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基于特征優(yōu)化和LS-SVM的棉田雜草識別
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Weed Identification Based on Features Optimization and LS-SVM in the Cotton Field
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    摘要:

    為了提高雜草識別的精度和效率,,提出了一種基于特征優(yōu)化和最小二乘支持向量機(LS-SVM)技術的棉田雜草識別方法。在對原始圖像灰度化,、濾波去噪和閾值分割等處理的基礎上,,提取植物葉片的6個幾何特征和7個Hu不變矩,,用粒子群優(yōu)化(PSO)算法對形狀特征進行優(yōu)化選擇,,縮減LS-SVM訓練樣本數據,,然后用訓練好的分類器進行雜草識別,。實驗結果表明,,該方法在有效縮減形狀特征的同時,,能夠保持高于原始特征集的識別率,平均正確識別率達到

    Abstract:

    95.8%,。In order to improve the accuracy and efficiency of weed identification, a method for cotton-weed recognition was proposed by using the combination technique of features optimization and least squares support vector machine (LS-SVM). After a series of image processing such as graying, filtering and threshold segmenting, six geometric shape features and seven Hu moment invariants were extracted from the single plant leaf. Then, using particle swarm optimization (PSO) algorithm, the extracted features were optimized in order to reduce the size of the training data sets. Finally, the weed was identified by using the trained classifier. The experimental results indicate that this method can effectively compact feature subset and maintain a higher accuracy than using the original feature set, the average correct identification rate is 95.8%. 

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李先鋒,朱偉興,紀濱,劉波.基于特征優(yōu)化和LS-SVM的棉田雜草識別[J].農業(yè)機械學報,2010,41(11):168-172. Weed Identification Based on Features Optimization and LS-SVM in the Cotton Field[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(11):168-172.

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