计算机应用研究Issue(11)：3336-3340,5.DOI:10.3969/j.issn.1001-3695.2015.11.031
利用 CBIA 与 WSN 构建的植物叶片分类系统
Plant leaf classification system using CBIA and WSN
摘要
Abstract
In order to use computer aided plant leaf classification algorithms in a practical way,this paper introduced a plant leaf classification system using content-based image analysis and wireless sensor network (WSN)techniques.First,it used a Sobel edge detector based full-automatic image segmentation method to obtain the accurate shapes of leaves.Second,it applied a Hotelling transform based method to rotate the obtained shapes and extracts nine shape features,including fourier descriptor and so on.Thirdly,it indentified different classes of leaves by a multi-class support vector machine classifier and evaluates the classification result by the classification accuracy.Furthermore,it used an early fusion approach to enhance the classification result by combine different features.Fourthly,it used the above classification method as the core technique to establish a WSN. Finally,it applied Java and Android techniques to implement an internet application on the mobile client.In experiments,it ob-tained good classification accuracies of 80% on two datasets,which were similar to that in other previous researches.Further-more,it designed a brief WSN framework and was able to finish a data transmission in 9 seconds.Lastly,it used Java technique to implement an application in Android system for image capturing and data transmission.In conclusion,this paper shows a re-markable result in the current phase,and it will be improved by more effective methods in the future work.关键词
基于内容的图像分析/叶片分类/图像分割/特征提取/支持向量机/特征融合/无线传感器网络Key words
content-based image analysis/leaf classification/image segmentation/feature extraction/support vector ma-chine/feature fusion/wireless sensor network分类
信息技术与安全科学引用本文复制引用
李晨,姚玮,韩忠伟,高一鸿,Florian Schmidt,蒋涛,丁惠君,王振宇,申旻旻..利用 CBIA 与 WSN 构建的植物叶片分类系统[J].计算机应用研究,2015,(11):3336-3340,5.基金项目
国家自然科学基金青年项目（61302121，61201440）；广东省自然科学基金自由申请项目（S2012010010295）；广东省新媒体与品牌传播创新应用重点实验室项目（2013WSYS0002）；广东省教育部产学研合作专项基金资助项目（2012B091100420）；吉林省大学生创新项目 （）
