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基于角果期高光譜的冬油菜產(chǎn)量預(yù)測(cè)模型研究
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國(guó)家自然科學(xué)基金項(xiàng)目(31471941)和國(guó)家油菜產(chǎn)業(yè)體系建設(shè)專項(xiàng)(CARS-13)


Prediction Models of Winter Oilseed Rape Yield Based on Hyperspectral Data at Pod-filling Stage
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    摘要:

    以連續(xù)3a田間氮肥水平試驗(yàn)為基礎(chǔ),研究基于高光譜估產(chǎn)的可行性,明確最佳光譜監(jiān)測(cè)方式和有效波段,降低光譜分析維數(shù),提高產(chǎn)量估測(cè)時(shí)效性。2013—2016年分別于湖北省武穴市和沙洋縣進(jìn)行大田試驗(yàn),通過測(cè)試角果期冠層光譜反射率、產(chǎn)量構(gòu)成因子(單株角果數(shù)、每角粒數(shù)和千粒質(zhì)量)和成熟期產(chǎn)量,利用偏最小二乘回歸(PLS)分別對(duì)油菜原初光譜(RSR)和一階微分光譜(FDR)與其產(chǎn)量及構(gòu)成因子間構(gòu)建定量分析模型并篩選有效波段。結(jié)果表明,基于全波段的FDR-PLS模型預(yù)測(cè)精度顯著優(yōu)于R-PLS,其最佳監(jiān)測(cè)指標(biāo)是冬油菜產(chǎn)量和角果數(shù),驗(yàn)證集決定系數(shù)(R2)分別為0.90和0.91,均方根誤差(RMSE)分別為379kg/hm2和66個(gè)/株,相對(duì)分析誤差(RPD)分別為3.11和3.12?;诟鞑ǘ巫兞恐匾酝队埃╒IP)值,確定冬油菜產(chǎn)量有效波段分別為628、753、882、935、1061、1224nm;角果數(shù)有效波段分別為628、758、935、1063、1457、1600nm。此后,再次構(gòu)建基于上述有效波段的冬油菜產(chǎn)量和角果數(shù)監(jiān)測(cè)模型,決定系數(shù)分別為0.91和0.87,均方根誤差分別為504kg/hm2和82個(gè)/株,相對(duì)分析誤差分別為2.34和2.52,估算精度較為理想。

    Abstract:

    Hyperspectral remote sensing can provide a non-destructive and effective approach for assessing the yield and yield components of oilseed rape timely. A quantitative technique was developed to estimate oilseed rape yield accurately depending on ground-based canopy reflectance spectra. Field experiments were conducted over three growing seasons at different sites (Wuxue and Shayang) in Hubei Province, China. The key parameters, including canopy hyperspectral reflectance during pod-filling period, seed yield and yield components (pod numbers per plant, seed numbers per pod and 1000 seed weight) were monitored. A partial least square (PLS) regression analysis was employed to perform the relationship between raw spectral reflectance (RSR), the first derivative reflectance (FDR) and seed yield and yield components. According to the calibration dataset, the best results were obtained with the FDR-PLS model for the prediction of yield and pod number, which yielded the highest coefficient of determination (R2cal) of 0.96 and 0.98, and the lowest root mean square error (RMSEcal) of 158kg/hm2 and 17 pods/plant, respectively. The tests using the independent validation dataset also showed that the FDR-PLS model could well forecast yield and pod number of winter oilseed rape, with values of R2val of 0.90 and 0.91, RMSEval of 379kg/hm2 and 66 pods/plant, and RPD of 3.11 and 3.12, respectively. The variable importance in projection (VIP) scores resulted from the PLS regression analysis were used to determine the effective wavelengths and reduce the dimensionality of the spectral reflectance data. The newly-developed FDR-PLS model using the effective wavelengths (628nm, 753nm, 882nm, 935nm, 1061nm and 1224nm) performed well in yield prediction with R2val of 0.91, RMSE val of 504kg/hm2 and RPDval of 2.34;Similar results were also obtained for pod number prediction with R2val of 0.87, RMSEval of 82 pods/plant and RPDval of 2.52 using the effective wavelengths (628nm, 758nm, 935nm, 1063nm, 1457nm and 1600nm). Consequently, the yield of winter oilseed rape could be reliably estimated with the in situ developed FDR-PLS method.

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李嵐?jié)?任濤,汪善勤,明金,劉秋霞,魯劍巍.基于角果期高光譜的冬油菜產(chǎn)量預(yù)測(cè)模型研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(3):221-229. LI Lantao, REN Tao, WANG Shanqin, MING Jin, LIU Qiuxia, LU Jianwei. Prediction Models of Winter Oilseed Rape Yield Based on Hyperspectral Data at Pod-filling Stage[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):221-229.

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  • 收稿日期:2016-07-11
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  • 在線發(fā)布日期: 2017-03-10
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