光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
10期
2713-2718
,共6页
马淏%吉海彦%Won Suk Lee
馬淏%吉海彥%Won Suk Lee
마호%길해언%Won Suk Lee
黄龙病%判别值%Fisher线性判别%分类树%近红外光谱
黃龍病%判彆值%Fisher線性判彆%分類樹%近紅外光譜
황룡병%판별치%Fisher선성판별%분류수%근홍외광보
Citrus greening%Discriminability%Fisher linear discriminant analysis%Classification tree%Vis-NIR spectroscopy
黄龙病作为柑橘类水果最具毁灭性的疾病之一，目前尚无有效的治愈手段，因此疾病预防成为已知的唯一有效方法。基于四种柑橘叶片（健康叶片、黄龙病叶片、铁缺乏叶片及氮缺乏叶片）VIS-NIR的反射光谱详细讨论了黄龙病的辨别方法以及在判别模型中光谱特征值的提取方法。在两类判别分析的特征值提取方法中，判别值（discriminability ）运算的引入，为特征值提取提供了一个可靠依据，判别值越大表明光谱差异性越大。以被选特征值建立的Fisher线性判别分析模型，黄龙病与健康、铁缺乏、氮缺乏叶片的分类判别预测准确率分别都超过了90％，分类效果符合预期。最后，又讨论了分类树（classificationTree）在多类判别中的应用。通过对柑橘叶片原始反射谱，一阶导数谱及被选特征值分别建立分类模型，四种柑橘叶片平均预测准确度都超过88％，尤其是基于特征值的分类结果更是超过94％，验证了在多类判别中检测柑橘黄龙病的可行性及特征值提取的重要性。结合传统分类方法（k-NN ，Bayesian）的结果分析，特征值作为输入变量的分类结果明显要优于原始光谱，证实了特征值选取的正确性，并为将来基于光谱特征值开发多光谱成像技术检测黄龙病打下坚实的基础。
黃龍病作為柑橘類水果最具燬滅性的疾病之一，目前尚無有效的治愈手段，因此疾病預防成為已知的唯一有效方法。基于四種柑橘葉片（健康葉片、黃龍病葉片、鐵缺乏葉片及氮缺乏葉片）VIS-NIR的反射光譜詳細討論瞭黃龍病的辨彆方法以及在判彆模型中光譜特徵值的提取方法。在兩類判彆分析的特徵值提取方法中，判彆值（discriminability ）運算的引入，為特徵值提取提供瞭一箇可靠依據，判彆值越大錶明光譜差異性越大。以被選特徵值建立的Fisher線性判彆分析模型，黃龍病與健康、鐵缺乏、氮缺乏葉片的分類判彆預測準確率分彆都超過瞭90％，分類效果符閤預期。最後，又討論瞭分類樹（classificationTree）在多類判彆中的應用。通過對柑橘葉片原始反射譜，一階導數譜及被選特徵值分彆建立分類模型，四種柑橘葉片平均預測準確度都超過88％，尤其是基于特徵值的分類結果更是超過94％，驗證瞭在多類判彆中檢測柑橘黃龍病的可行性及特徵值提取的重要性。結閤傳統分類方法（k-NN ，Bayesian）的結果分析，特徵值作為輸入變量的分類結果明顯要優于原始光譜，證實瞭特徵值選取的正確性，併為將來基于光譜特徵值開髮多光譜成像技術檢測黃龍病打下堅實的基礎。
황룡병작위감귤류수과최구훼멸성적질병지일，목전상무유효적치유수단，인차질병예방성위이지적유일유효방법。기우사충감귤협편（건강협편、황룡병협편、철결핍협편급담결핍협편）VIS-NIR적반사광보상세토론료황룡병적변별방법이급재판별모형중광보특정치적제취방법。재량류판별분석적특정치제취방법중，판별치（discriminability ）운산적인입，위특정치제취제공료일개가고의거，판별치월대표명광보차이성월대。이피선특정치건립적Fisher선성판별분석모형，황룡병여건강、철결핍、담결핍협편적분류판별예측준학솔분별도초과료90％，분류효과부합예기。최후，우토론료분류수（classificationTree）재다류판별중적응용。통과대감귤협편원시반사보，일계도수보급피선특정치분별건립분류모형，사충감귤협편평균예측준학도도초과88％，우기시기우특정치적분류결과경시초과94％，험증료재다류판별중검측감귤황룡병적가행성급특정치제취적중요성。결합전통분류방법（k-NN ，Bayesian）적결과분석，특정치작위수입변량적분류결과명현요우우원시광보，증실료특정치선취적정학성，병위장래기우광보특정치개발다광보성상기술검측황룡병타하견실적기출。
In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy ,HLB ,iron deficiency and nitrogen deficien-cy) .Between two classes of classification ,the values of discriminability of different spectra were calculated to extract spectral features .The greater value of discriminability showed a bigger difference of the two spectra ,which means it would be easier to distinguish the two classes .By the Fisher linear discriminant analysis ,three classification models (HLB & healthy ,HLB & iron deficiency and HLB & nitrogen deficiency ) based on the spectral features yielded more than 90% accuracies ,which were better than expected .And at last ,we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction .The models trained based on the original reflectance spectra ,first derivative and selected spectral features yielded more than 88% average accuracy ,and especially the model based on the spectral features yielded more than 94% average accuracies ,which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction .The results were compared based on classification tree ,k-NN and Bayesian classifiers .Adoption of spectral features as input variables was significantly superior to using the original spectrum ,which confirmed the validity of spectral feature selection .Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus g reening .
