本文選題：光譜分析 + 水稻葉瘟病　； 參考：《農(nóng)業(yè)工程學(xué)報(bào)》2016年13期

【摘要】：為進(jìn)行水稻葉瘟病與養(yǎng)分缺失的區(qū)分、實(shí)現(xiàn)葉瘟病及時(shí)、準(zhǔn)確的診斷,以大田試驗(yàn)為基礎(chǔ),利用高光譜成像儀獲取2個(gè)品種的健康、缺氮、輕度感病和重度感病共4類(lèi)水稻葉片的反射率光譜,對(duì)其光譜特性進(jìn)行分析,并采用多種預(yù)處理方法、分別結(jié)合偏最小二乘判別分析(partial least squares-discriminate analysis,PLS-DA)和主成分加支持向量機(jī)(principle component analysis-support vector machine,PCA-SVM)方法構(gòu)建水稻葉瘟病識(shí)別模型。試驗(yàn)結(jié)果顯示6個(gè)判別模型都獲得了較高的識(shí)別準(zhǔn)確率,經(jīng)標(biāo)準(zhǔn)正態(tài)變量(standard normal variate,SNV)變換預(yù)處理的PLS-DA模型獲得了最佳的識(shí)別結(jié)果,預(yù)測(cè)準(zhǔn)確率達(dá)100%,經(jīng)多元散射校正(multiplicative scatter correction,MSC)預(yù)處理的PCA-SVM模型的預(yù)測(cè)準(zhǔn)確率也達(dá)到97.5%。本研究為水稻葉瘟病的判別和分級(jí)提供了新方法,也為稻瘟病大范圍遙感監(jiān)測(cè)提供了基礎(chǔ)。
[Abstract]:In order to distinguish rice leaf blast from nutrient deficiency and realize timely and accurate diagnosis of rice leaf blast, based on field experiments, two varieties were obtained by using hyperspectral imager. The spectral characteristics of four kinds of rice leaf reflectance spectrum were analyzed, and various pretreatment methods were used. Combined with partial least squares-discriminate analysis (PLS-DA) and principal component plus support vector machine (principle component analysis-support vector machine PCA-SVM), a rice leaf blast identification model was constructed. The experimental results show that all the six discriminant models have high recognition accuracy, and the PLS-DA model preprocessed by standard normal variable SNV transformation has the best recognition result. The prediction accuracy of PCA-SVM model pretreated by multiple scattering correction scatter correction MSC( MSC-PCA-SVM) is up to 97.5%. This study provides a new method for the identification and classification of rice leaf blast and provides a basis for large-scale remote sensing monitoring of rice blast.
【作者單位】： 東北農(nóng)業(yè)大學(xué)電氣與信息學(xué)院;黑龍江東方學(xué)院計(jì)算機(jī)科學(xué)與電氣工程學(xué)部;
【基金】：國(guó)家“863”計(jì)劃項(xiàng)目(2013AA102303) 黑龍江省重大科技研發(fā)項(xiàng)目(GY2014ZB0011) 哈爾濱市科技攻關(guān)項(xiàng)目(2013AA6BN010)
【分類(lèi)號(hào)】：S435.111.41;TP751

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