光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
Spectroscopy and Spectral Analysis
2015年
9期
2629-2633
,共5页
周春艳%华灯鑫%乐静%万文博%蒋朋%毛建东
週春豔%華燈鑫%樂靜%萬文博%蔣朋%毛建東
주춘염%화등흠%악정%만문박%장붕%모건동
叶绿素含量%BP 神经网络模型%SPAD%WI
葉綠素含量%BP 神經網絡模型%SPAD%WI
협록소함량%BP 신경망락모형%SPAD%WI
Chlorophyll content%BP neural network model%SPAD%WI
活体植物叶片叶绿素含量 SPAD 值易受叶片厚度、水分等影响,提出了基于多参数神经网络建模的叶绿素含量精细反演方法。通过测量叶片在中心波长分别为650,940和1450 nm 光照射下的透过率,获得叶片的 SPAD 值和水分指数 WI(water index),同时用数字螺旋测微仪测量相应的叶片厚度并用分光光度法测得其叶绿素含量。利用建模集样本分别建立 SPAD 值与实测叶绿素含量之间的单参数模型和基于 BP 神经网络的 WI、厚度及 SPAD 值与实测叶绿素含量之间的非线性模型。利用这两种模型分别计算获得验证集样本的叶绿素含量预测值,对预测值和实测值进行了相关分析和相对误差的分析。实验以340个三种不同植物叶片为样本,用以上方法进行了分析。结果表明,利用 BP 神经网络建模后,每种植物样本的叶绿素含量预测精度都有不同程度的提高,尤其对于叶片厚度值较大的样本,效果更为明显。数据显示所有混合样本平均相对误差绝对值由单参数模型的7.55%降低到5.22%,实测值与预测值的拟合决定系数由0.83提高到0.93。验证了利用多参数 BP 神经网络模型可以有效地提高活体植物叶绿素含量预测精度的可行性。
活體植物葉片葉綠素含量 SPAD 值易受葉片厚度、水分等影響,提齣瞭基于多參數神經網絡建模的葉綠素含量精細反縯方法。通過測量葉片在中心波長分彆為650,940和1450 nm 光照射下的透過率,穫得葉片的 SPAD 值和水分指數 WI(water index),同時用數字螺鏇測微儀測量相應的葉片厚度併用分光光度法測得其葉綠素含量。利用建模集樣本分彆建立 SPAD 值與實測葉綠素含量之間的單參數模型和基于 BP 神經網絡的 WI、厚度及 SPAD 值與實測葉綠素含量之間的非線性模型。利用這兩種模型分彆計算穫得驗證集樣本的葉綠素含量預測值,對預測值和實測值進行瞭相關分析和相對誤差的分析。實驗以340箇三種不同植物葉片為樣本,用以上方法進行瞭分析。結果錶明,利用 BP 神經網絡建模後,每種植物樣本的葉綠素含量預測精度都有不同程度的提高,尤其對于葉片厚度值較大的樣本,效果更為明顯。數據顯示所有混閤樣本平均相對誤差絕對值由單參數模型的7.55%降低到5.22%,實測值與預測值的擬閤決定繫數由0.83提高到0.93。驗證瞭利用多參數 BP 神經網絡模型可以有效地提高活體植物葉綠素含量預測精度的可行性。
활체식물협편협록소함량 SPAD 치역수협편후도、수분등영향,제출료기우다삼수신경망락건모적협록소함량정세반연방법。통과측량협편재중심파장분별위650,940화1450 nm 광조사하적투과솔,획득협편적 SPAD 치화수분지수 WI(water index),동시용수자라선측미의측량상응적협편후도병용분광광도법측득기협록소함량。이용건모집양본분별건립 SPAD 치여실측협록소함량지간적단삼수모형화기우 BP 신경망락적 WI、후도급 SPAD 치여실측협록소함량지간적비선성모형。이용저량충모형분별계산획득험증집양본적협록소함량예측치,대예측치화실측치진행료상관분석화상대오차적분석。실험이340개삼충불동식물협편위양본,용이상방법진행료분석。결과표명,이용 BP 신경망락건모후,매충식물양본적협록소함량예측정도도유불동정도적제고,우기대우협편후도치교대적양본,효과경위명현。수거현시소유혼합양본평균상대오차절대치유단삼수모형적7.55%강저도5.22%,실측치여예측치적의합결정계수유0.83제고도0.93。험증료이용다삼수 BP 신경망락모형가이유효지제고활체식물협록소함량예측정도적가행성。
Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness,water content, etc,a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented.The SPAD values and water index(WI)of leaves were obtained by the leaf transmittance under the irradiation of light central wavelength in 650nm,940nm,1450nm respectively.Meanwhile,the corresponding blade thickness is got by micrometer and the chlorophyll content is measured by spectrophotometric method.To modeling samples,the single parameter model between SPAD values and chlorophyll content was built and the nonlinear model between WI,thickness,SPAD values and chlorophyll content was estab-lished based on BP neural network.The predicted value of chlorophyll content of test samples were calculated separately by two models,and the correlation and relative errors were analyzed between predicted values and actual values.340 samples of three different plant leaves were tested by the method described above in experiment.The results showed that compared with single parameter model,the prediction accuracy of three different plant samples were improved in different degrees,the average abso-lute relative error of chlorophyll content of all pooled samples predicted by BP neural network model reduced from 7.55% to 5.22%.the fitting determination coefficient is increased from 0.83 to 0.93.The feasibility were verified in this paper that the prediction accuracy of living plant chlorophyll content can improved effectively using multiple parameter BP neural network mod-el.