Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/36946
標題: 利用O-PLS建立在植被反射光譜資料之水稻產量預測及品種分類模式
Using Orthogonal Projections to Latent Structures (O-PLS) Method to Predict Yield and Classify Cultivars of Rice (Oryza Sativa L.) Based on Canopy Reflectance Spectra Data
作者: 林汶鑫
Lin, Wen-Shin
關鍵字: 潛在結構直交投影;orthogonal projections to latent structures(O-PLS);產量預測;品種分類;前處理方法;yield prediction;cultivars classification;pre-processing methods
出版社: 農藝學系所
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摘要: 
1960年代起,光譜遙測技術已經逐漸應用在對於環境監測以及評估的研究之中。近年來更被廣泛的應用於作物生長條件的監測,以及各種性狀的預測及推估,甚至於在作物種類的辨識。然而,在分析研究過程中,利用植被光譜資料建立之預測及推估模式除了會受各光譜特徵間的共線性影響之外,其光譜量測的變異亦可區分為Y-predictive variation及Y-orthogonal variation。其中Y-orthogonal variation是指外部干擾的影響,例如光譜的散射及飄移,量測背景影響、植株間或作物內部結構的差異及陽光的入射角度不同等,使得所量測的資料中也包含有影響模式效能的雜訊。因此,除了建立不受共線性影響的作物預測及分類模式之外,仍需在資料分析前進行資料前處理的修正,以減少干擾所造成的影響。
因此,本研究利用首先利用2001 - 2005年一期稻作臺農67號(Tainung No. 67, TNG 67)之光譜資料,利用不同前處理方法配合partial least squares regression (PLSR)進行稻米產量預測模式的建立與比較。在五種前處理方法(standard normal variate transformation (SNV); multiplicative scatter correction (MSC); orthogonal signal correction with cross validation (OSC(CV)); orthogonal signal correction with 15 components (OSC(15 comps.)); orthogonal projections to latent structures(O-PLS))中,OSC(CV)、OSC(15 comps.)及O-PLS均有優於其餘前處理方法及原始模式的表現。其中,利用O-PLS所建立之產量預測模式除表現較佳外,對於Y-orthogonal variation亦具有較易解釋的優點。本研究除利用O-PLS方法建立稻米產量的預測模式之外,更將其延伸應用至分類模式的建立。在利用2005年一期稻作台稉16號(Taikeng No. 16 , TK16)、台中秈10號(Taichung-Hsien No. 10, TCS10)、臺農71號(Tainung No. 71, TNG71)之不同生育期所建立的分類模式中,OPLS-DA(O-PLS for discriminant analysis)均能建立良好的分類模式,其正確分類率均幾乎達到100%。此外,PLS-DA(PLS for discriminant analysis)在孕穗期之前及成熟期之後具有較為穩定的分類能力。另外,利用VIP(variable importance in the projection)方法對於建立之PLSR(partial least squares regression)模式選取重要的波長變數,亦能有效的減少PLSR模式所使用的波長變數數目,並具有同等的預測及分類效能。綜合研究結果得知,利用O-PLS方法建立光譜資料的預測及分類模式具有較佳的表現,可充分減少PLSR模式中之成分數,有效移除與目標變數特性不相關之變異(Y-orthogonal variation),並具有較易解釋之特性。

Since 1960s, remote sensing technique has been utilized for monitoring and evaluating the characteristic of environment. Recently, canopy reflectance spectra was also further applied in the study of the crops for monitoring the growth conditions and predicting and evaluating the characteristics, even for distinguishing the cultivars. In order to construct the prediction or classification model during the study process, the systematic variations in predictors can be used, including Y-predictive variations and Y-orthogonal variations. However, the calibration models may be affected by the multicollinearity and Y-orthogonal variations. The Y-orthogonal variations represent the systematic variation (even known or unknown) in X that belongs to the orthogonal component of the systematic variation in response variables. In the canopy reflectance spectra, the reflected scatter, baseline shift, background absorption, the angles of sun elevation, and the structure of leaf are included in the Y-orthogonal variations. Therefore, besides avoiding the impact of the multicollinearity when constructing the prediction or classification models by using the reflectance spectral data, pre-processing spectral data before modeling is unavoidable to obtain reliable, accurate and stable calibration models.
In the first part of this dissertation, an important Japonica type paddy rice cultivar in Taiwan, Tainung 67 (TNG67), collected in 2001 - 2005 was the main materials in this study. Five signal pre-processing methods (standard normal variate transformation (SNV), multiplicative scatter correction (MSC), orthogonal signal correction with cross validation (OSC(CV)), orthogonal signal correction with 15 components (OSC(15 comps.)), orthogonal projections to latent structures(O-PLS)) combined with partial least squares regression (PLSR) were investigated and compared. The performance of the PLSR models using OSC(CV), OSC(15 comps.) and O-PLS pre-processing methods were better than the other methods and the original PLSR model. Moreover, the O-PLS pre-processing methods were more effective than the other methods in dealing with the intra-variance and Y-orthogonal variation. In the second part of this dissertation, besides using O-PLS to construct the calibration model for predicting rice yield, we also investigated how to use O-PLS to construct the multi-class classification model for classifying the rice cultivars using canopy reflectance spectral data. Ground-based canopy reflectance spectra obtained from three popular paddy rice cultivars in Taiwan, including Taichung-Hsien No. 10 (TCS10), Taikeng No. 16 (TK16) and Tainung No. 71 (TNG71) that were measured during entire growth period. O-PLS for discriminant analysis (OPLS-DA) model was successfully applied on canopy spectral reflectance data for classifying three popular paddy rice cultivars with the high accuracy of classification (reached to 100%). PLS for discriminant analysis (PLS-DA) model could provide the stable classifying results before booting stage and during the ripening phases. Furthermore, using variable importance in the projection (VIP) rule to select wavelength both in calibration and classification model could extract the important wavelengths. All of them could provide the original and important information and maintain the same performance on prediction and classification. Accordingly, the O-PLS algorithm could have the high accuracy of calibration and classification when using reflectance spectral data, and have the capability of model interpretation by removing the Y-orthogonal variations for constructing the more parsimonious PLS models with few components.
URI: http://hdl.handle.net/11455/36946
其他識別: U0005-1908200913552800
Appears in Collections:農藝學系

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