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Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

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Journal of Integrative Agriculture 2014年第4期13卷 760-769页

作者： LIU Liang-yun huaNG Wen-jiang PU Rui-liang wang ji-hua Key Laboratory of Digital Earth Science Center for Earth Observation and Digital EarthChinese Academy of Sciences Department of Geography Environmentand PlanningUniversity of South Florida National Engineering Research Center for Information Technology in Agriculture

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

关键词： spectral ratio index spectral reflectance vegetation index dehydration paraquat herbicide stripe rust

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金线莲苷的研究现状

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天然产物研究与开发 2024年第2期36卷 348-356,367页

作者：张闻婷杨旻静梅瑜王继华广东省农业科学院作物研究所广州510640 仲恺农业工程学院农业与生物学院广州510225

金线莲苷(kinsenoside),一种从药用植物金线莲中提取的天然生物活性糖苷,已被证明具有显著的药理功效。本文通过收集金线莲苷的相关研究文章,尤其针对近几年更新发表的研究成果,整理分析后将金线莲苷的研究现状主要分为3方面,即:金线莲... 详细信息

金线莲苷(kinsenoside),一种从药用植物金线莲中提取的天然生物活性糖苷,已被证明具有显著的药理功效。本文通过收集金线莲苷的相关研究文章,尤其针对近几年更新发表的研究成果,整理分析后将金线莲苷的研究现状主要分为3方面,即:金线莲苷的基本信息,包括化学结构、来源物种及影响积累的因素;金线莲苷的分离提取、定性定量检测、人工合成方法总结;金线莲苷的药理作用,包括降血糖、降血脂、保肝作用及缓解骨病变等。鉴于金线莲苷的原材料资源匮乏,人工种植成本昂贵导致的市场应用受限,我们认为通过提高开唇兰属植物中金线莲苷的提取效率,开发更多含有金线莲苷的植物,或以栽培效率和人工合成技术的进步等方式解决生产实践问题。而在药理研究方面,我们支持通过在大型灵长类动物上进行剂量毒性试验,开发靶向治疗策略提高治疗准确性,以及精确金线莲苷使用的干预时间点等试验为金线莲苷在未来临床实践中的转化应用奠定基础。

关键词：金线莲苷提取检测人工合成药理作用

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穿心莲TCP基因家族全基因组鉴定及非生物胁迫下的表达分析

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中国中药杂志 2024年第2期49卷 379-388页

作者：张龙徐世强李静宇胡学博顾艳王继华广东省农业科学院作物研究所广东省农作遗传改良重点实验室广东广州510640 华中农业大学植物科学技术学院湖北武汉430070

穿心莲是我国岭南地区重要的药用植物,具有清热解毒、抗菌消炎等功效。TCP基因家族是调控植物生长发育和胁迫响应的一类转录因子。为解析TCP基因家族成员在穿心莲非生物胁迫下的作用,该研究基于全基因组水平鉴定穿心莲TCP基因家族,并进... 详细信息

穿心莲是我国岭南地区重要的药用植物,具有清热解毒、抗菌消炎等功效。TCP基因家族是调控植物生长发育和胁迫响应的一类转录因子。为解析TCP基因家族成员在穿心莲非生物胁迫下的作用,该研究基于全基因组水平鉴定穿心莲TCP基因家族,并进行了其响应非生物胁迫的表达模式分析。结果表明,穿心莲TCP基因家族共有23个成员,氨基酸长度为136~508 aa,相对分子质量为14854.71~55944.90 kDa,等电点为5.67~10.39,均定位于细胞核,不均匀分布于13条染色体上,系统进化分析将其划分为3个亚家族:PCF、CIN和CYC/TB1。基因结构和保守基序分析显示绝大多数TCP基因家族成员均含有相同排列顺序的motif 1、motif 2、motif 3和1~3个CDS。启动子顺式作用元件分析表明穿心莲TCP基因家族的转录表达可能受光、激素和逆境胁迫的诱导;结合TCP基因家族响应逆境胁迫的表达模式分析和qRT-PCR验证,预测到ApTCP4、ApTCP5、ApTCP6和ApTCP11可能参与响应干旱、高温和MeJA等多种非生物胁迫。该研究为深入挖掘穿心莲TCP基因响应非生物胁迫下的功能奠定基础。

关键词：穿心莲 TCP转录因子基因家族非生物胁迫

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Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

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Journal of Integrative Agriculture 2012年第9期11卷 1445-1452页

作者： LI Cun-jun wang ji-hua wang Qian wang Da-cheng SONG Xiao-yu wang Yan huaNGWen-jiang Beijing Research Center for Information Technology in Agriculture Beijing 100097 P.R.China National Engineering Research Center for Information Technology in Agriculture Beijing 100097 P.R.China

Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.

关键词： grain protein content agronomic parameters multi-temporal Landsat partial least squares regression

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Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

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Journal of Integrative Agriculture 2019年第7期18卷 1547-1561页

作者： LI Zhen-hai jiN Xiu-liang LIU Hai-long XU Xin-gang wang ji-hua Key Laboratory of Agri-lnformatics Ministry of Agriculture and Rural AffairsBeijing 100097P.R. China Beijing Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097P R.China UMR EMMAH INRANAPVAvignon 84914France Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-lnformation Service TechnologyMinistry of Agriculture and Rural AffairsBeijing 100081P.R. China Beijing Research Center for Agri-Food Testing and Farmland Monitoring Beijing Academy of Agriculture and Forestry SciencesBeijing 100097P.R.China

A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.

关键词： global sensitivity analysis DSSAT EFAST wheat yield grain protein content

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Relationship of 2 100-2 300 nm Spectral Characteristics of Wheat Canopy to Leaf Area Index and Leaf N as Affected by Leaf Water Content

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Pedosphere 2006年第3期16卷 333-338页

作者： ZHAO Chun-jiang wang ji-hua LIU Liang-Yun huaNG Wen-jiang ZHOU Qi-Fa National Engineering Research Center for Information Technology in Agriculture Beijing 100089 （China） The State Key Laboratory of Plant Physiology and Biochemistry College of Life Sciences Zhejiang University Hangzhou 310012 （China）

The effects of leaf water status in a wheat canopy on the accuracy of estimating leaf area index (LAI) and N were determined in this study using extracted spectral characteristics in the 2 000-2 300 nm region of the short wave infrared (SWI) band. A newly defined spectral index, relative adsorptive index in the 2000-2300 nm region (RAI2000-2300), which can be calculated by RAI2000-2300 = (R2224 - R2054) (R2224 + R2054)-1 with R being the reflectance at 2224 or 2054 nm, was utilized. This spectral index, RAI2000-2300, was significantly correlated (P < 0.01) with green LAI and leaf N concentration and proved to be potentially valuable for monitoring plant green LAI and leaf N at the field canopy scale. Moreover, plant LAI could be monitored more easily and more successfully than plant leaf N. The study also showed that leaf water had a strong masking effect on the 2 000-2 300 nm spectral characteristics and both the coefficient between RAI2000-2300 and green LAI and that between RAI2000-2300 and leaf N content decreased as leaf water content increased.

关键词：氮反射系数小麦叶面积指数

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Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing,China,Using RS and GIS

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Agricultural Sciences in China 2011年第9期10卷 1419-1430页

作者： wang Da-cheng LI Cun-jun SONG Xiao-yu wang ji-hua YANG Xiao-dong huaNG Wen-jiang wang Jun-ying ZHOU ji-hong Institute of Agricultural Remote Sensing and Information Technology Zhejiang University Hangzhou 310029 P.R. China Beijing Research Center for Information Technology in Agriculture Beijing 100097 P.R.China Agricultural Technology Extension Station in Beijing Beijing 100029 P.R.China

It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network （ANN） analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system （GIS）. An integrated land suitability potential （LSP） index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.

关键词： LSP ANN suitable areas wheat RS and GIS

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Exploring the Feasibility of Winter Wheat Freeze Injury by Integrating Grey System Model with RS and GIS

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Journal of Integrative Agriculture 2013年第7期12卷 1162-1172页

作者： wang Hui-fang GUO wei wang ji-hua huaNG Wen-jiang GU Xiao-he DONG Ying-ying XUXin-gang Beijing Research Centre for Information Technology in Agriculture Institute of Agriculture Remote Sensing and Information System Application Zhejiang University

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model （GSM） to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing （RS） and geographic information system （GIS） technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis （GRA） was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm （GCA）, and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

关键词： winter wheat freeze injury RS GIS GRA GCA

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Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

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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 method. A combination of CWA and PLSR was found to

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

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Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

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Agricultural Sciences in China 2011年第10期10卷 1595-1602页

作者： LI Rui LI Cun-jun DONG Ying-ying LIU Feng wang ji-hua YANG Xiao-dong PAN Yu-chun Beijing Research Center for Information Technology in Agriculture Beijing 100097 P.R.China Information Engineering Institute Capital Normal University Beijing 100048 P.R. China Chengdu Dawan High School Chengdu 610300 P.R.China

Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

关键词： crop model assimilation Ensemble Kalman Filter algorithm leaf area index

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