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基于支持向量機的玉米田間雜草識別方法
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    摘要:

    提出了一種基于圖像處理和支持向量機(SVM)技術(shù)的玉米和雜草識別方法,。首先根據(jù)玉米與雜草,、土壤彩色圖像的特征提出一類圖像灰度化方法,,并通過對灰度圖像的除噪處理有效地分離目標對象,。然后從處理好的圖像中提取出目標對象的形狀特征參數(shù)作為輸入特征向量,進而提出玉米田間雜草識別的支持向量機方法,。試驗結(jié)果表明了方法的有效性,,通過適當選取核函數(shù)識別率可達到

    Abstract:

    98.3%。This paper proposed a method for corn-weed recognition by using the combinationtechnique of image processing and support vector machine (SVM). A gray processing algorithm was proposed based on the features of corn-weed color images. The object could be separated effectively by denoising the gray image. The shape features of the object were extracted and taken as feature vectors, which could be used to propose the SVM method for the recognition of corn-weed. Comparing the SVM method with the neural-network one, the former is better than the latter one seeing from the experimental results. Experimental results also show that the presented method is effective, and this method gives a recognition rate 98.3% with the properly selected kernel function. 

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吳蘭蘭,劉劍英,文友先,鄧曉炎.基于支持向量機的玉米田間雜草識別方法[J].農(nóng)業(yè)機械學報,2009,40(1):162-166.[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(1):162-166.

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