基于分数阶微分算法的大豆冠层氮素含量估测研究  

Estimation of Canopy Nitrogen Content of Soybean Crops Based on Fractional Differential Algorithm

作　　者：张亚坤 罗斌[2,3] 潘大宇[2,3] 宋鹏 路文超[2,3] 王成 赵春江[1,2,3] ZHANG Ya-kun;LUO Bin;PAN Da-yu;SONG Peng;LU Wen-chao;WANG Cheng;ZHAO Chun-jiang(School of Electrical and Information,Northeast Agricultural University, Harbin 150030,China;Beijing Research Center of Intelligent Equipment for Agriculture,Beijing 100097,China;National Research Center of Intelligent Equipment for Agriculture,Beijing 100097,China)

机构地区：[1]东北农业大学电气与信息学院,黑龙江哈尔滨150030 [2]北京农业智能装备技术研究中心,北京100097 [3]国家农业智能装备工程技术研究中心,北京100097

出　　处：《光谱学与光谱分析》2018年第10期3221-3230,共10页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31601216);北京市农林科学院创新能力建设专项(储备性研究)(KJCX20170418)资助

摘　　要：氮素与作物的生长发育、产量和品质密切相关。作物冠层氮素含量的快速、准确、无损检测对于作物营养诊断和长势评估具有重要意义。传统的氮素检测方法检测周期长、操作复杂,同时具有破坏性,无法实现作物氮素含量在时间和空间上的连续动态监测。基于光谱遥感技术快速、无损地获取作物氮素含量是近年来作物组分快速检测研究的热点。当前的研究大多基于原始光谱或整数阶微分(一阶、二阶)预处理后的光谱进行氮素含量预测,原始光谱或整数阶微分预处理后的光谱会忽略光谱曲线间的渐变信息,影响氮素含量的预测准确度。与原始光谱和整数阶微分方法相比,分数阶微分算法在背景噪声去除、有效信息提取等方面较有优势。为研究分数阶微分预处理算法在作物氮素检测中的应用,本文以不同施肥处理下的盆栽大豆作物为研究对象,获取大豆苗期、花期、结荚期和鼓粒期四个生育期共256组冠层高光谱及对应的大豆冠层氮素含量(CNC)数据,运用分数阶微分算法对光谱数据进行0~2阶微分预处理,微分间隔为0.1,分别采用归一化光谱植被指数NDSI、比值光谱指数RSI对预处理后的光谱数据和大豆冠层氮素含量数据进行相关性分析,得到各阶微分预处理下NDSIα(α代表分数阶微分阶数)与大豆CNC,RSIα与大豆CNC相关系数绝对值的最大值及其对应的波段组合——最优波段组合NDSIα(opt)和RSIα(opt),采用线性回归方法,建立各阶微分下NDSIα(opt)与CNC,RSIα(opt)与CNC的预测模型,并与常用植被指数(VOGII,MTCI,DCNI,NDRE)建立的氮素含量预测模型进行比较,研究分数阶微分算法对大豆作物冠层氮素含量预测模型的效果。结果表明:(1)在0~2阶微分范围内,最优波段组合NDSIα(opt),RSIα(opt)与大豆CNC的相关系数随阶数增加呈现先升高后下降趋势。其中,0.8阶微分下NDSI0.8(R725,R769)与大豆CNC的�Nitrogen is one of the most important fertilizers and closely related to the growth,development,yield and quality of crops.Rapid,accurate and non-destructive assessment of nitrogen content in crops is critical for nutrition diagnosis and growth monitoring.Traditional detection methods of nitrogen content arecomplicated,time-consuming and destructive,which makes the continuous dynamic monitoring of nitrogen content in time and space impossible.It is a hot topic for rapid and non-destructive estimation of crop nitrogen content based onspectral remote sensing technology in recent years.Nevertheless,existing researches about the estimation of nitrogen content were mostly focused on the original or integer differential spectra(first order,second order).Some studiesindicated that the original or integer differential spectra might ignore the effective information,which would influence the estimation accuracy of nitrogencontent in crops.Fractional order differential algorithm has the advantages inbackground noise removal and effective information extraction compared with theinteger differential methods.Hence,the objective of this study was to explore the feasibility of detecting nitrogen in crops by fractional order differential algorithm.256 datasets,which were consisted of canopy spectral data and the relevant canopy nitrogen content(CNC)data,were collected during seedling,flowering,pod and drum stages in soybean plants.The plants were treated with different fertilizer components under pot conditions.0~2 order differentials of spectral data were computed by Grünwald-Letnikov fractional differentialequation with differential interval of 0.1.Afterwards,the correlation between the preprocessed spectra and soybean CNC under different fractional order differential were analyzed using the normalized difference spectral index(NDSI)andratio spectral index(RSI).The maximums of correlation coefficient between soybean CNC and NDSIα(αis the fractional differential order),and between soybean CNC and RSIαwere determined under ea

关 键 词：冠层氮素含量 高光谱数据 植被指数 分数阶微分算法 

分 类 号：O657.3[理学—分析化学]