基于K-means聚类和RF算法的葡萄霜霉病检测分级方法

针对自然环境复杂背景下葡萄霜霉病检测分级困难的问题，提出了一种基于语义分割结合K-means聚类和随机森林算法的葡萄霜霉病检测分级方法，实现对葡萄霜霉病快速分级。构建了葡萄霜霉病数据集，采用HRNet v2+OCR网络建立葡萄叶片语义分割模型，提取复杂环境下葡萄叶片；采用K-means聚类算法将葡萄叶片分解为若干子区域图像，并标记少量数据集进行随机森林算法学习，实现葡萄叶片病斑分割与提取；同时在叶片提取和病斑提取过程中，设计一种像素尺寸变换方法，解决图像分辨率引起的精度低问题。基于HRNet v2+OCR网络的葡萄叶片分割模型的准确率为98.45%,平均交并比为97.23%;融合K-means聚类和随机森林(RF)算法的葡萄叶片正面、反面和正反面霜霉病病害分级准确率分别为52.59%、73.08%和63.32%,病害等级误差小于等于2级时的病害分级准确率分别为88.67%、96.97%和92.98%。研究结果表明，基于K-means聚类和随机森林算法的葡萄霜霉病检测分级方法能够准确地分割自然环境复杂背景下的葡萄叶片和葡萄霜霉病病斑，并实现葡萄霜霉病分级，为葡萄霜霉病精准防治提供了方法和...

Grading Detection Method of Grape Downy Mildew Based on K-means Clustering and Random Forest Algorithm

Aiming at the difficulty of grape downy mildew grading detection under the complex background of natural environment, a method of grape downy mildew grading detection based on semantic segmentation combined with K-means clustering and random forest was proposed to realize the rapid grading of grape downy mildew. The image data set of grape downy mildew under the complex background of natural environment was constructed, and the semantic segmentation model of grape leaf was established by HRNet v2+OCR network to extract grape leaf image. The K-means clustering algorithm was used to decompose grape leaf image into several subregion images, and a small number of data sets were marked for random forest learning to realize grape leaf disease spot segmentation and extraction from leaf image. At the same time, in the process of grape leaf extraction and disease spot extraction, an image size transformation method was designed to solve the problem of low accuracy caused by image resolution. The accuracy of grape leaf segmentation model based on HRNet v2+OCR network was 98.45%, and the mean intersection over union was 97.23%. The accuracy rates of downy mildew grading of grape leaf front, back and both sides were 52.59%, 73.08% and 63.32%, respectively, and the accuracy rates of disease grade error less than or equal to grade 2 were 88.67%, 96.97% and 92.98%, respectively. The research results showed that the grape downy mildew grading detection method based on K-means clustering and random forest could accurately segment grape leaf and grape downy mildew spots under the complex background of natural environments, and achieve grape downy mildew rapid grading, providing method and model support for precise control of grape downy mildew.