發(fā)布時(shí)間：2018-03-05 17:42

  本文選題：葉綠素含量　切入點(diǎn)：SPAD值反演　出處：《華中農(nóng)業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：葉綠素是作物進(jìn)行光合作用有關(guān)的重要色素。實(shí)時(shí)準(zhǔn)確的獲取作物葉片的葉綠素水平,有利于掌握作物生長(zhǎng)狀況,從而確�？茖W(xué)的栽培和施肥,對(duì)提高作物產(chǎn)量,實(shí)現(xiàn)精準(zhǔn)農(nóng)業(yè)有著非常重要的意義。諸如SPAD儀等傳統(tǒng)無(wú)損測(cè)量方法不能全面獲取作物葉綠素含量的分布,基于光譜技術(shù)的葉綠素反演方法雖然已經(jīng)日趨成熟,但葉綠素在作物體內(nèi)的分布、變化卻缺少可視化的表達(dá),而及早地發(fā)現(xiàn)作物缺乏氮素初期引起的葉綠素性狀變化,將對(duì)實(shí)際的生產(chǎn)提供必要的幫助。因此本研究以生長(zhǎng)期的水稻地上部分為研究對(duì)象,提出一種基于消費(fèi)級(jí)近紅外相機(jī)的水稻葉片葉綠素分布獲取方法,將光譜-葉綠素反演模型映射到水稻可見(jiàn)光和近紅外圖像上,得到水稻葉片葉綠素的二維分布,由此能夠直觀的觀察水稻的生長(zhǎng)狀況,為快速、無(wú)損的低成本營(yíng)養(yǎng)診斷提供了更多參考。主要研究?jī)?nèi)容及結(jié)果如下:本研究通過(guò)對(duì)普通單反相機(jī)加載近紅外濾波片獲取水稻多波段三個(gè)通道光譜信息,篩選最優(yōu)近紅外波段圖像,建立水稻預(yù)測(cè)模型,將反演結(jié)果映射到水稻圖像上,實(shí)現(xiàn)水稻葉綠素含量的直觀描述。在篩選過(guò)程中,對(duì)比了單通道光譜信息和綜合通道信息對(duì)水稻葉綠素含量預(yù)測(cè)的影響,分析了不同植被指數(shù)和擬合模型對(duì)水稻葉綠素含量預(yù)測(cè)的效果,并比較了消費(fèi)級(jí)近紅外單反相機(jī)成像方式和高光譜相機(jī)成像方式在葉綠素反演不同方面的優(yōu)劣。結(jié)果表明,相機(jī)成像通道對(duì)于葉綠素含量的預(yù)測(cè)影響,R通道G通道B通道。以單一植被指數(shù)建立的單因子預(yù)測(cè)模型中,GVI預(yù)測(cè)精度最高,R2=0.7851,預(yù)測(cè)水稻葉綠素含量的最優(yōu)波段是可見(jiàn)光綠光波段G和近紅外760nm(NIR760R)的組合,擬合函數(shù)更接近二次曲線形式。多因子預(yù)測(cè)模型中,6個(gè)波段信息經(jīng)偏最小二乘回歸,R2=0.8541,4個(gè)由最優(yōu)波段構(gòu)造的植被指數(shù)建立的最小二乘支持向量機(jī)模型,R2=0.8314。將含有最優(yōu)波段的水稻光譜信息融入葉綠素反演模型,最后得到整個(gè)水稻葉片葉面的葉綠素分布,從而實(shí)現(xiàn)水稻營(yíng)養(yǎng)的準(zhǔn)確描述、定量分析和可視化表達(dá)�？偟脕�(lái)看,消費(fèi)級(jí)近紅外相機(jī)比高光譜相機(jī)更適用于在線監(jiān)測(cè)。
[Abstract]:Chlorophyll is an important pigment related to photosynthesis of crops. Obtaining the chlorophyll level of crop leaves in real time and accurately is beneficial to the understanding of crop growth, so as to ensure scientific cultivation and fertilization, so as to increase crop yield. It is very important to realize precision agriculture. Traditional nondestructive measurement methods such as SPAD instrument can not obtain the distribution of chlorophyll content in crops. Although the method of chlorophyll inversion based on spectral technology is becoming more and more mature, However, the distribution of chlorophyll in crops, the changes in the lack of visual expression, and early detection of crop nitrogen deficiency caused by the initial changes in chlorophyll traits, Therefore, a method of obtaining chlorophyll distribution in rice leaves based on consumer near infrared camera is proposed. The spectral chlorophyll inversion model is mapped to the visible light and near infrared images of rice, and the two-dimensional distribution of chlorophyll in rice leaves is obtained. Thus, the growth of rice can be observed intuitively. The main research contents and results are as follows: in this study, the three channels spectral information of rice was obtained by loading NIR filter into common SLR cameras. The optimal near infrared band images were screened, the rice prediction model was established, and the inversion result was mapped to the rice image to realize the direct description of rice chlorophyll content. The effects of single channel spectral information and integrated channel information on the prediction of chlorophyll content in rice were compared, and the effects of different vegetation indices and fitting models on the prediction of chlorophyll content in rice were analyzed. The advantages and disadvantages of consumer near infrared SLR camera imaging mode and hyperspectral camera imaging mode in chlorophyll inversion are compared. The influence of camera imaging channel on the prediction of chlorophyll content in R channel G channel B channel. In the single factor prediction model established by single vegetation index, the precision of GVI prediction is the highest (R2GVI) 0.7851.The optimum band for predicting chlorophyll content in rice is that the band can be used to predict chlorophyll content in rice. See the combination of light and green band G and NIR 760nmNIR760R), The fitting function is closer to the conic form. In the multifactor prediction model, the information of 6 bands is regressed by partial least squares regression (R2N) 0.8541, and the least squares support vector machine model (LS-SVM), which is established by vegetation index constructed from the optimal band, will contain the best information. The spectral information of rice in the band is integrated into the chlorophyll inversion model. Finally, the chlorophyll distribution of the whole rice leaf was obtained, so as to realize the accurate description, quantitative analysis and visual expression of rice nutrition. In general, the consumer near infrared camera is more suitable for on-line monitoring than the hyperspectral camera.
【學(xué)位授予單位】：華中農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O657.33;S511

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉仁杰;房俊龍;李民贊;孫紅;吳李p，

本文編號(hào)：1571257