基于叶片图像和环境信息的黄瓜病害识别方法

作物病害严重影响着作物的产量和质量，病害类型识别是病害防治的前提。利用图像处理和统计分析，提出了一种基于病害叶片图像和环境信息的黄瓜病害类别识别方法。采集不同季节、温度和湿度等环境下的病害叶片图像，并记录病害的环境信息；利用属性约简法提取病害叶片的5个环境信息特征向量，对病害叶片图像进行一系列图像处理，提取病斑图像的颜色、形状、纹理等35个统计特征向量。将两者结合得到黄瓜病害的40个特征分量。再利用统计分析系统（statistical analysis system，SAS）的判别分析方法，选择10个分类能力强的特征分量，计算作物病害的聚类中心分类特征向量。最后，利用最大隶属度准则识别病害叶片...

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Bibliographic Details
Published in农业工程学报 Vol. 30; no. 14; pp. 148 - 153
Main Author 王献锋 张善文 王震 张强
Format Journal Article
LanguageChinese
Published 西京学院工程技术系,西安,710123%西北农林科技大学林学院,杨凌,712100 2014
Subjects
判别分析
图像识别
环境信息
病害
黄瓜
病害
discriminant analysis
判别分析
environment information
cucumber
diseases
黄瓜
图像识别
环境信息
image recognition
Online AccessGet full text
ISSN1002-6819
DOI10.3969/j.issn.1002-6819.2014.14.019

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Summary:作物病害严重影响着作物的产量和质量，病害类型识别是病害防治的前提。利用图像处理和统计分析，提出了一种基于病害叶片图像和环境信息的黄瓜病害类别识别方法。采集不同季节、温度和湿度等环境下的病害叶片图像，并记录病害的环境信息；利用属性约简法提取病害叶片的5个环境信息特征向量，对病害叶片图像进行一系列图像处理，提取病斑图像的颜色、形状、纹理等35个统计特征向量。将两者结合得到黄瓜病害的40个特征分量。再利用统计分析系统（statistical analysis system，SAS）的判别分析方法，选择10个分类能力强的特征分量，计算作物病害的聚类中心分类特征向量。最后，利用最大隶属度准则识别病害叶片的病斑类别。对黄瓜的霜霉病、褐斑病和炭疽病3种叶部病害的识别率高达90%以上。试验结果表明，该方法能够有效识别作物叶部病害类别，可为田间开放环境下实现作物病害的快速自动识别提供依据。
Bibliography:Wang Xianfeng, Zhang Shanwen, Wang Zhen, Zhang Qiang（1. Department of Engineering and Technology, Xijing University, Xi＇an 710123, China 2. College of Forestry, Northwest A ＆ F University, Yangling 712100, China）
11-2047/S
Crop disease is one of the main disasters for Chinese agriculture and it seriously affects the yield and quality of crops, and causes economic losses to farmers. Early detection and prevention of crop diseases is critical to control the diseases, improve crop yields, reduce the economic losses and control pesticide pollution. Therefore, the research of recognition methods for crop diseases is necessary. In this study, a disease recognition method of cucumber disease, based on leaf image and environmental information, is proposed. In this method, the cucumber disease features, including environmental classifying features and disease leaf classifying features, were extracted by image processing and statistical analysis methods. The classifying features were then selected by SAS discriminant ana
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2014.14.019