吴兰兰,刘剑英,文友先,邓晓炎.基于支持向量机的玉米田间杂草识别方法[J].农业机械学报,2009,40(1):162-166.
.[J].Transactions of the Chinese Society for Agricultural Machinery,2009,40(1):162-166.
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基于支持向量机的玉米田间杂草识别方法   [下载全文]
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DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  玉米  杂草识别  支持向量机  预处理  核函数
基金项目:
吴兰兰  刘剑英  文友先  邓晓炎
华中农业大学
中文摘要:提出了一种基于图像处理和支持向量机(SVM)技术的玉米和杂草识别方法。首先根据玉米与杂草、土壤彩色图像的特征提出一类图像灰度化方法,并通过对灰度图像的除噪处理有效地分离目标对象。然后从处理好的图像中提取出目标对象的形状特征参数作为输入特征向量,进而提出玉米田间杂草识别的支持向量机方法。试验结果表明了方法的有效性,通过适当选取核函数识别率可达到
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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. 

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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