摘要
植物叶片识别是植物自动分类识别研究的重要分支和热点,利用卷积神经网络进行图像分类研究已成为主流。为了提高植物叶片识别准确率,提出了基于多特征融合和卷积神经网络的植物叶片图像识别方法。首先对植物叶片图像进行预处理,提取LBP特征和Gabor特征,将多特征相加融合输入网络进行训练,使用卷积神经网络(AlexNet)构架作为分类器,利用全连接层对植物叶片进行识别。为了避免过拟合现象,使用"dropout"方法训练卷积神经网络,通过调节学习率、dropout值、迭代次数优化模型。实验结果表明,基于多特征融合的卷积神经网络植物叶片识别方法对Flavia数据库32种叶片和MEW2014数据库189种叶片识别分类效果较好,平均正确识别率分别为93.25%和96.37%,相比一般的卷积神经网络识别方法,该方法可以提高植物叶片的识别准确率,鲁棒性更强。
Plant leaf recognition is an important branch and hotspot of plant automatic classification and recognition.In order to improve the accuracy of plant leaf recognition,a method of multi-feature fusion combined with convolution neural network is proposed.In the experiment,Firstly,we extract LBP features and Gabor features.The leaf LBP coding map is evenly divided into 7×7 blocks,and different weights are assigned.The LBP histogram of each sub-block is calculated,and then normalized,and the normalized histogram of all sub-blocks is connected to obtain the whole Histogram and feature map.We use a four-direction Gabor filter to filtering the leaf image to obtain four sub-images.Each sub-image is divided into 4×4 sub-blocks,the average and variance of the filtered energy values of each sub-block are calculated,obtaining 128 dimensional Gabor features.Then the image of plant leaves is preprocessed to size 227×227 pixels with labels,local binary patterns features and Gabor features are extracted,the multi-features are added and fused through the feature fusion layer.Then the convolution neural network(AlexNet)framework is used as the classifier,and the full connection layer is used to identify the plant leaves.In order to avoid over fitting phenomenon,"dropout"method is used to train convolutional neural network,optimizing training model by adjusting learning rate and dropout value.The experimental results show that the plant recognition method based on multi-feature fusion convolution neural network classifies 32 kinds of leaves in Flavia leaves database and 189 kinds of leaves in MEW2014 leaves database,with an average correct recognition rate of 93.25%and 96.37%,respectively.This shows that compared with the general convolution neural network recognition method,this method can improve the recognition accuracy and robustness of plant leaves.
作者
韩斌
曾松伟
HAN Bin;ZENG Song-wei(School of Information Engineering,Zhejiang A&F University,Hangzhou 311300,China;Zhejiang Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research,Zhejiang A&F University,Hangzhou 311300,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S01期113-117,共5页
Computer Science
基金
国家自然科学基金两化融合重点项目(U1809208)
浙江省自然科学基金公益性项目(GN18C200030)。
关键词
植物叶片识别
LBP
GABOR
卷积神经网络
深度学习
Leaf image recognition
Local binary patterns
Gabor
Convolutional neural network
Deep learning
作者简介
通信作者:曾松伟,born in 1975,Ph.D,associate professor.His main research interests include electronic information system integration and research on application of agriculture and forestry internet of things.zsw@zafu.edu.cn;韩斌,born in 1980,postgraduate.His main research interests include pattern recognition and intelligent information processing.hanbin@zafu.edu.cn。
