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首页> 外文期刊>Sensor Letters: A Journal Dedicated to all Aspects of Sensors in Science, Engineering, and Medicine >Image Recognition of Maize Diseases Based on Fuzzy Clustering and Support Vector Machine Algorithm
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Image Recognition of Maize Diseases Based on Fuzzy Clustering and Support Vector Machine Algorithm

机译:基于模糊聚类和支持向量机算法的玉米病害图像识别

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As to the fact that recognition rate of maize diseases is not high enough, this paper aims to find a new method used for recognize maize disease in ways of fuzzy cluster and vector machine based on machine vision technology. Enhance images with median filter method, and divide disease with the method of fuzzy c-means clustering algorithms, then extract color, shape, and texture feature. Finally, recognize disease with SVM recognition method, and the recognition accuracy rate is above 95%. Median filter algorithm could be used to smooth disease images of maize effectively. Meanwhile segmentation algorithm of fuzzy cluster could divide disease images of maize accurately, especially it is better that make use of SVM linear kernel function identifying these images as classified function.
机译:针对玉米病害识别率不够高的事实,本文旨在寻找一种基于机器视觉技术的模糊聚类和矢量机相结合的玉米病害识别新方法。使用中值滤波方法增强图像,并使用模糊c均值聚类算法对疾病进行分类,然后提取颜色,形状和纹理特征。最后,采用支持向量机识别方法对疾病进行识别,识别准确率达到95%以上。中值滤波算法可以有效地平滑玉米的病害图像。同时模糊聚类的分割算法可以对玉米的病害图像进行准确的分割,特别是利用支持向量机线性核函数将这些图像识别为分类函数更好。

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