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基于高光譜成像的馬鈴薯葉片葉綠素分布可視化研究
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國家自然科學(xué)基金項(xiàng)目(31501219)、國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YFD0300606、2016YFD0300610)和中央高?;究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目(2017TC029)


Visualization of Chlorophyll Distribution of Potato Leaves Based on Hyperspectral Imaging Technology
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    摘要:

    針對馬鈴薯作物葉片進(jìn)行了葉綠素含量無損檢測技術(shù)及分布圖繪制方法研究,用以指示作物長勢并指導(dǎo)精細(xì)化管理。首先利用高光譜成像技術(shù)采集了65個(gè)馬鈴薯葉片的400個(gè)樣本點(diǎn)高光譜圖像和相應(yīng)的SPAD值,提取并計(jì)算葉綠素測量區(qū)域的葉片平均光譜后,分別采用蒙特卡羅無信息變量消除算法(MC-UVE)和自適應(yīng)重加權(quán)算法(CARS)篩選出了12個(gè)和23個(gè)葉綠素含量敏感波長,建立了馬鈴薯葉片葉綠素含量偏最小二乘(PLS)回歸模型。建模結(jié)果如下:基于MC-UVE算法篩選的12個(gè)敏感波長的PLSR診斷模型,建模精度R2C為0.79,驗(yàn)證精度R2V為0.73;基于CARS算法篩選的23個(gè)敏感波長建立的PLSR診斷模型,建模精度R2C為0.82,驗(yàn)證精度R2V為0.80。擇優(yōu)選取CARS-PLSR模型計(jì)算馬鈴薯葉片每個(gè)像素點(diǎn)的葉綠素含量,從而利用偽彩色繪圖繪制了馬鈴薯葉片葉綠素含量可視化分布圖,最終實(shí)現(xiàn)馬鈴薯葉片含量無損檢測以及葉綠素分布可視化表達(dá),以期為后續(xù)馬鈴薯作物大田冠層葉綠素分布診斷提供支持。

    Abstract:

    Non-destructive detection of chlorophyll content and drawing the chlorophyll distribution map of potato crop leaves could indicate crop growth and guide field management. In this paper, the hyperspectral imaging technique was used to diagnose the chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral images of 65 potato leaves were collected and divided into 400 regions of interesting (ROI). Meanwhile, the SPAD values of these 400 ROI samples were measured. After extracting and calculating the average leaf spectrum of the chlorophyll measurement area, the 12 chlorophyll content sensitive wavelengths were chosen by the Monte Carlo uninformative variables elimination (MC-UVE) algorithm and the 23 chlorophyll content sensitive wavelengths were selected by the competitive adaptive reweighted sampling (CARS) algorithm. They were used to establish the partial least squares regression (PLSR) model of chlorophyll content index of potato leaves respectively. The results were as follows: 12 sensitive wavelengths selected by MC-UVE algorithm were 532.54nm, 534.27nm, 566.78nm, 737.60nm, 741.61nm, 742.51nm, 759.49nm, 772.92nm, 816.54nm, 880.88nm, 928.84nm, 943.88nm. The modeling determination coefficient was 0.79, and predictive determination coefficient was 0.73. Meanwhile, 23 sensitive wavelengths selected by the CARS algorithm were 394.01nm, 399.94nm, 492.03nm, 493.32nm, 494.18nm, 534.27nm, 536.86nm, 537.30nm, 537.73nm, 543.79nm, 544.22nm, 545.52nm, 547.25nm, 547.69nm, 548.12nm, 550.29nm, 550.72nm, 553.76nm, 555.49nm, 938.93nm, 986.36nm, 987.74nm, 1018.30nm.The modeling determination coefficient of the PLSR diagnostic model built with these wavelengths was 0.82, and predictive determination coefficient was 0.80. Thus, the chlorophyll content of potato leaves can be calculated by CARS-PLS model, and the visual distribution map of chlorophyll content in potato leaves was plotted by using pseudo-color drawing. It provides a method for the diagnosis of chlorophyll distribution in the future.

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鄭濤,劉寧,孫紅,龍耀威,楊瑋,ZHANG Qin.基于高光譜成像的馬鈴薯葉片葉綠素分布可視化研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(s1):153-159, 340. ZHENG Tao, LIU Ning, SUN Hong, LONG Yaowei, YANG Wei, ZHANG Qin. Visualization of Chlorophyll Distribution of Potato Leaves Based on Hyperspectral Imaging Technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(s1):153-159, 340.

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  • 收稿日期:2017-07-10
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  • 在線發(fā)布日期: 2017-12-10
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