小麦叶部常见病害特征提取及识别技术研究.pdfVIP

154 2014，50（7） Computer Engineering and Applications 计算机工程与应用 小麦叶部常见病害特征提取及识别技术研究 王美丽 1，牛晓静 1，张宏鸣 1，赵建邦 1，何东健 2 WANG Meili1, NIU Xiaojing1, ZHANG Hongming1, ZHAO Jianbang1, HE Dongjian2 1.西北农林科技大学 信息工程学院，陕西 杨凌 712100 2.西北农林科技大学 机械电子与工程学院，陕西 杨凌 712100 1.College of Information Engineering, Northwest AF University, Yangling, Shaanxi 712100, China 2.College of Mechanical and Electronic Engineering, Northwest AF University, Yangling, Shaanxi 712100, China WANG Meili, NIU Xiaojing, ZHANG Hongming, et al. Research on feature extraction and recognition of common diseases of wheat leaf. Computer Engineering and Applications, 2014, 50（7）：154-157. Abstract：This paper selects four common diseases of wheat leaf images, using image processing techniques to identify different types of disease. Firstly, the RGB color space is converted to HSV color space, the relevant color characteristics （hue and saturation）are extracted, and then geometry features（perimeter area, squareness, roundness, eccentricity, etc.） are extracted. To obtain the eigenvalues of each disease range, the sample images are analyzed, and then the eigenvalues of the unknown samples are used to identify different kinds of wheat diseases. This research takes powdery mildew and rust （leaf rust, stripe rust and stem rust）as research objects. Based on color characteristics, the powdery mildew and rust are identified, according to the shape characteristics, leaf rust, stripe rust and stem rust are identified. The proposed method is simple and convenient with an identification rate of more than 96%. The experimental results show that the chosen color and shape features of these four common diseases are valid and feasible for wheat diseases identification. Key words：wheat disease; feature extraction; image recognition 摘 要：选取小麦叶部常见病害图像，利用图像处理技术进行病害种类的识别。将图像由 RGB 彩色空间转换到 HSV 颜色空