基于HSV色彩空间的红枣叶片病斑分割方法

Segmentation Method of Disease Spots on Jujube Leaves Based on HSV Color Space

在OpenCV框架下的python 3环境中,开发了一种处理红枣病害叶片图像的技术,通过提取红枣病害区域HSV色彩空间分离H、S、V通道颜色分量图,经过灰度变换得到3个灰度图像,对灰度图像进行腐蚀、膨胀形态学处理,去噪后再进行平滑及阈值分割操作,最终获得较佳的红枣叶片病斑区域的分割方法。结果表明,基于HSV色彩空间的病斑分割方法能够有效地提取红枣叶片的病害特征,为机器视觉在红枣病虫害识别的应用中提供了依据。

In the python 3 environment under the OpenCV framework, a technology for processing leaf image of red jujube disease leaves has been developed. H, S, and V channel color component maps are separated by extracting the HSV color space of the red jujube disease area. For gray-scale images, the gray-scale images are etched and expanded morphologically, and then smoothed and thresholded after denoising. Finally, a better method for segmenting the diseased area of the jujube leaves is obtained. The results show that the lesion segmentation method based on HSV color space can effectively extract the disease characteristics of red jujube leaves, and provide a research basis for the application of machine vision in the identification of jujube diseases and insect pests.