本文選題：生理活性 + 低溫脅迫��； 參考：《山西農(nóng)業(yè)大學》2015年碩士論文

【摘要】：為研究低溫脅迫下冬小麥生理活性變化高光譜監(jiān)測的可行性,本研究以晉太182(強冬性)和濟22(半冬性)兩個冬小麥品種為供試材料,采用盆栽設(shè)計,使用人工霜凍箱對拔節(jié)期冬小麥進行時間(4h,8h,12h)和溫度(-2℃,-4℃,-6℃)兩個因素共9個處理的低溫脅迫。測定低溫脅迫后不同生育時期功能葉過氧化物酶(POD)、超氧化物歧化酶(SOD)和丙二醛(MDA)的生理活性變化,并采集對應冬小麥冠層光譜,對光譜(全譜和特征光譜)和冬小麥生理活性(POD、SOD、MDA)進行多元線性回歸分析,構(gòu)建低溫脅迫后冬小麥生理活性監(jiān)測模型。結(jié)果表明：1.在冬小麥的整個生育期中,對照組冬小麥從拔節(jié)期到灌漿期,POD活性呈升高趨勢,SOD和MDA沒有明顯變化,趨勢比較平緩。低溫脅迫后,冬小麥功能葉的POD活性變化總體趨勢仍與對照組相同,但仍可以看出,強冬性品種的抗寒性較強,而半冬性品種懨復能力較強。超氧化物歧化酶保護系統(tǒng)對脅迫時間的敏感程度高于脅迫溫度：MDA積累量沒有表現(xiàn)出明顯變化。2.從化學維分析,POD、SOD 和 MDA三種生理指標的活力值分布范圍都很廣并且驗證集活力值均包含于建模集范圍內(nèi)。從平均值和標準差分析,建模集和驗證集的偏移幅度也比較理想,說明所選建模集數(shù)據(jù)和驗證集的數(shù)據(jù)劃分具有代表性,同樣數(shù)據(jù)量也滿足建模的要求,可以進行建模。3.從光譜維分析,實現(xiàn)基于POD、SOD、MDA三種生理因子活性的監(jiān)測是可行的,并且分析結(jié)果表明,SG-MLR模型建模效果最好,預測效果最佳。每個時期篩選出的特征波段,全部分散分布于可見光和近紅外范圍,但大部分位于可見光區(qū)域,特別是在400-450 nm波段范圍更為集中。具體表現(xiàn)為,從拔節(jié)期到灌漿期80%以上的特征波段都位于可見光范圍。除抽穗期外,50%以上的特征波段位于400-450 nm波段范圍,即三種生理指標的活性變化在該波段范圍攜帶有更多的有效信息。4.通過全波段粵特征波段建模的分析比較得知,特征波段建模(SPA-MLR)與全譜建模(SG-MLR)的建模集相關(guān)系數(shù)RC、RP及校正標準差(RMSEc),預測標準差(RMSEp)差異性不大(抽穗期差異較大),但是采用特征波段進行建模,數(shù)據(jù)量會減少95%左右,大大減少了共線和冗余信息,減輕了計算工作量,降低了模型復雜程度。因此,可以用特征波段代替全波段進行建模。
[Abstract]:In order to study the feasibility of hyperspectral monitoring of physiological activity changes of Winter Wheat under low temperature stress, this study used two winter wheat varieties of Jintai 182 (strong winter) and 22 (semi winter) as the test materials, using pot design, using artificial frost boxes for winter wheat at jointing stage (4h, 8h, 12h) and temperature (-2 C, -4, -6 C) 9 factors. The physiological activities of functional leaf peroxidase (POD), superoxide dismutase (SOD) and malondialdehyde (MDA) were measured at different growth stages after low temperature stress, and the canopy spectra of winter wheat were collected, and the spectral (total and characteristic spectra) and physiological activity of Winter Wheat (POD, SOD, MDA) were analyzed by multiple linear regression analysis, and the construction of winter wheat physiological activity (POD, SOD, MDA) was analyzed. The results of the physiological activity monitoring model of winter wheat after low temperature stress showed that: 1. during the whole growth period of winter wheat, the activity of POD in the control group increased from the jointing stage to the filling stage, and there was no obvious change in SOD and MDA, and the trend was relatively slow. The overall trend of POD activity in winter wheat was still in phase with the control group after low temperature stress. It is still clear that the cold resistance of the strong winter varieties is stronger and the semi winter variety is stronger. The sensitivity of the superoxide dismutase protection system to the stress time is higher than that of the stress temperature. The MDA accumulation does not show significant changes in the distribution of the activity values of the three physiological indexes of.2., POD, SOD and MDA. From the mean value and the standard deviation analysis, the offset of the modeling set and the validation set is also ideal. It shows that the data partition of the selected modeling set and the validation set is representative. The same amount of data also satisfies the requirements of the modeling. The modeling.3. can be analyzed from spectral dimension, and the model can be modeled. It is feasible to monitor the activity of three physiological factors based on POD, SOD and MDA. And the analysis results show that the SG-MLR model has the best modeling effect and the best prediction effect. All the characteristic bands selected at each time are distributed in the visible and near infrared range, but most of them are in the visible light region, especially at the 400-450 nm band range. The feature bands above 80% from the jointing period to the filling stage are in the visible light range. In addition to the heading period, more than 50% of the characteristic bands are located in the 400-450 nm band range, that is, the activity changes of three physiological indexes carry more effective information.4. through the full band Guangdong characteristic band modeling. The correlation coefficient RC, RP and correction standard deviation (RMSEc) of characteristic band modeling (SPA-MLR) and full spectrum modeling (SG-MLR) are analyzed and compared. The difference of prediction standard deviation (RMSEp) is not significant (the difference of heading period is large). However, the data amount will be reduced by about 95% with the feature band modeling, which greatly reduces the collinear and redundant information and reduces the calculation. Workload can reduce the complexity of the model, so the characteristic band can be used instead of the full band for modeling.
【學位授予單位】：山西農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S127;S512.11

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