基于高光譜和圖像技術(shù)的蘋果葉片葉綠素和磷素含量估測研究
發(fā)布時(shí)間：2018-08-17 14:27
【摘要】：葉綠素(Chlorophyll)和磷素(Phosphorus)是蘋果樹生長發(fā)育的重要營養(yǎng)元素,是作物健康生長和影響產(chǎn)量的物質(zhì)基礎(chǔ)。傳統(tǒng)的葉綠素和磷素測定方法,多為實(shí)驗(yàn)室化驗(yàn)分析法,結(jié)果雖較為準(zhǔn)確,但存在費(fèi)時(shí)、費(fèi)力的缺點(diǎn)。近年來發(fā)展起來的高光譜遙感和圖像分析等無損檢測技術(shù),因其具有方便快捷的優(yōu)點(diǎn),能夠?qū)崿F(xiàn)無損、快速和準(zhǔn)確地估測植物營養(yǎng)狀況,對提高蘋果樹的信息化管理具有重要的技術(shù)指導(dǎo)與實(shí)踐意義。本研究以山東煙臺棲霞和臨沂蒙陰蘋果園為研究區(qū),以紅富士蘋果葉片為研究對象,分別于2014年和2015年5月(新梢旺長期)前后進(jìn)行樣本采集和試驗(yàn)數(shù)據(jù)測定。利用ASD Field Spec 4地物光譜儀和數(shù)碼相機(jī)分別獲取蘋果葉片的光譜反射值和圖像,在實(shí)驗(yàn)室里利用化學(xué)分析方法測定葉片葉綠素含量和磷素含量。通過數(shù)據(jù)分析,得到了蘋果葉片磷素含量的原始光譜反射率的響應(yīng)規(guī)律和相關(guān)關(guān)系,又對原始光譜反射率進(jìn)行一階微分變換,得到了一階微分形式的響應(yīng)規(guī)律和相關(guān)關(guān)系;構(gòu)建并篩選了磷素含量的高光譜特征參量,建立了磷素含量的估測模型。在進(jìn)行圖像分割與顏色值的獲取基礎(chǔ)上,分析了蘋果葉片葉綠素含量與RGB顏色參數(shù)的相關(guān)關(guān)系,篩選出了影響葉綠素含量的核心顏色參數(shù),建立了葉綠素含量的估測模型。主要結(jié)果有:(1)得到了蘋果葉片磷素含量的高光譜敏感波段。經(jīng)過相關(guān)分析,蘋果葉片磷素含量與350~2500 nm波段的高光譜反射率的呈顯著負(fù)相關(guān)特性,其中,藍(lán)光(521~568 nm)、紅光(697~736 nm)、近紅外(1347~1878 nm和2022~2400 nm)波段是磷素含量的敏感波段,其中在R1720處取得最高的相關(guān)系數(shù)r=-0.6485。(2)篩選出了不同葉綠素含量的蘋果葉片核心顏色參數(shù)。經(jīng)過蘋果葉片圖像直方圖分析,構(gòu)建并篩選了葉片葉綠素含量與RGB顏色系統(tǒng)的核心顏色參數(shù),分別為B值、B/R、B/G、G/(R+G+B)、B/(R+G+B)、(R-B)/(R+B)、(GB)/(G+B)、(R-B)/(R+G+B)、(G-B)/(R+G+B)。(3)建立了蘋果葉片磷素含量的估測模型。經(jīng)比較分析,蘋果葉片磷素含量的最佳估測模型為基于主要植被指數(shù)的變量組合(DVI(556,712),DVI(677,1728),RVI(542,1094),RVI(705,937),DVI(FDR567,FDR1980)基于高光譜和圖像技術(shù)的蘋果葉片營養(yǎng)狀況估測研究NDVI(937,549),和DVI(FDR523,FDR1883))建立的隨機(jī)森林模型,其估測模型的決定系數(shù)R2=0.9236,均方根誤差RMSE=0.0158,相對誤差為RE=6.9150%。(4)建立了蘋果葉片不同葉綠素含量估測模型�；诿舾蓄伾珔�(shù)B值、B/R、B/G、G/(R+G+B)、B/(R+G+B)、(R-B)/(R+B)、(G-B)/(G+B)、(RB)/(R+G+B)、(G-B)/(R+G+B)建立的葉綠素含量的支持向量機(jī)模型(SVM),對Chl.a、Chl.b、Chl.(a+b)和SPAD估測的決定系數(shù)R2分別為0.8754、0.8374、0.8671和0.8129,均方根誤差RMSE分別為0.0194、0.0350、0.0497和0.9281,相對誤差RE分別為0.8059%、1.7540%、1.122%和1.1894%,模型評估指標(biāo)均通過了P=0.01極顯著性檢驗(yàn)水平,尤其是對Chl.a的估測效果最佳。
[Abstract]:Chlorophyll (Chlorophyll) and phosphate (Phosphorus) are important nutrient elements for apple tree growth and development. The traditional methods for the determination of chlorophyll and phosphorus are mostly laboratory analytical methods. Although the results are more accurate, they have the disadvantages of time-consuming and laborious. Non-destructive testing techniques, such as hyperspectral remote sensing and image analysis, have been developed in recent years. Because of their convenient and rapid advantages, they can be used to estimate the nutritional status of plants quickly, quickly and accurately. It has important technical guidance and practical significance for improving the information management of apple trees. In this study, Qixia apple orchard in Yantai, Shandong Province and Mengyin apple orchard in Linyi were used as the research area, and the leaves of Red Fuji apple were used as the research objects. The samples were collected and the experimental data were measured before and after May 2014 and May 2015. The spectral reflectance and image of apple leaves were obtained by ASD Field Spec 4 ground object spectrometer and digital camera respectively. The chlorophyll content and phosphorus content in leaves were determined by chemical analysis method in laboratory. Through the data analysis, the response law and the correlation relation of the original spectral reflectance of apple leaf phosphorus content were obtained, and the first order differential transformation of the original spectral reflectance was carried out, and the response law and the correlation relation of the first order differential form were obtained. The hyperspectral characteristic parameters of phosphorus content were established and the estimation model of phosphorus content was established. On the basis of image segmentation and color value acquisition, the correlation between chlorophyll content and RGB color parameters of apple leaves was analyzed, the core color parameters affecting chlorophyll content were screened out, and the estimation model of chlorophyll content was established. The main results were as follows: (1) the hyperspectral sensitive band of phosphorus content in apple leaves was obtained. The correlation analysis showed that the phosphorus content in apple leaves was negatively correlated with the hyperspectral reflectance at 350 ~ 2500nm. The blue (521 ~ 568 nm),) red (697 ~ 736 nm),) near infrared (1347 ~ 1878 nm and 2022 ~ 2 400 nm) bands were sensitive to phosphorus content. The highest correlation coefficient was obtained at R1720. (2) the core color parameters of apple leaves with different chlorophyll content were selected. Based on histogram analysis of apple leaf image, the core color parameters of leaf chlorophyll content and RGB color system were constructed and screened. The estimation model of P content in apple leaves was established as B value B / R / (R G B) B / (R G B), (R B) / (R B), (GB) / (G B), (R-B / (R G B), (G-B) / (R G B). (3). By comparative analysis, the optimal estimation model of phosphorus content in apple leaves was established as a random forest model based on the variable combination of main vegetation index (DVI (556712) and RVI (572 / 1094) / RVI (705937) DVI (FDR567N / FDR1980), NDVI (937549) and DVI (FDR523UFDR1883) based on hyperspectral and image techniques. The determination coefficient of the estimation model is R2 + 0.9236, the root mean square error (RMSE) is 0. 0158, and the relative error is RET 6. 9150. (4) the estimation model of different chlorophyll content in apple leaves has been established. A support vector machine model based on the sensitive color parameter B / B / (R G B) / (R G B), (G-B / (G B), (RB) / (R G B), (G-B) / (R G B) was established to estimate the determination coefficients of Chl. (a b) and SPAD for Chl. Chl. (a b) and SPAD were 0.87540.83740.8671 and 0.8129, respectively. The root mean square error (RMSE) was 0.01943.500.0497 and 0.9281, respectively.The mean square error (RMSE) was 0.01943500.0497 and 0.9281respectively, and the determination coefficients of (a b) and SPAD were 0.87540.83740.8671 and 0.8129, respectively. The mean square error (RMS) was 0.01943500.0497 and 0.9281respectively. For the error RE of 0.80590.75400.122% and 1.1894%, the model evaluation index has passed the P0.01 significant test level. In particular, the estimation of Chl.a is the best.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S661.1
本文編號：2187927
[Abstract]:Chlorophyll (Chlorophyll) and phosphate (Phosphorus) are important nutrient elements for apple tree growth and development. The traditional methods for the determination of chlorophyll and phosphorus are mostly laboratory analytical methods. Although the results are more accurate, they have the disadvantages of time-consuming and laborious. Non-destructive testing techniques, such as hyperspectral remote sensing and image analysis, have been developed in recent years. Because of their convenient and rapid advantages, they can be used to estimate the nutritional status of plants quickly, quickly and accurately. It has important technical guidance and practical significance for improving the information management of apple trees. In this study, Qixia apple orchard in Yantai, Shandong Province and Mengyin apple orchard in Linyi were used as the research area, and the leaves of Red Fuji apple were used as the research objects. The samples were collected and the experimental data were measured before and after May 2014 and May 2015. The spectral reflectance and image of apple leaves were obtained by ASD Field Spec 4 ground object spectrometer and digital camera respectively. The chlorophyll content and phosphorus content in leaves were determined by chemical analysis method in laboratory. Through the data analysis, the response law and the correlation relation of the original spectral reflectance of apple leaf phosphorus content were obtained, and the first order differential transformation of the original spectral reflectance was carried out, and the response law and the correlation relation of the first order differential form were obtained. The hyperspectral characteristic parameters of phosphorus content were established and the estimation model of phosphorus content was established. On the basis of image segmentation and color value acquisition, the correlation between chlorophyll content and RGB color parameters of apple leaves was analyzed, the core color parameters affecting chlorophyll content were screened out, and the estimation model of chlorophyll content was established. The main results were as follows: (1) the hyperspectral sensitive band of phosphorus content in apple leaves was obtained. The correlation analysis showed that the phosphorus content in apple leaves was negatively correlated with the hyperspectral reflectance at 350 ~ 2500nm. The blue (521 ~ 568 nm),) red (697 ~ 736 nm),) near infrared (1347 ~ 1878 nm and 2022 ~ 2 400 nm) bands were sensitive to phosphorus content. The highest correlation coefficient was obtained at R1720. (2) the core color parameters of apple leaves with different chlorophyll content were selected. Based on histogram analysis of apple leaf image, the core color parameters of leaf chlorophyll content and RGB color system were constructed and screened. The estimation model of P content in apple leaves was established as B value B / R / (R G B) B / (R G B), (R B) / (R B), (GB) / (G B), (R-B / (R G B), (G-B) / (R G B). (3). By comparative analysis, the optimal estimation model of phosphorus content in apple leaves was established as a random forest model based on the variable combination of main vegetation index (DVI (556712) and RVI (572 / 1094) / RVI (705937) DVI (FDR567N / FDR1980), NDVI (937549) and DVI (FDR523UFDR1883) based on hyperspectral and image techniques. The determination coefficient of the estimation model is R2 + 0.9236, the root mean square error (RMSE) is 0. 0158, and the relative error is RET 6. 9150. (4) the estimation model of different chlorophyll content in apple leaves has been established. A support vector machine model based on the sensitive color parameter B / B / (R G B) / (R G B), (G-B / (G B), (RB) / (R G B), (G-B) / (R G B) was established to estimate the determination coefficients of Chl. (a b) and SPAD for Chl. Chl. (a b) and SPAD were 0.87540.83740.8671 and 0.8129, respectively. The root mean square error (RMSE) was 0.01943.500.0497 and 0.9281, respectively.The mean square error (RMSE) was 0.01943500.0497 and 0.9281respectively, and the determination coefficients of (a b) and SPAD were 0.87540.83740.8671 and 0.8129, respectively. The mean square error (RMS) was 0.01943500.0497 and 0.9281respectively. For the error RE of 0.80590.75400.122% and 1.1894%, the model evaluation index has passed the P0.01 significant test level. In particular, the estimation of Chl.a is the best.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S661.1
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 程立真;朱西存;高璐璐;李程;王凌;趙庚星;姜遠(yuǎn)茂;;基于RGB模型的蘋果葉片葉綠素含量估測[J];園藝學(xué)報(bào);2017年02期
2 程立真;朱西存;高璐璐;王凌;趙庚星;;基于隨機(jī)森林模型的蘋果葉片磷素含量高光譜估測[J];果樹學(xué)報(bào);2016年10期
3 岳學(xué)軍;全東平;洪添勝;Wei Xiang;劉永鑫;王健;;不同生長期柑橘葉片磷含量的高光譜預(yù)測模型[J];農(nóng)業(yè)工程學(xué)報(bào);2015年08期
4 何勇;彭繼宇;劉飛;張初;孔汶汶;;基于光譜和成像技術(shù)的作物養(yǎng)分生理信息快速檢測研究進(jìn)展[J];農(nóng)業(yè)工程學(xué)報(bào);2015年03期
5 劉紅玉;毛罕平;朱文靜;張曉東;高洪燕;;基于高光譜的番茄氮磷鉀營養(yǎng)水平快速診斷[J];農(nóng)業(yè)工程學(xué)報(bào);2015年S1期
6 高洪燕;毛罕平;張曉東;;生菜葉中磷含量的光譜定量分析[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2014年S1期
7 程洪;Lutz Damerow;Michael Blanke;孫宇瑞;;基于圖像處理與支持向量機(jī)的樹上蘋果早期估產(chǎn)研究[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2015年03期
8 苗騰;趙春江;郭新宇;陸聲鏈;溫維亮;;基于葉綠素相對值的植物葉片顏色模擬方法[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2014年08期
9 冀榮華;鄭立華;鄧小蕾;張瑤;李民贊;;基于反射光譜的蘋果葉片葉綠素和含水率預(yù)測模型[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2014年08期
10 劉艷麗;何紹蘭;呂強(qiáng);易時(shí)來;謝讓金;鄭永強(qiáng);劉雪峰;鄧烈;;柑橘花鉀素營養(yǎng)的高光譜表征[J];果樹學(xué)報(bào);2014年06期
相關(guān)碩士學(xué)位論文 前1條
1 黃木易;冬小麥條銹病害的高光譜遙感監(jiān)測[D];安徽農(nóng)業(yè)大學(xué);2004年
，本文編號：2187927
本文鏈接：http://sikaile.net/shoufeilunwen/zaizhiyanjiusheng/2187927.html
最近更新
教材專著
