基于热脉冲精密土壤水分测试仪的研究与设计

针对土壤水分很难连续准确测量以及测试装置很难在深埋处进行测量的问题,开发一款热脉冲精密土壤水分测试装置。该装置以STM32为硬件开发平台,辅助HS3001温湿度传感器和PTC加热模块组成热脉冲测含水量模块,通过施加短时低热量热脉冲,使得被测土壤先升温再降温,由测试装置定时测量土壤温度的变化值,最后基于理想半无限空间热脉冲传导理论,计算出土壤体积含水量。该装置利用热脉冲可以间接测量土壤体积含水量,热脉冲时间控制在1～2 min,测量周期约300 s,具有短时连续监测的优势;同时在非加热模式下,可以直接获取土壤温湿度,在降雨入渗和水位变化时可以直接监测湿度变化;此外加热模块还可以为温湿度传感器升温除湿,以增强深埋土壤高湿度环境下长期工作性能。     

基于空气湿度的土壤含水量估计模型研究

针对当前土壤含水量测量装置结构复杂、测量稳定时间长、成本造价高以及传感器间一致性差等问题,对土壤水分蒸发过程与土壤含水量之间的关系进行了理论分析。基于对近土壤区域空气相对湿度的测量,在彭曼蒙特斯公式基础上提出了一种土壤含水率估计模型,并设计制作了集温度、光照强度及土壤含水量测量为一体的测试系统进行实验验证。实验表明,测试结果与称重法得到的实际含水量具有较高的一致性,且该测试具有响应速度快、成本低等优势。     

负压吸力与含水量数理统计关系探讨

土壤水分张力计是测量土壤水势的一种传感器,它和压力、温度、比重等参数一样都是通过传感器转换成人们最易接受的物理量,土壤水分张力计测量结果为土壤水负压吸力,负压吸力表示土壤水的能态（即水势,常称负压吸力,单位是千帕斯卡ｋＰａ）,不是土壤对水的吸力,而是水受到各种力的综合作用后,水所处的能态位置,负压吸力与含水量的关系视为水分特征曲线即土壤持水特性曲线,是研究土壤持水特性的一个重要方面,如同研究血压与心血管病的关系一样,土壤水负压吸力决定着动植物的命运,同样具有重要意义。土壤在脱水过程中的含水量与负压吸力的关系为非线性,接近双曲线。本文采用数理统计中常用的最小二乘法来处理变量间的关系。第一步,选择样本,实测负压吸力与含水量,将其关系一一对应,导出双曲线方程;第二步,将双曲线的自变量倒过来直接转换为直线并定出回归方程,从而公式化,方便利用。     

土样含水量快速测定传感技术研究

土壤含水量的速测受土壤电导和土壤类型等诸多因素的影响，如何确保测试精度是实现土壤含水量速测的关键。首先分析了土壤含水量速测传统方法存在的问题，并从低成本快速测试的目标出发提出了实现土样含水量测试的电容传感技术、传感器结构和原理，并给出了传感器的信号检测处理电路。使用速测技术设计而成的测定仪器测试结果与常规烘干称重法对比，其精度达到土样含水量测定的要求。     

旱地玉米秸秆覆盖保墒节水效果浅析

采用秸秆覆盖能够增加土壤充水孔隙的数量,同时因覆盖物的遮阴作用,又抑制了土壤水分蒸发,从而使覆盖田土壤含水量比常规耕作田大为增加.为了进一步探讨旱地玉米秸秆覆盖对土壤水分的影响,山西省土壤肥料工作站从1992年开始,在长治县设置了不同覆盖形式的对比试验示范,对其保墒效果进行了对比分析.     

基于嵌入式油品含水率检测系统的设计

文中设计了一种基于嵌入式的油品含水率检测系统。该系统硬件部分设计了电容-电压转换电路、温度测量电路、串口通信电路以及单片机最小系统电路等。通过设计的电容传感器来检测油中的含水量,把含水量的多少转换为电容传感器电容值的变化,最后根据相应关系计算出含水率。经实验研究表明,该系统含水率检测精度达到了0.01级,测量精度高、稳定性好。     

TDR300在测量土壤水分中的应用

土壤水分是土壤组成的重要的可变量,采用时域反射技术可以快速准确的测量土壤容积含水量.该仪器可以通过内置的数据采集器多点收集数据,也可以手动删除数据,通过软件可以下载数据、改变设置和测量相对含水量的程序.     

PASW-I型便携式土壤水分观测仪应用

文章介绍了一种应用FDR原理的便携式土壤水分观测仪及其实际应用。该仪器具有现场读数和存储数据的功能,其传感器技术指标符合中国气象局土壤水分观测仪的设计要求,可对各类土壤和多种介质的水分进行测量,用于土壤水分监测预警,有助于提高土壤墒情应急观测能力。文章还对3个应用台站的人工及器测数据进行了对比分析,并据此提出了合理使用该仪器的建议。     

浅谈土壤检测项目结果计算

建立一种统一的、更具可比性的土壤分析检测结果的计算方法。通过分析比较土壤检测现行有效的国家标准和行业标准,发现各标准中的结果计算差异主要在土壤水分概念以及数据的运用,提出统一以烘干土壤为基准并用干物质含量对称样质量进行修正的计算方法,这是一种更合理的结果计算方法。统一土壤检测项目结果计算方法更能体现检测结果的准确性和可比性,消除各检测项目分析过程中土壤水分的差异,简化实验过程,提高批量多项目检测的效率。     

一种液压油多污染物微检测传感器设计及研究

基于微流体芯片技术提出了一种液压油微传感器.微传感器由内嵌硅钢片的平面电感检测线圈与对置硅钢片构成.电感检测模式下对金属颗粒进行检测,电容模式下检测水滴与气泡,实现了对油液中铁磁性颗粒、非铁磁性颗粒、水滴、气泡4种污染物的区分检测.通过实验证明新型结构的微传感器在电感模式下对金属颗粒检测精度的提高,并可以检测到油液中2...     

Operative Calibration Methodology of a TDR Sensor for Soil Moisture Monitoring under Irrigated Crops

Time domain reflectometry (TDR) is more and more frequently used for soil water content measurements in replacement of other techniques as neutron probe. Such observations that can be continuously collected on dataloggers are convenient for monitoring water fluxes under irrigated crops. Though relationships to calculate volumetric water content from analysis of TDR signal and collected data are published in literature or given by manufacturers for different commercialized devices, the results are not satisfactory for many soils and specific calibrations are required. In replacement of the traditional calibration method, two rapid operative computer assisted methods are proposed. The first one is based on drip moistening of a cylindrical sample of disturbed dry soil in which a TDR sensor is vertically embedded. The second one uses the same cylindrical sample wetted close to saturation in which a TDR sensor is inserted step by step during measurements. The data from the two methods were fitted using second degree models. These results are in good agreement with conventional calibration method or gravimetric field measurements of soil water content. Utilization of short time step TDR measurements for monitoring soil water storage under a furrow irrigated corn shows consistent observations with water applications or uptakes by crop. Field comparison with neutron probe and gravimetric measurements corroborate the need of a specific calibration for the soil studied in this paper though its clay content is about 20%.     

Effect of Time-Domain Reflectometry probe location on soil moisture measurement during wetting and drying processes

In this paper studies on the impact of the location of Time-Domain Reflectometry probes in soil samples on the moisture measurement are presented. In particular, we were interested if the commonly accepted assumption that moisture measurements performed by TDR probes correctly average the value of the soil water content in a soil sample. Soil samples having different physical properties, both undisturbed and disturbed, were used. Our results show that the soil moisture measurement depends on the placement of the TDR probe in the sample, and a TDR probe placed vertically measures the arithmetic mean of soil moisture for the whole sample, for both the wetting and the drying cycles. This result is general and does not depend on the analyzed sample.     

Classification and authentication of unknown water samples using machine learning algorithms

This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples.     

Study of the volumetric water content based on density,suction and initial water content

The practical application of determination of the soil water retention curves (SWRC) is in seepage modelling in unsaturated soil. The models based on the physics behind the seepage mechanism has been developed for predicting the SWRC. However, those models rarely consider the combined effects of initial volumetric water content and soil density. One of the best routes to study these effects is to formulate the SWRC models/functional relations with volumetric water content as an output and the soil density, initial volumetric water content and soil suction as input parameters. In light of this, the present work introduces the advanced soft computing methods such as genetic programming (GP), artificial neural network and support vector regression (SVR) to formulate the volumetric water content models based on the suction, density and initial volumetric water content. The performance of the three models is compared based on the standard measures and goodness-of-fit tests. The findings from the statistical validation reveals that the GP model performs the best in generalizing the volumetric water content values based on the suction, density and initial water content. Further, the 2-D and 3-D plots, evaluating the main and the interaction effects of the three inputs on the volumetric water content are generated based on the parametric procedure of the best model. The study reveals that the volumetric water content values behave non-linearly with respect to soil suction because it first decreases till a certain point of soil suction and then increases suddenly.     

车载式GC-MS测定水中64种半挥发性有机化合物

利用车载式气相色谱-质谱联用仪,以液液萃取为前处理手段,采用了同时全扫描和选择离子监测模式建立了地表水中64种半挥发性化合物(SVOCs)的检测方法。方法以64种SVOCs的全扫描谱图进行定性,以其特征离子峰进行定量。当水样取样体积为250 mL时,64种SVOCs的检出限在0.002～0.048μg/L之间,精密度为2.1%～8.8%。通过对上海一处河水和实验室自来水水样进行分析,结果表明有16种SVOCs检出,其中酞酸酯类含量较高。测定方法准确度高,精密度好,简便快捷,显示了方法作为现场检测或应急检测的应用前景。     

原位衍生分析土壤中多种酸性有机农药残留

为克服土壤中酸性有机农药测定的预处理过程复杂、回收率低和二次污染严重等不足,建立了土壤中酸性有机农药原位衍生分析方法。将土壤样品、Na₄-EDTA和水混合均匀,待水分挥发后将样品转移至衍生瓶中,以五氟苄基溴为衍生试剂进行原位超声衍生;萃取液经净化后,采用负化学电离源(NCI)选择离子监测模式测定;同时讨论了络合萃取条件、衍生条件、净化条件、负化学源质谱条件及土壤TOC对样品分析的影响。结果表明：该方法在5~500 μg/L线性范围内,各组分响应峰面积与其相应浓度的线性相关系数r²大于0.998 0;空白基质中不同浓度的加标回收率在80%~110%之间（n=7）,相对标准偏差在8.2%~13%之间（n=7）。应用该方法对不同地域的实际土壤样品进行分析检测,检出了多数酸性有机农药。     

智能化农业信息远程数据采集系统

阐述了智能化农业信息数据采集和远程无线传输系统。等多种参量，并由手机短信模块定时发往实验室的计算机内，的工作量。该系统能定时测量环境温湿度、土壤含水量、光照强度实现无人值守的实时测量和自动记录，可大大降低测量     

Study of sample temperature compensation in the measurement of soil moisture content

Since the near-infrared (NIR) spectrum is susceptible to sample temperature fluctuations, we investigate the influence of sample temperature on the predictive power of calibration model for soil moisture content (MC) and propose the multi-source information fusion technology based on back propagation neural network (BPNN) to compensate for sample temperature effect. With the discrete wavelet transform (DWT) as the pre-processing method and the least squares support vector machine (LS-SVM) regression as the modeling method, a model at 20 °C to predict MC of the soil samples at other temperatures was established. The results show that except for 20 °C, the root mean square error of prediction (RMSEP) are large. We analyze the predicted results with the dual-factor analysis of variance without duplication and the result shows that the effect of sample temperature on the prediction model for soil MC is significant. A temperature compensation model was then established with combining of soil MC and sample temperature based on BPNN. The predicted results showed that the prediction precision of the model was improved significantly.     

A method for calibrating a strip resonator used in measurements of the complex permittivity of moist soils and grounds

A method for calibrating a microstrip microwave resonator used to measure the complex permittivity of moist soils and grounds is proposed. This method is based on the use of mixtures of a disperse solid substance and aqueous solutions of salts with known values of their complex permittivity. The resonator is composed of two segments of strip lines, the capacitive electromagnetic coupling between which occurs through a container with a moist soil sample. Sandy soil samples wetted by a sodium chloride solution were used as reference samples. The values of their complex permittivity were determined using the generalized refraction model and the reference data for the complex permittivity of a salt solution. A system of algebraic equations was set up, the solution to which, together with the formulas of the refraction model, allowed the complex permittivity of the test soil sample to be reconstructed from the measured values of the resonance frequency shifts and Q factor. The rms errors in measuring the real and imaginary parts of the complex permittivity, which varied over the ranges 4.79–11.03 and 0.39–3.15, were 0.21 and 0.03, respectively.