基于可见近红外光谱的土壤有机质快速检测方法和仪器研究

发布时间：2018-06-05 17:14

本文选题：土壤有机质 + 可见近红外光谱　；参考：《浙江工业大学》2015年硕士论文

【摘要】：土壤有机质是土壤重要的组成部分,对土壤肥力的发展和植物的生长起着重要作用。我国传统农业生产中注重精耕细作、大量施用有机肥料,导致劳动生产率较低、对土地环境的影响较大。因此快速获取土壤中有机质含量信息,对于农业生产的科学管理、定量施肥的推广和精细农业的发展具有基础性的作用和实际意义。传统的土壤有机质含量测定主要是依靠实验室化学方法测定,尽管该方法测量精度较好,然而其具有对操作人员的要求较高、耗费时间较长以及测量成本较高的缺点。由于土壤有机质含量是农田养分分级的重要指标,因此亟需一种快速简便、精度较高的方法和仪器对农田土壤有机质含量进行分级评估。本研究基于可见近红外光谱技术针对浙江省的土壤特点对有机质含量进行了光谱建模研究和仪器研制的探索,其中实验土壤样本采集自浙江省内十个不同地区共104个;采用美国海洋公司的USB4000光纤光谱仪获取土壤光谱,范围为350~1050nm。本文的主要工作和研究内容如下:(1)采用可见近红外光谱技术建立了基于全谱的土壤有机质光谱检测模型,对土壤有机质含量进行定量检测。根据采集的土壤光谱特点,分析了异常样本及其对建模的影响。针对土壤光谱中噪声较大的问题,采用平滑滤波、多元散射校正、基线校正和小波阈值消噪等预处理方法对光谱进行预处理并进行对比,其中小波阈值消噪法在sym6小波函数7层分解下除噪效果最佳,PLS模型的预测结果相比原始光谱决定系数(R2P)由0.74提高到了0.76,相对分析误差(RPD)由2.00提高到了2.07。结果表明适当的预处理方法可以提高土壤有机质检测模型精度。(2)研究了适用于仪器设计的光谱计算模型简化方法。由于土壤光谱数据量大、包含了大量冗余信息,导致基于全谱方式建立的检测模型复杂度高、计算量大,因此通过提取光谱中的特征波长简化土壤有机质定量分析模型。提出了采用间隔偏最小二乘法、无信息变量消除、连续投影算法、竞争自适应重加权采样、遗传算法、蚁群优化算法和粒子群优化算法等方法提取光谱中的特征波长建模,结果显示粒子群优化算法提取的26个波长PLS建模预测效果最佳,其R2P为0.81、RPD为2.31,通过选择特征波长有效的简化了检测模型并缩短了计算时间。(3)研制了基于光谱特征波长的便携式土壤有机质检测仪器样机。采用模块化的硬件设备设计并制作了仪器原型,并根据设计的光谱仪硬件特点使用Java编程语言开发了基于ARM-Linux嵌入式系统的土壤有机质快速检测软件,该软件具有良好的交互界面并且功能实现较为完备。采用该仪器对20个样本实际检测结果R2P为0.78,RPD为1.74,根据《浙江省标准农田地力调查与分等定级技术规范》有机质等级划分标准对土壤有机质含量分级准确率达到85%。该样机经过进一步完善后可适用于土壤有机质的分级和现场使用。
[Abstract]:Soil organic matter is an important part of soil and plays an important role in the development of soil fertility and plant growth. In the traditional agricultural production of our country, we pay more attention to intensive ploughing and a large amount of organic fertilizer, which results in low labor productivity and great influence on the land environment. Therefore, it is of fundamental and practical significance for scientific management of agricultural production, extension of quantitative fertilization and development of fine agriculture to obtain the information of soil organic matter content quickly. The traditional determination of soil organic matter content mainly depends on the laboratory chemical method. Although the precision of this method is good, it has the disadvantages of higher requirement for operators, longer time consumption and higher measuring cost. Because the content of soil organic matter is an important index of farmland nutrient classification, it is urgent to use a rapid, simple and accurate method and instrument to evaluate the soil organic matter content in farmland. Based on the visible near infrared spectroscopy (VNIR), the content of organic matter in Zhejiang Province was studied by spectral modeling and instrument development. The experimental soil samples were collected from 10 different regions of Zhejiang province. The soil spectrum was obtained by USB4000 optical fiber spectrometer of American Ocean Company, with a range of 350 ~ 1050nm. The main work and research contents of this paper are as follows: (1) A spectral detection model of soil organic matter based on full spectrum was established by using visible near infrared spectroscopy (VNIR) to quantitatively detect the content of soil organic matter. According to the characteristics of soil spectrum collected, the abnormal samples and their effects on modeling were analyzed. Aiming at the problem of high noise in soil spectrum, the spectral pretreatment methods such as smoothing filtering, multivariate scattering correction, baseline correction and wavelet threshold de-noising are used to pre-process and compare the spectra. The prediction result of wavelet threshold de-noising method under the sym6 wavelet function 7-layer decomposition is improved from 0.74 to 0.76, and the relative analysis error increases from 2.00 to 2.07 compared with the original spectral determinant coefficient (R _ 2P). The results show that proper pretreatment method can improve the precision of soil organic matter detection model. Because of the large amount of soil spectral data and a large amount of redundant information, the detection model based on full spectrum method has high complexity and large amount of computation. Therefore, the quantitative analysis model of soil organic matter is simplified by extracting characteristic wavelengths from the spectrum. In this paper, the methods of interval partial least square method, information free variable elimination, continuous projection algorithm, competitive adaptive re-weighted sampling, genetic algorithm, ant colony optimization algorithm and particle swarm optimization algorithm are proposed to extract the characteristic wavelength modeling in the spectrum. The results show that 26 wavelength PLS models extracted by particle swarm optimization algorithm have the best prediction effect. The R2P of R2P is 0.81g RPD is 2.31. By selecting characteristic wavelength, the detection model is simplified and the calculation time is shortened. A portable instrument for detecting soil organic matter based on spectral characteristic wavelength is developed. The prototype of the instrument is designed and made with modularized hardware equipment. According to the hardware characteristics of the spectrometer, the rapid detection software of soil organic matter based on ARM-Linux embedded system is developed with Java programming language. The software has a good interactive interface and complete function. The actual R2P of 20 samples detected by this instrument was 0.78 and RPD was 1.74. According to the Technical Specification for soil fertility investigation and grading in Zhejiang Province, the classification accuracy of soil organic matter content was 85% according to the standard of organic matter classification. After further improvement, the prototype can be applied to soil organic matter classification and field use.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S151.95

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