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基于多特征降維的植物葉片識(shí)別方法
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中央高?;究蒲袠I(yè)務(wù)費(fèi)專(zhuān)項(xiàng)資金項(xiàng)目(2015ZCQ-GX-04)和北京市科技計(jì)劃項(xiàng)目(Z161100000916012)


Method of Leaf Identification Based on Multi-feature Dimension Reduction
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    摘要:

    植物種類(lèi)識(shí)別方法主要是根據(jù)葉片低維特征進(jìn)行自動(dòng)化鑒定,。針對(duì)低維特征不能全面描述葉片信息,識(shí)別準(zhǔn)確率低的問(wèn)題,,提出一種基于多特征降維的植物葉片識(shí)別方法,。首先通過(guò)數(shù)字圖像處理技術(shù)對(duì)植物葉片彩色樣本圖像進(jìn)行預(yù)處理,獲得去除顏色、蟲(chóng)洞、葉柄和背景的葉片二值圖像,、灰度圖像和紋理圖像。然后對(duì)二值圖像提取幾何特征和結(jié)構(gòu)特征,,對(duì)灰度圖像提取Hu不變矩特征、灰度共生矩陣特征,、局部二值模式特征和Gabor特征,,對(duì)紋理圖像提取分形維數(shù),共得到2183維特征參數(shù),。再采用主成分分析與線(xiàn)性評(píng)判分析相結(jié)合的方法對(duì)葉片多特征進(jìn)行特征降維,,將葉片高維特征數(shù)據(jù)降到低維空間。降維后的訓(xùn)練樣本特征數(shù)據(jù)使用支持向量機(jī)分類(lèi)器進(jìn)行訓(xùn)練,。試驗(yàn)結(jié)果表明:使用訓(xùn)練后的支持向量機(jī)分類(lèi)器對(duì)Flavia數(shù)據(jù)庫(kù)和ICL數(shù)據(jù)庫(kù)的測(cè)試葉片樣本進(jìn)行分類(lèi)識(shí)別,,平均正確識(shí)別率分別為92.52%、89.97%,,有效提高了植物葉片識(shí)別的正確率,。

    Abstract:

    The identification of plant species is an essential part of botanical study and agricultural production. However, low dimension features cannot describe the leaf information. Thus, it cannot differentiate varieties of plants, and the accuracy is low. A method of plant species identification was proposed based on multi-feature dimension reduction. Firstly, color images of plant leaves were preprocessed by the digital image processing technology. The binary image, gray scale image and texture image without the petiole, wormhole and background were obtained after the preprocessing. Secondly, geometric characteristics and structural characteristic were extracted from the binary image. Hu moment invariants features, gray level co-occurrence matrix features, LBP features and Gabor features were extracted from the gray scale image. The fractal dimension was extracted from the texture images and 2183 features were extracted to describe leaf samples in number. Thirdly, the method of combining principal component analysis (PCA) and linear discriminant analysis (LDA) was adopted to reduce the feature dimension. Then the feature data of training samples was adopted to train the support vector machine classifier. Finally, the support vector machine classifier was used to classify the feature data of test samples. The experiments were carried out on Flavia database and ICL database. The average accuracy was 92.52% and 89.97%, respectively. The experiments showed that the average accuracy of the proposed method was better than that of the compared researches.

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鄭一力,鐘剛亮,王強(qiáng),趙玥,趙燕東.基于多特征降維的植物葉片識(shí)別方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(3):30-37. ZHENG Yili, ZHONG Gangliang, WANG Qiang, ZHAO Yue, ZHAO Yandong. Method of Leaf Identification Based on Multi-feature Dimension Reduction[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):30-37.

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  • 收稿日期:2016-09-03
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  • 在線(xiàn)發(fā)布日期: 2017-03-10
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