农业工程学报2012，Vol.28Issue(3)：172-176,5.DOI:10.3969/j.issn.1002-6819.2012.03.030
基于图像识别的小麦腥黑穗病害特征提取与分类
Feature extraction and classification of Tilletia diseases based on image recognition
摘要
Abstract
The identification of three types of diseases of Tilletia caries (DC.) Tul, Tilletia indica Mitra and Tilletia controversa Kiihn are important in the imports and exports inspection and quarantine for their harm to wheat production and human health. Three diseases were recognized and classified based on image analysis and pattern recognition techniques by using Tilletia diseases micrographs. Six typical patterns in sixteen features of shape and texture in the images of the disease infected spores were extracted. Minimum distance method, BP neural network and support vector machine (SVM) were used for the recognition and classification of 96 samples of Tilletia diseases infected spores images. The experimental results showed that the classification performance of SVM was superior to that of minimum distance method and BP neural network, the overall recognition accuracy reached up to 82.9%. Therefore, it is practicable to recognize and classify three types of Tilletia diseases by image analysis and SVM.关键词
图像识别/支持向量机/分类/特征提取/小麦腥黑穗病害Key words
image recognition/support vector machine/classification/feature extraction/Tilletia diseases分类
信息技术与安全科学引用本文复制引用
邓继忠,李敏,袁之报,金济,黄华盛..基于图像识别的小麦腥黑穗病害特征提取与分类[J].农业工程学报,2012,28(3):172-176,5.基金项目
质检公益性行业科研专项(200910008) （200910008）
