摘要
为了减少黄瓜叶部病害给农业生产带来的损失,提高病害的识别率和精度,提出了一种基于颜色特征和属性约简算法的黄瓜病害叶片分割与识别方法。该方法首先利用最大类间方差(Otsu)阈值法对黄瓜病害叶片图像进行病斑分割;其次提取病斑图像的36个分类特征,再利用基于区分矩阵的属性约简算法进行特征选择;最后利用最近邻分类器进行病害识别。该方法在5种常见黄瓜病害叶片图像数据库上进行了病害识别试验,结果表明,识别率高达94.8%。说明,该方法对作物病害叶片图像识别是有效可行的。
To reduce the loss caused by cucumber leaf disease, and improve disease recognition rate and accuracy, a leaf image segmentation and disease recognition method for cucumber was proposed based on color feature and attribute re-duction algorithm. Firstly, the Otsu algorithm was applied to segment the cucumber diseased leaf image. Secondly, 36 di-agnostic characters of disease lesion were extracted, and selected by using attribute reduction algorithm based on discern-ibility matrix. Finally, the crop diseases were recognized using the nearest neighbor classifier. As an effective and feasible approach for crop disease recognition, this method could recognize as high as 94. 8% of five cucumber diseases.
出处
《江苏农业学报》
CSCD
北大核心
2015年第3期526-530,共5页
Jiangsu Journal of Agricultural Sciences
基金
国家自然科学基金项目(61272333)
河南省科技厅科技攻关项目(142102310518
142400410853
142300410309)
河南省教育厅科学技术研究重点项目(14B520064
15A520100)
郑州大学西亚斯国际学院校级科研项目(2014KYYB23)
关键词
颜色特征
属性约简
病斑分割
病害识别
color feature
attribute reduction
image segmentation
disease recognition
作者简介
谢泽奇（1981-），男，河南镇平人，硕士，讲师，研究方向为计算机应用、图像处理。（E—mail）xzq0413@163．com
张会敏，（E—mail）zhm0413@163．com
