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作 者:王树文[1] 赵越[1] 王丽凤[1] 王润涛[1] 宋玉柱[1] 张长利[1] 苏中滨[1]
机构地区:[1]东北农业大学电气与信息学院,哈尔滨150010
出 处:《农业工程学报》2016年第20期187-194,共8页Transactions of the Chinese Society of Agricultural Engineering
基 金:国家"863"项目(AA2013102303);黑龙江省博士后科研启动基金项目(LBH-Q13022);黑龙江省自然科学基金面上项目(C2015006);哈尔滨市科技创新人才项目(2015RQQXJ020)
摘 要:为快速、无损和准确地诊断水稻营养状况,开展了基于高光谱成像技术的寒地水稻叶片氮素含量预测研究。以不同施氮水平下的水稻叶片为研究对象,利用高光谱成像技术,分析拔节期水稻叶片光谱,采用全波段高光谱数据、连续投影算法及分段主成分分析(segmented principal components analysis,SPCA)与相关分析(correlation analysis,CA)相结合的方法建立多种回归分析模型,并对模型进行检验和筛选。结果表明:随着施氮水平提高,水稻叶片反射率在可见光区域降低,在近红外区域升高。在校正集决定系数上,基于多元逐步回归分析的全波段模型较好,校正集决定系数为0.821,校正集均方根误差RMSEC=0.079;在预测集决定系数上,基于SPCA-CA结合多元回归分析的多变量单波段指数、差值指数、双差值指数模型较好,预测集决定系数为0.869,预测集均方根误差RMSEP=0.085。该研究结果为快速检测水稻叶片氮素含量及水稻生长期间精确施肥管理提供了参考。In this paper,in order to realize the quick,non-destructive and accurate diagnosis of rice nutritional status,we use hyperspectral imaging techniques as an approach for nitrogen content prediction of rice leaves in cold region.The experiments were carried out for two years(2014 and 2015) at Fangzheng country,Heilongjiang province,China.Longdao 20 was chosen as the test cultivar.6 nitrogen fertilization rates were applied in our experiments,i.e.,NO(0 kg/hm^2),Nl(60 kg/hm^2),N2(90 kg/hm^2),N3(120 kg/hm^2),N4(150 kg/hm^2),and N5(180 kg/hm^2).The hyperspectral reflectance and nitrogen content of rice leaves under different nitrogen levels at jointing stage were separately measured using American Headwall imaging spectrometer and German AA3 analyzer.The hyperspectral images of 240 rice leaf samples in the spectral range of400-1 000 nm were acquired.Average spectrum was extracted from the region of interest(ROI) of each sample.Several regression analysis(RA) estimate models have been built based on different characteristic spectral parameters using different algorithms which include successive projections algorithm(SPA) and segmented principal components analysis(SPCA)combined with correlation analysis(CA) for testing and screening.The first method,a nitrogen content value estimation model based on multiple stepwise regression analysis(MSRA) in the whole wavelength region of 400~1000nm has been built and been predicted.Wavelengths 899,890 nm were retained as the model independent variables.The second method,8characteristic wavelengths i.e.,454,460,475,504,525,685,700 and 735 nm were chosen by SPA and selected as modeling variables of MSRA.Wavelengths 735,525 nm were chosen as the model independent variables.The third method,we divided the whole wavelength into 5 parts which are 400-504,505-670,671-697,698-724 and 725-1 000 nm using correlation coefficient matrix method.The principal component analysis(PCA) was carried out on each part,and 7 sensitive bands were se
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