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Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using continuous wavelet analysis

Journal of Integrative Agriculture 2012年第9期11卷 1474-1484页

作者： ZHANG Jing-cheng YUAN Lin WANG Ji-hua HUANG Wen-jiang CHEN Li-ping ZHANGDong-yan Beijing Research Center for Information Technology in Agriculture Beijing 100097 R.P.China Institute of Agriculture Remote Sensing and Information System Application Zhejiang University Hangzhou 310029 R.P.China Center for Earth Observation and Digital Earth Chinese Academy of Sciences Beijing 100094 R.P.China

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR met

关键词： powdery mildew disease severity continuous wavelet analysis partial least square regression

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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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In-situ transient photovoltage study on interface electron transfer regulation of carbon dots/NiCo_(2)O_(4) photocatalyst for the enhanced overall water splitting activity

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Nano Research 2022年第3期15卷 1786-1795页

作者： Haodong Nie Yan Liu Yi Li Kaiqiang Wei Zhenyu Wu Hong Shi Hui Huang Yang Liu Mingwang Shao Zhenhui Kang Institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123China Macao Institute of Materials Science and Engineering Macao University of Science and TechnologyTaipa 999078Macao SARChina

Photocatalytic hydrogen production by overall water solar-splitting is a prospective strategy to solve energy ***,the rapid recombination of photogenerated electron–hole pairs deeply restricts photocatalytic activity of ***,the in-situ transient photovoltage(TPV)technique was developed to investigate the interfacial photogenerated carrier extraction,photogenerated carrier recombination and the interfacial electron delivery kinetics of the *** carbon dots/NiCo_(2)O_(4)(CDs/NiCo_(2)O_(4))composite shows weakened recombination rate of photogenerated carriers due to charge storage of CDs,which enhances the photocatalytic water decomposition activity without any *** can accelerate the interface electron extraction about 0.09 ms,while the maximum electron storage time by CDs is up to 0.7 *** optimal CDs/NiCo_(2)O_(4)composite(5 wt.%CDs)displays the hydrogen production of 62µmol·h^(−1)·g^(−1) and oxygen production of 29µmol·h^(−1)·g^(−1) at normal atmosphere,which is about 4 times greater than that of pristine NiCo_(2)O_(4).This work provides sufficient evidence on the charge storage of CDs and the interfacial charge kinetics of photocatalysts on the basis of in-situ TPV tests.

关键词： carbon dots photocatalytic hydrogen evolution transient photovoltage technique normal pressure continuous wavelet analysis

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The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

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The Crop Journal 2022年第5期10卷 1264-1273页

作者： Xiaohu Zhao Jingcheng Zhang Ruiliang Pu Zaifa Shu Weizhong He Kaihua Wu College of Artificial Intelligence Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina School of Geosciences University of South FloridaTampaFL 33620USA Lishui Institute of Agriculture and Forestry Sciences Lishui 323000ZhejiangChina

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among ***, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

关键词： Hyperspectral Crop parameters Crop phenotyping continuous wavelet analysis Successive projections algorithm

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Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 138-144页

作者： Jingcheng Zhang Bin Wang Xuexue Zhang Peng Liu Yingying Dong Kaihua Wu Wenjiang Huang College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhou 310018China Key laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094 China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China

Detection of yellow rust using hyperspectral data is of practical importance for disease control and *** an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and *** the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected *** impacts of the mother wavelet type and spectral interval on disease detection were *** results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow *** Mexh wavelet slightly outperformed the others in estimating disease *** the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 ***,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow *** relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

关键词： continuous wavelet analysis spectral interval hyperspectral data wheat yellow rust

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Automatic spikes detection in seismogram

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Acta Seismologica Sinica(English Edition) 2003年第3期16卷 348-355页

作者：王海军靳平刘贵忠 Xi'an Jiaotong University Xi'an 710049China Northwest Institute of Nuclear TechnologyXi'an 710024China Northwest Institute of Nuclear Technology Xi'an 710024China Xi'an Jiaotong University Xi'an 710049China

Data processing for seismic network is very complex and fussy,because a lot of data is recorded in seismicnetwork every day,which make it impossible to process these data all by manual ***,seismic datashould be processed automatically to produce a initial results about events detection and ***,these results are reviewed and modified by *** automatic processing data quality checking is *** are three main problem data that exist in real seismic records,which include:spike,repeated data and

关键词： continuous wavelet analysis nonlinear energy operator spikes seismic

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Mental Activity of the Brain and Emotional Stress

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Journal of Behavioral and Brain Science 2022年第5期12卷 196-210页

作者： Yumatov Evgeniy Antonovich Pertsov Sergey Sergeevich P. K. Anokhin Research Institute of Normal Physiology Moscow Russia

The article considers the biological nature and origins of emotional stress. Emotional stress is primarily formed in the mental activity of the brain in the form of pronounced long-term negative emotions and is secondarily manifested in neurophysiological mechanisms and somatovegetative processes. However, all studies of the development of emotional stress are focused on the study of central neurophysiological mechanisms, excluding the possibility of analysis for the “sources” of emotional stress, which is primarily formed in the subjective sphere of brain activity, i.e., in the mechanisms of emotions. In our studies, we propose a fundamentally new methodology for studying the mental activity of the human brain and, in particular, the mechanisms of emotions. Thereby, modern methods of psychophysiology make it possible to come closer to understanding the nature of emotional stress.

关键词： Brain Emotions Emotional Stress Neurophysiology Psyche Electroencephalogram continuous wavelet analysis

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