基于图像处理的温室黄瓜霜霉病诊断系统

摘要

Downy mildew is one of the most common diseases suffered by greenhouse cucumbers,which may decrease the quality of greenhouse cucumbers and cause great economical loss to the farmers.In order to increase the accuracy of downy mildew diagnosis for greenhouse cucumbers,a downy mildew diagnosis system for greenhouse cucumbers was designed based on image processing.Focusing on the disease spots images captured in greenhouse field,the conditional random fields (CRF) based on segmentation method was utilized for the system to achieve disease spots images.When building the CRF model,decision tree model was used to extend unary potential function,which could effectively improve the accuracy of segmentation.The post-segmentation images and the disease spots images were transferred to HSV color space,and then 25 features,including color,texture and morphology features,were extracted.A subset of features was generated by rough set method.Finally,the RBF based SVM was used for the system to identify the greenhouse cucumber downy mildew.Taking cucumber downy mildew images obtained in greenhouse from agricultural innovation base of institute of plant protection,Tianjin academy of agricultural sciences as an example,the system was tested.The results showed that the segmentation method used by the system could effectively segment the disease spots images,which managed to overcome the noise caused by the illumination and complex background.A subset of 12 features was obtained by rough set method from the original feature set of 25 features,which improved the efficiency of the system.The identification accuracy of cucumber downy mildew reached 90％,which indicated that the downy mildew diagnosis system could meet the requirement of identification for greenhouse cucumbers.%为进一步提高温室黄瓜霜霉病诊断的准确率,构建了一个基于图像处理的温室黄瓜霜霉病诊断系统.针对温室黄瓜栽培现场采集的病害图像,采用基于条件随机场(Conditional random fields,CRF)的图像分割方法进行病斑图像分割,并采用决策树模型扩展一元势函数,提高病斑图像分割的准确性;将分割后的病斑图像转换到HSV颜色空间并提取其颜色、纹理和形状等25个特征,利用粗糙集方法进行特征选择与优化;构建了基于径向基核函数的SVM分类器,准确地识别与诊断温室黄瓜霜霉病.系统试验验证结果表明,该系统采用的病斑分割方法,能够克服复杂背景和光照条件的影响,准确地提取病斑图像;采用粗糙集方法能够有效地选择分类特征,将25个初始特征减少到12个,提高了运行效率;黄瓜霜霉病识别准确率达到90％,能够满足设施蔬菜叶部病害诊断的需求.