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| 1 | Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network显示文摘Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy. | HUANG Yajie LI Zhen YE Huichun ZHANG Shiwen ZHUO Zhiqing XING An HUANG Yuanfang | 2019 | Chinese Geographical Science2019，29，2： | 5 |
| 2 | Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery显示文摘The disease of banana Fusarium wilt currently threatens banana production areas all over the world.Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments.The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine(SVM),random forest(RF),and artificial neural network(ANN)algorithms to identify locations that were infested or not infested with Fusarium wilt.An unmanned aerial vehicle(UAV)equipped with a five-band multi-spectral sensor(blue,green,red,red-edge and near-infrared bands)was used to capture the multi-spectral imagery.A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt.The results showed that the SVM,RF,and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery.The overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based approach.The RF algorithm required significantly less training time than the SVM and ANN algorithms.The maps generated by the SVM,RF,and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2,accounting for 36.3%-40.1%of the total planting area of bananas in the study area.The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%.A simulation of the resolutions of satellite-based imagery(i.e.,0.5 m,1 m,2 m,and 5 m resolutions)showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt.The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery.The results provide guidance for disease treatment and crop planting adjustments. | Huichun Ye Wenjiang Huang Shanyu Huang Bei Cui Yingying Dong Anting Guo Yu Ren Yu Jin | 2020 | International Journal of Agricultural and Biological Engineering2020，13，3： | 3 |
| 3 | Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis显示文摘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). | Wenjiang Huang Junjing Lu Huichun Ye Weiping Kong A.Hugh Mortimer Yue Shi | 2018 | International Journal of Agricultural and Biological Engineering2018，11，2： | 2 |
| 4 | SBA-15-supported molybdenum oxides as efficient catalysts for selective oxidation of ethane to formaldehyde and acetaldehyde by oxygen显示文摘 | Yinchuan Lou Huichun Wang Qinghong Zhang Ye Wang | 2007 | Journal of Catalysis2007，，2： | 1 |
| 5 | Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages显示文摘Leaf area index(LAI)and canopy chlorophyll density(CCD)are key indicators of crop growth status.In this study,we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages.The indices were calculated with Sentinel-2 MSI data and hyperspectral data.Their performances were validated against ground measurements using R2,RMSE,and bias.The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling stage.Furthermore,rededge modified indices outperformed the traditional indices for both data genres.Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early joint-ing and milk development stage,while indices with long red-edge wavelength estimate the sought variables better at the middle three stages.The results were consistent with the red-edge inflec-tion point shift at different growth stages.The best indices for Sentinel-2 LAI retrieval,Sentinel-2 CCD retrieval,hyperspectral LAI retrieval,and hyperspectral CCD retrieval at five growth stages were determined in the research.These results are beneficial to crop trait monitoring by providing references for crop biophysical and bio-chemical parameters retrieval. | Naichen Xing Wenjiang Huang Huichun Ye Yingying Dong Weiping Kong Yu Ren Qiaoyun Xie | 2022 | Big Earth Data2022，6，4： | 0 |
| 6 | Carbide and Nitride Precipitation in High Temperature TensiledSpecimens and Hot Ductility of Nb-and Ti-ContainingSteel CC Slabs显示文摘Hot ductility of the Nb- and Ti-containing line-pipe steel CC slab specimens were measured under the sirain rate of 1 x 10-3/s. Three types of precipitates were found in the fractured specimens. One was the block-shaped coarse TiN particles precipitated at high temperature. Another type was the fine dynamic precipitation products precipitated at 950~900℃ which caused remarkable ductility reduction of the steel. The third type was the co-existed precipitates formed by fine Nb precipitates nucleating and growing on TiN paricles. Compared with Nb-containing steel which contains no Ti, there was no ductility drop for Nb- and Ti-containing steel at temperature between 850℃ and Ar3 and, the γ→α transformation inside the grain matrixes proceeded faster, which both improved the ductility of the steel in the low ductility temperature Region Ⅲ. | WU Dongmei WANG Xinhua LIU Xinyu WANG Wanjun FEI Huichun ZHANG Li YE Jinwei (Metallurgy Engineering School, USTB, Beijing 100083, China) (Baoshan Iron and Steel Co., Shanghai 200091) | 1997 | International Journal of Minerals,Metallurgy and Materials1997，11，2： | 0 |
| 7 | Simple assessment of farmland soil phosphorus loss risk at county scale with high landscape heterogeneity显示文摘In order to improve the existing phosphorus index assessment methods,using the interactive evaluation index(IEI)as an auxiliary variable,the geographically weighted regression(GWR)was adopted as prediction means.A method of regional soil phosphorus risk assessment was constructed by modifying phosphorus index model(MPIM).The GWR-IEI method more accurately predicted available phosphorus(AP)and soil organic matter(SOM),and the prediction precision and goodness of fit were high.Compared with the ordinary least square(OLS)method,the relative improvement of the root mean squared errors(RMSE)with the GWR-IEI method reached 28.95%for available phosphorus predicted,while that of SOM was 21.24%.The phosphorus loss risk of most of the study area(95.29%)was moderate to low.The areas featuring an extremely high phosphorus loss accounted for merely 0.33%of the total research area.Phosphorus loss depends on the effects of many factors.Areas which have strong source or transfer factors are not necessarily high-risk areas for phosphorus loss.Only the co-occurrence of transfer and source factors leads to high risk and greater potential for phosphorus loss.The GWR-IEI-MPIM method accurately reflected the degree of risk for phosphorus at the regional scale,which provides a valuable reference for risk assessment of phosphorus. | Huirong Ning Qimeng Liu Shiwen Zhang Huichun Ye Qiang Shen Wentao Zhang Zhen Li | 2021 | International Journal of Agricultural and Biological Engineering2021，14，2： | 0 |
| 8 | Dragon 4-Satellite Based Analysis of Diseases on Permanent and Row Crops in Italy and China显示文摘The AMEOS(Assimilating Multi-source Earth Observation Satellite data for crop pests and diseases monitoring and forecasting)project aims to bring together cutting edge research to provide pest and disease monitoring and forecast information,integrating multi-source information(Earth Observation,meteorological,entomological and plant pathological,etc.)to support decision making in the sustainable management of insect pests and diseases in agriculture.The main objective of the project,that is,improving crop diseases and pests monitoring and forecasting,will be achieved by utilizing EO data,developing new algorithms,and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information.The project foresees the assessment of the possibility of using available satellite images datasets to assess the evolution of diseases on permanent(olive groves,vineyards),or row crops(wheat)in Italy and China.The paper describes the results of the research activity which focused on:①improving the classification of the agricultural areas devoted to winter wheat and olive trees,starting from what has been made available from the Corine Land Cover initiative;②developing an approach suitable to be automated for estimating trees by using Sentinel 2 images;③developing a new index,REDSI(consisting of Red,Re 1,and Re 3 bands),for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale.The research activity covers the:Province of Lecce,that is the Italian area strongly affected,since 2015,by the Xylella fastidiosa disease which causes a rapid decline in olive plantations.Province of Anyang,Neihuang county,which was affected by the yellow rust disease in the spring 2017. | Giovanni LANEVE Roberto LUCIANI Pablo MARZIALETTI Stefano PIGNATTI Wenjiang HUANG Yue SHI Yingying DONG Huichun YE | 2020 | Journal of Geodesy and Geoinformation Science2020，3，4： | 0 |
| 9 | A new spectral index for the quantitative identification of yellow rust using fungal spore information显示文摘Yellow rust(Puccinia striiformis f.sp.Tritici)is a frequently occurring fungal disease of winter wheat(Triticum aestivum L.).During yellow rust infestation,fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins.We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index(YRSI).The estimation accuracy and stability were evaluated using two years of leaf spectral data,and the results were compared with eight indices commonly used for yellow rust detection.The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data(R^(2)=0.710,RMSE=0.097)and outperformed the published indices(R^(2)=0.587,RMSE=0.120)for the validation using the 2002 spectral data.The random forest(RF),k-nearest neighbor(KNN),and support vector machine(SVM)algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested,healthy,and aphid–infested wheat spectral data.The YRSI provided the best performance. | Yu Ren Huichun Ye Wenjiang Huang Huiqin Ma Anting Guo Chao Ruan Linyi Liu Binxiang Qian | 2021 | Big Earth Data2021，5，2： | 0 |
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