本文選題：甜橙 + 葉片�。� 參考：《西南大學(xué)》2015年碩士論文

【摘要】：本文以15年生枳橙(Citrus. sinensis × Poncirus. trifoliata, "Carrizo Citrange")砧哈姆林甜橙(C.sinensis (L.), "Hamlin Sweet Orange")為對象,研究基于高光譜圖像信息的葉、花營養(yǎng)和成花能力的光譜預(yù)測方法,旨在為建立甜橙葉片和花器營養(yǎng)水平實(shí)時(shí)監(jiān)測與高效合理施肥管理技術(shù)提供依據(jù)。主要研究結(jié)果如下：1.哈姆林甜橙葉片營養(yǎng)的近紅外高光譜特征與預(yù)測研究(1)哈姆林甜橙植株花芽分化期當(dāng)年生春梢、夏梢和盛花期上一年春梢葉片450-1000nnm光譜平均反射率曲線呈現(xiàn)規(guī)律為：葉片成熟度越高,光譜曲線波動(dòng)越趨于平穩(wěn),即光譜曲線綠峰、紅谷、近紅外平臺區(qū)域光譜反射率間差值越小。(2)為消除或減小試驗(yàn)材料本身、儀器和外界環(huán)境因素噪聲對光譜信息的干擾,本研究選用五點(diǎn)移動(dòng)平均平滑(SG-平滑)、多元散射校正(MSC)、標(biāo)準(zhǔn)正態(tài)變量變換(SNV)、一階導(dǎo)數(shù)(1-Der)和二階導(dǎo)數(shù)(2-Der)等五種方法對甜橙葉片原始光譜(RAW)進(jìn)行預(yù)處理,并在此基礎(chǔ)上分析基于PLS模型的氮(N)、磷(P)、鉀(K)和可溶性總糖(TC)含量預(yù)測精度。結(jié)果顯示,花芽分化期當(dāng)年生春梢葉片光譜經(jīng)過五種方法預(yù)處理后對葉片N、P、K和TC含量的PLS模型預(yù)測,以SG-平滑算法預(yù)處理光譜預(yù)測效果優(yōu)于其他預(yù)處理方法；花芽分化期當(dāng)年生夏梢葉片光譜2-Der預(yù)處理后PLS建模預(yù)測N、P、K和TC的效果更優(yōu)；盛花期上一年春梢營養(yǎng)枝葉片光譜數(shù)據(jù)經(jīng)過SG-平滑預(yù)處理后對N、P、K和TC含量的預(yù)測效果優(yōu)于其他預(yù)處理方法。(3)基于哈姆林甜橙葉片的近紅外高光譜信息可以較好實(shí)現(xiàn)對葉片N、P、K和全C含量的預(yù)測。其中,對葉片N含量的預(yù)測以花芽分化期當(dāng)年生春梢葉片近紅外光譜SG-平滑算法預(yù)處理后的PLS建模預(yù)測精度較高,預(yù)測精度可達(dá)0.735;對P含量的光譜預(yù)測以花芽分化期當(dāng)年生春梢葉片原始光譜PLS建模最好,預(yù)測精度達(dá)0.733；對K含量的近紅外高光譜預(yù)測以花芽分化期當(dāng)年生春梢葉片原始光譜的PLS建模預(yù)測較好,預(yù)測精度達(dá)0.728；對全C含量的光譜預(yù)測以采用盛花期上一年春梢葉片光譜,SG-平滑算法預(yù)處理后PLS建模預(yù)測,對全C含量預(yù)測精度達(dá)0.688。2.哈姆林甜橙花營養(yǎng)的近紅外高光譜特征與預(yù)測研究(1)研究建立以0.1最優(yōu)閾值間隔和0.5～0.6灰度值的甜橙花朵高光譜圖像有效光譜信息提取技術(shù),篩選出哈姆林甜橙花的N營養(yǎng)含量特征光譜波段為692.146～735.3nm,以及440.07～481.07nm,692.146～735.3nm波段組合。以該特征波段組合反射光譜的siPLS預(yù)測模型,可以較好的預(yù)測花的N含量,其預(yù)測精度達(dá)0.762；通過特征波段組合篩選使得預(yù)測模型運(yùn)算所需實(shí)際波點(diǎn)數(shù)減少為108個(gè),比全光譜預(yù)測減少了85.8%,預(yù)測效率較大提高。(2)經(jīng)過篩選,得到哈姆林甜橙花P含量預(yù)測特征光譜波段為573.757～633.437nm,特征波段組合為573.757～633.437nm和693.74～752.653nm;以該特征波段組合反射光譜的siPLS預(yù)測模型,可以較好的實(shí)現(xiàn)對花的P含量預(yù)測,其預(yù)測精度達(dá)0.885,且實(shí)際僅用152個(gè)波點(diǎn)數(shù)參與模型預(yù)測運(yùn)算,比全光譜預(yù)測的波點(diǎn)數(shù)減少了80.0%(3)篩選出哈姆林甜橙花K含量預(yù)測的特征高光譜波段為593.313～643.711nm,表明以特征波段篩建立的iPLS預(yù)測模型,對于花K含量的預(yù)測效果最優(yōu),其預(yù)測精度達(dá)到0.916,實(shí)際采用64個(gè)波點(diǎn)數(shù)參與預(yù)測運(yùn)算,比全光譜波點(diǎn)數(shù)減少了91.6%。3.葉片碳氮比和樹體花量的高光譜預(yù)測(1)哈姆林甜橙葉片C/N估測的特征波長為507.117,507.886,523.294,527.928,534.114,543.41nm,iPLS建模預(yù)測精度達(dá)0.914。(2)秋季上一年春梢葉片對次年植株花量預(yù)測的特征波長為544.186,552.726,567.516,572.196,575.319,582.352,588.613和593.313nm,以PLS建模的預(yù)測精度達(dá)0.893,可用于次年植株花量的預(yù)測。
[Abstract]:In this paper, the 15 year old orange orange (Citrus. sinensis x Poncirus. trifoliata, "Carrizo Citrange") anvil (C.sinensis (L.), Hamlin Sweet Orange) was used as the object to study the spectral pretest method based on hyperspectral image information of leaf, flower nutrition and flower forming ability, aiming at establishing the real-time monitoring of the nutrition level of orange leaves and flower organs. The main research results are as follows. The main results are as follows: 1. the near infrared hyperspectral characteristics and prediction of the leaf nutrition of Hamlin sweet orange (1) the annual spring shoot of the flower bud differentiation period of the Hamlin sweet orange plant, the average reflectance curve of the 450-1000nnm spectrum of the leaves of the spring shoot of the summer shoots and the flowering stage is: leaves The higher the maturity of the film, the more stable the spectral curves fluctuate, that is, the difference between the spectral reflectance of the spectral curve, the Red Valley and the near infrared platform is smaller. (2) to eliminate or reduce the experimental material itself, the interference of the instrument and the ambient noise to the spectral information, the research selects five points moving average smoothness (SG- smooth) and multiple scattering correction. Positive (MSC), standard normal variable transformation (SNV), first derivative (1-Der) and two order derivative (2-Der) were used to pretreat the original spectrum of sweet orange leaves (RAW), and on this basis, the prediction accuracy of nitrogen (N), phosphorus (P), potassium (K) and soluble total sugar (TC) based on PLS model was analyzed. The results showed that the spring shoot Ye Pianguang of the flower bud differentiation period was in the spring shoot. After preprocessing the PLS model of the content of N, P, K and TC, the spectral prediction effect of the SG- smoothing algorithm is superior to the other pretreatment methods after five methods. The effect of PLS modeling on the PLS modeling of the leaves of summer shoot leaves in the flower bud differentiation period is better than that of the PLS modeling, and the effect of P, K and TC is better. The prediction effect on the content of N, P, K and TC after SG- smoothing is superior to other pretreatment methods. (3) near infrared hyperspectral information based on Hamlin sweet orange leaves can be used to predict the content of N, P, K and all C in leaves. The prediction of the content of N of leaf blade N is the SG- flat near infrared spectrum of the spring shoot leaves of the flower bud differentiation period. The prediction accuracy of PLS modeling is high and the prediction accuracy can reach 0.735. The prediction of P content by spectral prediction is best and the prediction accuracy is 0.733. Near infrared hyperspectral prediction of K content is predicted by PLS modeling prediction at the flower bud differentiation stage as the original spectrum of the annual spring shoot leaf. The prediction accuracy is 0.728, and the spectrum prediction of the total C content is used for the spring shoot leaf spectrum of the first year of the flowering period, the prediction of PLS modeling after the SG- smoothing algorithm, the near infrared hyperspectral characteristics and prediction of the 0.688.2. Hamlin sweet orange flower nutrition for the total C content prediction accuracy (1), the 0.1 optimal threshold interval and 0.5 ~ 0. are established. 6 gray value of orange flower hyperspectral image effective spectral information extraction technology, the spectral band of N nutrient content of Hamlin sweet orange flower is 692.146 ~ 735.3nm, and 440.07 ~ 481.07nm, 692.146 to 735.3nm band combination. The siPLS prediction model of the combined reflectance spectrum of the characteristic band can be used to predict the N content of the flower better. The prediction accuracy is 0.762, and the number of actual wave points required for the prediction model operation is reduced to 108 through the combination of feature bands. The prediction efficiency is reduced by 85.8% and the prediction efficiency is higher than that of the full spectrum prediction. (2) after screening, the spectral wave segment of the P content prediction feature of Hamlin orange flower is 573.757 to 633.437nm, and the characteristic band combination is 573.757. To 633.437nm and 693.74 ~ 752.653nm, the siPLS prediction model of the combined reflectance spectrum of the characteristic band can be used to predict the P content of the flower better. The prediction accuracy is 0.885, and the actual number of 152 wave points is only involved in the model prediction operation, and the K content of Hamlin sweet orange flower is screened out by 80% (3) than the total spectrum predicted by the total spectrum. The characteristic hyper spectral band of 593.313 ~ 643.711nm shows that the iPLS prediction model established by the feature band screen has the best prediction effect for the flower K content, and its prediction precision is 0.916. The actual use of 64 wave points is involved in the prediction operation, and the high spectral prediction (1) of the carbon and nitrogen ratio of 91.6%.3. leaves and the flower volume of the tree body is reduced than the total spectral wave points. The characteristic wavelength of the Hamlin orange leaf C/N estimation is 507.117507.886523.294527.928534.114543.41nm, and the prediction accuracy of iPLS modeling is 0.914. (2) the characteristic wavelengths of the spring shoot of the spring shoot of the last year in the autumn are 544.186552.726567.516572.196575.319582.352588.613 and 593.313nm, and the predictive precision of PLS modeling is obtained. Up to 0.893, it can be used to predict the plant flower volume of the next year.

【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S666.4;S127

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙江濤;李曉峰;李航;徐睿_，

本文編號：1791276