Color Measurement in Leaf Diagnosis

Abstract

The paper presents for the first time in literature the use of color measurement in leaf diagnosis. It uses the data obtained in certain well- established conditions, in CIE 1976 system for quantifying the color changes of leaves in different sufference (plant or leaf damage) symptoms.

The data are presented for sugar beet plants, but the application can be easily extended to any crop, arboretums, or ecological systems.

As a further extension of the study, it can be used in the telediagnosis of plants' sufferences by plane or even by satellite.     

Analysis of non-organic elements in plant foliage using polarised X-ray fluorescence spectrometry

An X-ray fluorescence (XRF) method for the rapid and non-destructive analysis of 30 non-organic elements in plant leaves over five orders of magnitude concentration from several percentage of dry weight to sub-milligram per kilogram, is described. There is a growing need for a simple method of monitoring non-organic trace elements in plant material, especially those which accumulate in soils with application of fertilisers, both inorganic and biosolids. Earlier attempts to use XRF for the analysis of plant material suffered from sensitivity loss due to the high background of organic matrices caused by scattering of the X-ray source. We overcome this by using polarised X-ray sources in a Cartesian geometrical arrangement with sample and energy dispersive detector (EDPXRF), a configuration which can achieve an order of magnitude reduction in background compared with unpolarised sources. Further sensitivity gains are made using a high power tube as a source of primary X-rays and for some analytes employing secondary targets for near-monochromatic excitation. Sample preparation is simple involving only pulverising and briquetting of dried samples. Accuracy is evaluated by comparing data obtained for the suite of NIST plant leaf samples (NIST 1515 apple leaves, NIST 1547 peach leaves, NIST 1570a spinach leaves, NIST 1573a tomato leaves and NIST 1575a pine needles) and tobacco leaf standards CTA-OTL-1 and CTA-VTL-2. The data compare well over the whole concentration range and the method provides a rapid analytical tool for monitoring potentially toxic trace elements in all types of foliage with adequate sensitivity for many purposes.     

植物激素吲哚乙酸电化学生物传感器的研究

本文提出了以绿豆芽叶片组织-二茂铁修饰的碳糊电板(LFMCE)作为植物激素吲哚乙酸传感器的研制，探讨了影响电极性能的因素，初步研究了吲哚乙酸氧化酶处于最优活性的基本条件。实验表明，此电极在NaH2PO4-Na2HPO4(pH5．8)缓冲溶液中对植物生长素有良好的电催化作用，氧化峰电位约在0.42V左右。用循环伏安法测得的氧化峰电流变化值与吲哚乙酸浓度在8-l60μg／mL范围内呈良好的线性关系，检测下限为4．2μg／mL，相关系数为0．9984，在样品回收率的测定中取得满意结果。     

几种常见树种叶片光谱秋季变化特征分析

叶片随着时间生长产生变化,其光谱特征也会发生变化。研究相同树种叶片不同时间条件下的光谱变化规律以及不同树种叶片相同时间条件下的光谱特征,不仅为植被叶片光谱随时间变化的规律研究提供理论基础,也是高光谱遥感精确识别植被信息的关键。选取北京市10种常见树种,利用地物光谱仪观测各树种不同时间叶片光谱,同时将观测的光谱进行一阶微分处理和典型波段分析,对比相同时间不同树种叶片光谱的差异,分析相同树种不同时间的光谱变化规律,探索不同时相条件下高光谱遥感识别树种的有效波段。结果表明：不同树种叶片光谱均随时间的改变而产生显著变化,但差异规律各不相同;不同树种相同时间叶片光谱在部分波段存在显著差异,为高精度树种识别提供了理论依据和叶片基础光谱数据。     

火焰原子吸收光谱法测定中草药丹参不同部位金属元素的含量

采用HNO₃-HClO₄(4∶1)混酸消化丹参的根、茎和叶,利用火焰原子吸收光谱法对消化液中的五种人体必需的金属元素K,Cu,Zn,Fe,Mg进行了测定。该方法的标准曲线的相关系数为0.998 7～0.999 7,加标回收率为99.7％～104.4％,相对标准偏差(RSD)小于2％。结果显示：K,Cu,Zn,Fe,Mg在丹参根、茎和叶的含量均有以下次序：K＞Mg＞Fe＞Zn＞Cu。丹参叶的K,Cu,Fe,Mg的含量大于根和茎。根、茎、叶中的锌铜比值均较小。该测定结果为研究金属元素K,Cu,Zn,Fe,Mg在丹参中的分布以及金属元素的含量与丹参药效之间的关系提供了有用的数据。     

冬小麦不同生育时期叶面积指数反演方法

针对当前作物叶面积指数遥感反演过程中,在不同生育时期采用相同的植被指数进行反演存在叶面积指数反演精度较低的问题。以冬小麦为研究对象,选取了对冬小麦覆盖度响应程度不同的六种宽带和四种窄带共10种植被指数,分析比较了在冬小麦整个生育期选用当前广泛使用的归一化植被指数(NDVI)反演冬小麦的LAI和在冬小麦不同生长阶段选用不同的植被指数反演冬小麦LAI的结果差异。在冬小麦整个生育期内使用NDVI反演小麦LAI得到的LAI反演值和真实值之间的R2=0.558 5,RMSE=0.320 9。改进的比值植被指数(mSR)适合于反演冬小麦生长前期(拔节期之前)的LAI,得到的LAI反演值和真实值之间的相关系数r=0.728 7,均方根误差RMSE=0.297 1;比值植被指数(SR)适于反演冬小麦生长中期(拔节到抽穗前),得到的LAI反演值和真实值之间的R2=0.654 6,RMSE=0.306 1;NDVI适于反演冬小麦生长后期(抽穗到成熟期)的LAI,得到的LAI反演值和真实值之间的R2=0.679 4,均方根误差RMSE=0.316 4。 研究表明：在冬小麦的不同生育时期,根据地表作物覆盖度的变化和反射率的变化,选择不同的植被指数建立冬小麦LAI的反演模型获得的反演精度均高于在冬小麦整个生育期使用NDVI获得的反演结果。说明在冬小麦的不同生育时期选择不同的植被指数构建LAI的分段反演模型可以改善冬小麦LAI的反演精度。     

不同含水量条件下树种叶片光谱差异分析

叶片含水量是影响树种光谱特征的一个重要因素,探索不同叶片含水量条件下相同树种叶片光谱特征的变化规律及相同叶片含水量条件下不同树种叶片的光谱差异,不仅是高光谱植被遥感信息识别的关键,也为研究植被光谱随着叶片含水量变化产生的差异提供理论支撑。利用地物光谱仪对6个树种叶片进行观测,获取了不同含水量叶片的反射光谱,同时进行一阶微分光谱变换,从而分析不同叶片含水量条件下各树种叶片的光谱变化特征,对比了相同叶片含水量条件下不同树种叶片的光谱差异,探索高光谱遥感识别树种叶片含水量的可能波段。结果表明：各树种叶片光谱都随着叶片含水量的改变而产生较大差异,但变化规律各有不同;相同叶片含水量条件下,不同树种叶片的光谱在部分波长范围差异较大,为实现树种高精度识别提供了可能。本研究旨在为植被叶片光谱库的构建以及植被的高光谱识别提供理论支撑和基础数据。     

利用偏最小二乘回归从冬小麦冠层光谱提取叶片含水量

通过人为控制灌溉水平,在冬小麦3个发育期(孕穗、开花、乳熟)测定了冠层光谱和叶片含水量(leaf water content,LWC)。针对每期数据,结合偏最小二乘回归和迭代特征去除,建立了基于诊断波段的LWC回归模型。结果表明,叶片水分的光谱响应及反演精度受小麦生长状态的影响。在孕穗、开花和乳熟3个发育阶段,回归模型中光谱数据的最佳利用形式分别为对数光谱、导数光谱和反射率光谱;重要光谱区间为SWIR,NIR和SWIR;模型交叉验证决定系数(R2_CV)为0.750,0.889和0.696。研究结论对今后监测冬小麦旱情和开发作物水分遥感产品具有重要的指导作用。     

Effect of seasonal variation on dust samples of a winter fog affected urban environment of India,South East Asia

Physical and chemical characteristics of leaf dust (LD) samples, collected between 2011 and 2013, of a severely winter fog affected and polluted urban environment (Allahabad, India) of Indo-Gangetic plain are presented in this study. The weather changes in the study area are grouped into three effective annual seasonal periods – winter fog, pre-monsoon and post-monsoon. The pronounced impact of seasonal variations is observed in LD characteristics, as ~46% of the winter fog period samples ranges in size between 2.5 and 20 µm with high magnetic susceptibility (up to 938.22 × 10^?8 m³ kg^?1). In comparison, 35% of the LD from pre-monsoon period occurs in the size range of 100–300 µm with low magnetic susceptibility (up to 43.29 × 10^?8 m³ kg^?1). The fractal analysis involving submicroscopic clast dimension of LD also reveals a seasonal influence. The toxic metals (Pb, Zn, Cd, Cr and Ni) in LD belonging to winter fog and post-monsoon periods increase in comparison to samples from pre-monsoon time. Multivariate statistical analysis of trace element data suggests that the LD collected from winter fog and post-monsoon periods originate predominantly from anthropogenic source, whereas those collected during pre-monsoon owe their origin to crustal and anthropogenic sources, mainly. The present study has demonstrated the role of seasonal variation in controlling the physical and chemical properties of particulate matter based on multiple parameter analyses, and the derivation of the dust load from a mixed source (upper continental crust, anthropogenic and vehicular) for the study area for the first time.     

离子色谱法测定水果及其树叶的含氯量

本文提出用离子色谱法测定水果及其树叶中氯含量的方法,在IC-PAK阴离子柱上,用4 mmol/L邻苯二甲酸(pH4.5)作流动相进行洗脱,电导检测器检测。方法简便、快速,灵敏、准确。氯的最小检出量为0.14μg,相对标准偏差为2.4%,回收率为99.4%。