首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first extracting features of chromaticity moments was done then classification method of SVM for recognition of plant disease was discussed.
首先利用色度矩提取植物病害叶片的特征向量，然后利用支持向量机分类方法进行病害的识别。
At first, extracting features of chromaticity moments was done, then classification method of SVM for recognition of plant disease was discussed.
根据日光温室黄瓜病害的彩色纹理图像的特点，将支持向量机和色度矩方法用于识别黄瓜病害。
According to the features of color texture image of cucumber disease in sunlight greenhouse, recognition of cucumber disease using Support Vector Machine (SVM) and chromaticity moments is introduced.
在进行分类时，首先以色度矩作为特征向量，然后将支持向量机分类方法应用于黄瓜病害的识别。
At first, extracting features of chromaticity moments is done, then classification method of SVM for recognition of cucumber disease is discussed.
针对葡萄病害彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于葡萄病害识别的方法。
According to the features of color texture image of grape disease, a method of recognizing of grape dis-ease using support vector machine (SVM) and chromaticity moments is introduced.
首先利用色度矩提取葡萄病害叶片纹理图像的特征向量，然后将支持向量机分类方法应用于病害的识别。
At first, extracting features of chromaticity moments of texture image of grape disease is done, then classification method of SVM for recognition of grape disease is discussed.
针对植物病害彩色纹理图像的特点，提出将支持向量机和色度矩分析方法相结合应用于植物病害识别中。
According to the features of color texture image of plant disease, recognition of plant disease using support vector machine (SVM) and chromaticity moments was introduced.
首先利用色度矩提取玉米病害叶片纹理图像的特征向量，然后将支持向量机分类方法应用于病害的识别。
At first, the extracting features of chromaticity moments of texture image of maize disease is done, then classification method of SVM for recognition of maize disease is discussed.
针对玉米病害叶片彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于玉米病害识别的方法。
According to the features of color texture image of maize disease, a method of recognizing disease by using support vector machine (SVM) and chromaticity moments is introduced.
针对玉米病害叶片彩色纹理图像的特点，提出一种将支持向量机和色度矩分析应用于玉米病害识别的方法。
According to the features of color texture image of maize disease, a method of recognizing disease by using support vector machine (SVM) and chromaticity moments is introduced.
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