminis)and stripe rust(Puccinia striiformis ***)are two of the most prevalent winter wheat diseases in *** study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral *** spectral normalization process was applied prior to the *** t-tests were used to determine the sensitivity of the spectral bands and vegetation *** 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 *** 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 *** 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 *** analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for *** 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 *** detection of the severity of stripe rust using

关键词： winter wheat crop disease powdery mildew stripe rust nitrogen-water stress continuous wavelet analysis quantitative identification

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