基于GA-BP神经网络和特征向量优化组合的黄瓜叶片病斑识别

针对家庭种植水培黄瓜中用户难以准确识别病害的问题,设计了一种基于图像处理的黄瓜叶片病斑识别系统。应用自适应小波对原始图像进行降噪处理,在HSV空间通过阈值分割结合形态学操作获得理想的黄瓜叶片图像,并通过自适应阈值分离病斑,提取病斑形态学、颜色和纹理原始特征参数。利用GA-BP神经网络定义原始特征参数对分类结果的灵敏度,递归剔除灵敏度较低的若干特征,降低特征参数的维数。根据优化后的特征参数组合,利用支持向量机对黄瓜炭疽病和白粉病进行识别。实验结果表明,本方法对黄瓜炭疽病和白粉病的综合分类正确率在96%以上。设计的方法有效提高了黄瓜病害的识别率,并为其他作物病害的智能识别提供了借鉴。

Cucumber leaf lesion identification based on GA-BP neural network and feature vector optimization combination

In order to solve the problem that it is difficult for the users to identify the disease accurately in the domestic hydroponic cucumber, a cucumber leaf spot recognition system based on image processing was designed. Adaptive wavelet was applied to the original image noise reduction processing. The ideal cucumber leaf segmentation image was obtained by threshold segmentation combined with morphological operation in HSV space. The disease spots of cucumber leaves were obtained by adaptive threshold segmentation, and the morphology, color and texture original characteristic parameters were extracted from spots. The sensitivity of original characteristic parameters was defined based on GA-BP network, and the optimal combination of features was realized by recursive elimination of some features with low sensitivity. According to the optimized combination of characteristic parameters, the support vector machine was used to identify the anthrax and powdery mildew of cucumber. The experimental results showed that the method effectively improved the recognition rate of cucumber diseases and provided a reference for other crop diseases intelligent identification.