發(fā)布時(shí)間：2018-04-06 20:38

  本文選題：實(shí)測(cè)光譜　切入點(diǎn)：光譜識(shí)別　出處：《新疆大學(xué)》2014年碩士論文

【摘要】：21世紀(jì)人類將步入信息社會(huì)，要求農(nóng)業(yè)由傳統(tǒng)向現(xiàn)代的轉(zhuǎn)變，經(jīng)營(yíng)方式由粗放型向集約型轉(zhuǎn)變。當(dāng)前，我國(guó)正面臨著越來越尖銳的資源與環(huán)境問題，為保障13億人口的食物安全，推動(dòng)農(nóng)業(yè)科學(xué)技術(shù)的發(fā)展、實(shí)施可持續(xù)發(fā)展戰(zhàn)略勢(shì)在必行。地界學(xué)的專家也認(rèn)識(shí)到“數(shù)字地球戰(zhàn)略”將是推動(dòng)我國(guó)信息化建設(shè)和社會(huì)經(jīng)濟(jì)、資源環(huán)境可持續(xù)發(fā)展的重要武器，發(fā)展現(xiàn)代農(nóng)業(yè)始終成為農(nóng)業(yè)可持續(xù)發(fā)展的必由之路。 農(nóng)作物類型的識(shí)別和作物某些參數(shù)(如葉綠素、葉面積指數(shù)等)及產(chǎn)量的估測(cè)在農(nóng)業(yè)生產(chǎn)中非常重要；由于人類環(huán)境在作物生長(zhǎng)過程中的影響越來越大，適時(shí)快速檢測(cè)作物類型及作物葉綠素含量顯得愈加重要；高光譜技術(shù)通過對(duì)作物光譜反射率及其與葉綠素含量關(guān)系的研究分析已經(jīng)成為作物監(jiān)測(cè)及估測(cè)的強(qiáng)有力工具，并且其也是現(xiàn)代農(nóng)業(yè)研究的一個(gè)方面。 經(jīng)濟(jì)作物生產(chǎn)是高效益型的農(nóng)業(yè)經(jīng)濟(jì)產(chǎn)業(yè)，一個(gè)區(qū)域的經(jīng)濟(jì)作物生產(chǎn)絕大部分參與市場(chǎng)流通，農(nóng)民衣住行的費(fèi)用都是從經(jīng)濟(jì)作物的收益中獲取。自黨的十一屆三中全會(huì)提出的“決不放松糧食生產(chǎn)，積極發(fā)展多種經(jīng)營(yíng)”方針以來，各地區(qū)都對(duì)農(nóng)業(yè)結(jié)構(gòu)進(jìn)行調(diào)整，其中，經(jīng)濟(jì)作物所占比例有所增大，農(nóng)民收入及農(nóng)業(yè)生產(chǎn)也得到提高與發(fā)展。在工業(yè)化、城鎮(zhèn)化深入發(fā)展中同步推進(jìn)農(nóng)業(yè)現(xiàn)代化，發(fā)展現(xiàn)代農(nóng)業(yè)，提高農(nóng)業(yè)發(fā)展的科技含量，是十二五時(shí)期農(nóng)業(yè)生產(chǎn)的一項(xiàng)重大任務(wù)。因此，利用光譜技術(shù)對(duì)經(jīng)濟(jì)作物進(jìn)行研究具有實(shí)際意義。 本論文利用美國(guó)ASD（analytical spectral device)公司的ASD FieldSpecPro光譜儀和SPAD·502便攜式葉綠素儀于2012年5-9月測(cè)量了研究區(qū)三種經(jīng)濟(jì)作物的高光譜反射率與葉綠素含量，分析了打瓜、甜菜和葫蘆的光譜特征；采用基于主成分分析的BP神經(jīng)網(wǎng)絡(luò)技術(shù)及主成分分析的Fisher判別模型對(duì)三種作物進(jìn)行光譜識(shí)別研究并進(jìn)行對(duì)比；基于特征光譜及植被指數(shù)對(duì)葉綠素含量進(jìn)行了預(yù)估，并對(duì)作物進(jìn)行了簡(jiǎn)單估產(chǎn)研究。得到了以下研究結(jié)果： 1、將作物實(shí)測(cè)光譜數(shù)據(jù)進(jìn)行預(yù)處理，然后利用主成分分析對(duì)其進(jìn)行降維處理，提取到5個(gè)主成分，其累計(jì)貢獻(xiàn)率達(dá)99.79%，能很好解釋原始光譜的全部信息；以5個(gè)主成分得分值為輸入變量，作物種類為輸出變量，建立PCA-BP模型和PCA-FDA模型，前者擬合殘差為5.2156×10-6，對(duì)15個(gè)未知樣本的識(shí)別率達(dá)到100%，后者待判樣本回帶驗(yàn)證率及檢驗(yàn)樣本識(shí)別率均達(dá)100%；PCA-BP模型是通過不斷調(diào)整隱含層的節(jié)點(diǎn)數(shù)來優(yōu)化模型結(jié)構(gòu)以及其人為設(shè)定預(yù)測(cè)結(jié)果中作物區(qū)分的界限偏差，使得受主觀因素影響較大，而PCA-FDA模型操作簡(jiǎn)單明了且客觀性強(qiáng)、識(shí)別結(jié)果更科學(xué)。 2、打瓜葉綠素含量與所選光譜指數(shù)相關(guān)性均達(dá)到極顯著水平，其中以MSAVI2與打瓜葉綠素含量相關(guān)性(R2=0.82)最好；且以其為自變量，葉綠素含量為因變量建立的三項(xiàng)式函數(shù)模型： Y=393.91x3-580.68x2+327.49x-4.1198精度最高（R2=0.8066），成為打瓜葉綠素含量最佳估算模型。葫蘆葉綠素含量估算中，基于RNDVI的葉綠素估算模型的確定性系數(shù)R2為0.7376，RMSE為1.0249，優(yōu)于其他植被指數(shù)模型；較于前者，基于主成分的對(duì)數(shù)模型預(yù)測(cè)值確定性系數(shù)R2為0.8201，RMSE為0.9126，為最佳葉綠素估算模型。 3選取的植被指數(shù)中，甜菜產(chǎn)量與VARI在各生育期相關(guān)性最好，均達(dá)到顯著性水平，相關(guān)系數(shù)最大的為塊根膨大期（0.8306），與其次為葉叢繁茂期（0.8107），苗期（0.8076），，糖分累積期（0.8015）；VARI與甜菜產(chǎn)量在4個(gè)生育期建立的回歸方程相關(guān)系數(shù)均達(dá)到極顯著相關(guān)水平，塊根膨大期一元三次回歸方程RMSE最小為0.0882，單時(shí)相估產(chǎn)精度均高；四個(gè)生育期復(fù)合回歸模型檢驗(yàn)R2與RMSE分別為0.960和0.127，精度最高，估算效果最好。
[Abstract]:In twenty-first Century, mankind will enter the information society, the requirement of agricultural transformation from traditional to modern, changing the operating mode from extensive to intensive. At present, our country is facing the problems of environment and resources is more and more sharp, 1 billion 300 million of the population for the protection of food safety, promote the development of agricultural science and technology, it is imperative to implement the strategy of sustainable development. Border studies are also aware of the "Digital Earth" strategy will be to promote the informationization construction and the social economy in our country, an important weapon for the sustainable development of resources, the development of modern agriculture has become the only way which must be passed to sustainable agricultural development.
Some parameters identification and crop crop types (such as chlorophyll, leaf area index) and yield estimation is very important in agricultural production; due to human environmental impact in the crop growth process more and more timely, rapid detection of crop types and crop chlorophyll content becomes more and more important; hyperspectral technology has become a powerful and crop monitoring the estimation tool through the research and Analysis on crop spectral reflectance and its relationship with chlorophyll content, and is also one aspect of the modern agricultural research.
Economic crop production is high benefit agricultural economy industry, production of a regional economic crop in the vast majority of farmers participate in the market circulation, housing bank charges are obtained from the economic crop income. Since the party's proposed the third Plenary Session of the 11th CPC Central Committee "no grain production, has been actively developing a diversified economy" policy, by region all the adjustment of agricultural structure, the economic crop proportion increased, the income of the farmers and agricultural production has been improved with the development in industrialization, urbanization development in promoting the modernization of agriculture, the development of modern agriculture, improving agricultural science and technology development, is an important task for agricultural production in 12th Five-Year so period. The study of economic crops, which has practical significance to use spectral techniques.
In this paper, using the ASD (analytical spectral device), ASD FieldSpecPro and SPAD 502 spectrometer portable chlorophyll meter measuring the three economic crops in the study area of high spectral reflectance and chlorophyll content in 2012 5-9 months, analyzed the spectral characteristics of melon, sugar beet and gourd; the study on the evaluation model of spectral recognition on three kinds of crops and compared the BP neural network technology of principal component analysis and principal component based on Fisher; spectral characteristics and vegetation index were estimated based on the content of chlorophyll, and the crops were a simple estimation research. The following results were obtained:
1, the crop measured spectral data preprocessing, then use principal component analysis to reduce the dimension of the extracted 5 main components, the cumulative contribution rate of 99.79%, can well explain all information of the original spectrum; the 5 principal component score values as input variables, output variables for crop types the establishment of PCA-BP model, and PCA-FDA model, the fitting error is 5.2156 * 10-6, the identification of 15 unknown samples reached 100%, the latter to be sentenced to the sample with verification test sample rate and recognition rate reached 100%; the PCA-BP model is through continuous adjustment and optimization of model structure and its prediction result set in crop distinction the number of hidden layer nodes deviation, which is affected by the subjective factors, while the PCA-FDA model is simple and has strong objectivity, the recognition result is more scientific.
2, the chlorophyll content of watermelon and the selected spectral index correlation reached significant level, the correlation between MSAVI2 and chlorophyll content of Watermelon (R2=0.82) and its best; as independent variables, the chlorophyll content for three type variables to establish function model: Y=393.91x3-580.68x2 +327.49x-4.1198 (R2=0.8066), the highest precision become the best models for the estimation of chlorophyll content of watermelon estimation of chlorophyll content. RNDVI gourd, chlorophyll estimation model of the deterministic coefficient of R2 is based on 0.7376, RMSE was 1.0249, better than other vegetation index model; compared with the former, the prediction model of principal component values of the logarithmic deterministic coefficient R2 is based on the 0.8201, RMSE is 0.9126, the best for chlorophyll estimation model.
3 selection of vegetation index, yield and VARI of sugar beet best in different growth period are significant correlation, correlation coefficient, the maximum period of tuber enlargement (0.8306), and then leaves lush growing stage (0.8107), (0.8076), seedling sugar accumulation period (0.8015); the correlation coefficients of regression equation and VARI beet yield in the 4 stages of the establishment have reached a significant level, the period of tuber enlargement, one of the three regression equations of RMSE minimum is 0.0882, while the yield estimation accuracy is higher; the four stage composite regression model verification of R2 and RMSE were 0.960 and 0.127, the highest precision, the best estimate.

【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：O433;S31

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