本文關(guān)鍵詞：基于高光譜技術(shù)的煙草含氮化合物估測模型研究　出處：《山東農(nóng)業(yè)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 高光譜 葉綠素 總氮 煙堿 估測模型

【摘要】：煙草氮素是影響其內(nèi)在質(zhì)量最為重要的營養(yǎng)元素,準(zhǔn)確及時(shí)的煙草氮素營養(yǎng)判斷是保證氮肥合理施用、保證煙葉質(zhì)量的基本前提。研究快速、無損的煙株氮素營養(yǎng)信息提取方法,對于準(zhǔn)確判斷煙株氮素營養(yǎng)水平,指導(dǎo)煙田氮素管理決策,促進(jìn)氮肥的合理施用,實(shí)現(xiàn)煙株正常生長發(fā)育和煙田生態(tài)系統(tǒng)的健康發(fā)展均具有重要的理論和現(xiàn)實(shí)意義。本研究選擇烤煙相對集中的產(chǎn)區(qū),在自然環(huán)境(自然光源、自然噪聲環(huán)境等)下,研究高光譜特征參數(shù)與烤煙煙葉總氮含量的相關(guān)關(guān)系,并建立估測模型,預(yù)測烤煙葉片總氮、煙堿含量,及時(shí)獲取烤煙生育過程中總氮、煙堿含量變化信息,為實(shí)現(xiàn)大面積遙感預(yù)測烤煙氮素營養(yǎng)豐缺評估、指導(dǎo)烤煙追肥奠定基礎(chǔ)。主要研究結(jié)果如下:1)煙草冠層葉綠素含量估測模型:利用煙草冠層在663nm、1173 nm和1670nm處反射光譜的一階微分作自變量,得到葉綠素含量估測模型Y=25.640+6487.507×R’663-20299.859×R’1670-13751.203×R’1173,決定系數(shù)達(dá)0.602�；谖恢米兞亢兔娣e變量所建的葉綠素估測模型預(yù)測精度高于一階微分和植被指數(shù)兩種參數(shù)所建模型,并且以紅邊幅值和紅邊面積為自變量所建模型Y=30.732+11195.95×Dr-256.608×SDr,模型決定系數(shù)為0.839;經(jīng)檢驗(yàn),葉綠素實(shí)測值與估測值相關(guān)系數(shù)為0.916,RMSEP為1.6449,模型精度較好,可以預(yù)測現(xiàn)蕾期煙葉葉綠素含量。2)煙草葉片葉綠素含量估測模型:以現(xiàn)蕾期高光譜特征值λ682、λ494、λ1932為自變量建立的多元線性估測模型為Y=44.27-2233.55λ682+2721.33λ494-61.56λ1932,F值38.5247,p值為0.0000,決定系數(shù)R2=0.5463。利用實(shí)測值對估測模型進(jìn)行檢驗(yàn),擬合方程的預(yù)測值與實(shí)測值相關(guān)系數(shù)為0.8354;RMSEP為3.8209;RE為8.92%;模型具有良好的擬合效果,決定系數(shù)R2達(dá)到0.6980。3)煙草葉片總氮含量估測模型:以NDVI(573,440)建立的單變量總氮含量估測模型Y=9.8752X NDVI(573,440)^-1.1849,決定系數(shù)R2=0.6756;擬合方程的RMSEP為3.1926;RE為11.78%。模型精度較好,可以用來預(yù)測現(xiàn)蕾期煙葉總氮含量。以高光譜特征值SDr/SDb、NDVI(573,440)、NDVI(660,440)、NDSI(FD700,FD690)為自變量建立的多元估測模型優(yōu)選Y=30.5397+14.0959xNDSI(FD700,FD690)-32.0771x NDVI(573,440)+5.2260xNDVI(660,440)+0.9540x SDr/SDb,估測模型的R2=0.631;擬合方程預(yù)測值與實(shí)測值的RMSEP為7.5077;RE為33.87%,模型精度較好。4)煙草葉片煙堿含量估測模型以現(xiàn)蕾期高光譜參量(SDr-Sy)/(SDr+Sy)建立單變量估測模型Y=2.2427/(1+EXP(6.1463-4.7476x(SDr-Sy)/(SDr+Sy)))預(yù)測煙葉煙堿含量,其值與實(shí)測值的相關(guān)系數(shù)達(dá)到0.8148,均方根誤差(RMSEP)為0.2140,相對誤差(RE%)15.47;用估測模型Y=-4.2628+5.8974x(SDr-Sy)/(SDr+Sy)-1.3260x(SDr-Sy)/(SDr+Sy)2預(yù)測煙葉煙堿含量,其值與實(shí)測值的相關(guān)系數(shù)達(dá)到0.7955,均方根誤差(RMSEP)為0.2272,相對誤差(RE%)14.42。多元估測模型方程Y=-0.4753+1.6982x(SDr-Sy)/(SDr+Sy)-0.1616x Rg/Ro預(yù)測的煙葉煙堿含量,與實(shí)測值的相關(guān)系數(shù)達(dá)到0.8841;預(yù)測方程的均方根誤差(RMSEP)為0.2335,相對誤差(RE%)15.47。經(jīng)過模型檢驗(yàn),現(xiàn)蕾期建立的單變量估測模型Y=2.2427/(1+EXP(6.1463-4.7476 x(SDr-Sy)/(SDr+Sy)))、Y=-4.2628+5.8974x(SDr-Sy)/(SDr+Sy)-1.3260x(SDr-Sy)/(SDr+Sy)2和多變量估測模型Y=-0.4753+1.6982x(SDr-Sy)/(SDr+Sy)-0.1616x Rg/Ro都可用于現(xiàn)蕾期煙草葉片煙堿的預(yù)測。
[Abstract]:Tobacco nitrogen is the most important nutritional element affecting its internal quality. Accurate and timely nitrogen nutrition judgement is the basic premise to ensure the rational application of nitrogen fertilizer and ensure the quality of tobacco leaves. Study on rapid and nondestructive tobacco nitrogen nutrition information extraction method, the accurate judgement of tobacco nitrogen level, nitrogen in tobacco field guide management decisions, promote the rational application of nitrogen fertilizer, which has important theoretical and practical significance to realize the healthy development of the normal growth of tobacco and tobacco field ecosystem. This research chooses the relative concentration of the flue-cured tobacco producing areas, in the natural environment (natural light and natural noise environment), correlation between the total nitrogen content of flue-cured tobacco and high spectral characteristic parameters, and establish the estimation model of prediction of total nitrogen and nicotine content of flue-cured tobacco leaves, the contents of total nitrogen and nicotine to obtain timely change information from the growth of tobacco in the process, lay the foundation for the realization of large area remote sensing prediction of tobacco nitrogen nutrient abundance assessment, guidance of topdressing for tobacco. The main results are as follows: 1) estimation of Canopy Chlorophyll content of tobacco model: using tobacco canopy as independent variables in first-order differential 663nm, 1173 nm and 1670nm reflectance spectra, obtained the chlorophyll content estimation model of Y=25.640+6487.507 * R '663-20299.859 * R' 1670-13751.203 * R '1173, decision coefficient was 0.602. The position variable and variable area chlorophyll estimation model of the prediction accuracy is higher than the first-order differential vegetation index and two parameters based on the model, and the red edge amplitude and area of red edge as the independent model of Y=30.732+11195.95 * Dr-256.608 * SDr model, the coefficient of determination was 0.839; after the examination, and estimate the value of the correlation coefficient is 0.916 the measured values of chlorophyll, RMSEP is 1.6449, the accuracy of the model is good, can predict the chlorophyll content of budding leaf. 2) the estimation model of chlorophyll content in tobacco leaves: the multivariate linear estimation model based on the hyperspectral eigenvalues of lambda, lambda 494, and lambda 1932 as Y=44.27-2233.55, 682+2721.33, lambda, 494-61.56 and 1932, F 38.5247, P value 0, and determination coefficient R2=0.5463. The estimated model is tested by the measured value. The correlation coefficient between the predicted value and the measured value of the fitting equation is 0.8354, RMSEP is 3.8209, RE is 8.92%, the model has good fitting effect, and the determination coefficient R2 reaches 0.6980. 3) the estimation model of total nitrogen content in tobacco leaves: the Y=9.8752X NDVI (573440) ^-1.1849, the coefficient of determination R2=0.6756, the RMSEP of the fitting equation is 3.1926, and the RE is 11.78%, based on the total variable nitrogen content of NDVI (573440). The precision of the model is good, and it can be used to predict the total nitrogen content of tobacco leaves at the bud stage. SDr/SDb and NDVI with high spectral characteristic value (573440), NDVI (660440), NDSI (FD700, FD690) as the independent variable to establish the estimation model of multivariate optimization Y=30.5397+14.0959xNDSI (FD700, FD690) -32.0771x NDVI (573440) +5.2260xNDVI (660440) +0.9540x SDr/SDb, the R2=0.631 estimation model; fitting equation of the predicted value and the measured value of RMSEP 7.5077; RE is 33.87%, the accuracy of the model is good. 4) the nicotine content of tobacco leaves estimation model at bud hyperspectral parameter (SDr-Sy) / (SDr+Sy) a single variable estimation model of Y=2.2427/ (1+EXP (6.1463-4.7476x (SDr-Sy) / (SDr+Sy))) to predict nicotine content, the correlation coefficient value and the measured value reached 0.8148, the root mean square error (RMSEP) for 0.2140, the relative error (RE%) estimation model with 15.47; Y=-4.2628+5.8974x (SDr-Sy) / (SDr+Sy) -1.3260x (SDr-Sy) / (SDr+Sy) 2 Prediction of nicotine content, the correlation coefficient value and the measured value reached 0.7955, both Fang Genwu (RMSEP) was 0.2272, the relative error (RE%) 14.42. Multivariate prediction model equation Y=-0.4753+1.6982x (SDr-Sy) / (SDr+Sy) -0.1616x Rg/Ro predicted nicotine content in tobacco leaves, and the correlation coefficient between the measured values and the measured values reached 0.8841. The root mean square error (RMSEP) of the prediction equation was 0.2335, and the relative error (RE%) 15.47. After the model test, the single variable Y=2.2427/ model to estimate the squaring period established (1+EXP (6.1463-4.7476 x (SDr-Sy) / (SDr+Sy))), Y=-4.2628+5.8974x (SDr-Sy) / (SDr+Sy) -1.3260x (SDr-Sy) / (SDr+Sy) and 2 multivariate estimation model of Y=-0.4753+1.6982x (SDr-Sy) / (SDr+Sy) -0.1616x Rg/Ro can be used to prediction of the budding period of nicotine in tobacco leaves.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：S572

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