基于联合稀疏模型的黄瓜病害自动识别
|
摘要
提取黄瓜7种叶部病害图像颜色、形状和纹理的共26种特征进行研究,发现不同形式的特征在用同一样本集合稀疏表示时,它们的稀疏系数有着相似的结构。通过引入联合稀疏模型构造方程,对这一规律进行数学描述,使用加速近端梯度法求解联合稀疏系数,最后借助重构误差来实现病害识别。试验表明,这一算法的正确识别率达到90.67%,较稀疏表示分类算法提高5.7%,计算消耗时间7.5 s,较稀疏表示分类算法缩短4.3 s。
Twenty-six color,shape and texture features were extracted from seven kinds of cucumber disease leaf.It was found that the sparsity coefficients for different features had similar structures when they were sparse represented by the same training set.By introducing the joint sparse model to construct the cost equation,thus the regularity was summarized in mathematics.The joint sparse coefficients were solved by using the accelerated proximal gradient method.Finally,disease recognition was realized by means of reconstruction error.Experiments demonstrated that the correct recognition rate of this algorithm reaches 90.67%,which is 5.7% higher than that of the sparse representation classification algorithm,and the computational consumption time is 7.5 s,shortening 4.3 s than that of the sparse representation classification algorithm.
Twenty-six color,shape and texture features were extracted from seven kinds of cucumber disease leaf.It was found that the sparsity coefficients for different features had similar structures when they were sparse represented by the same training set.By introducing the joint sparse model to construct the cost equation,thus the regularity was summarized in mathematics.The joint sparse coefficients were solved by using the accelerated proximal gradient method.Finally,disease recognition was realized by means of reconstruction error.Experiments demonstrated that the correct recognition rate of this algorithm reaches 90.67%,which is 5.7% higher than that of the sparse representation classification algorithm,and the computational consumption time is 7.5 s,shortening 4.3 s than that of the sparse representation classification algorithm.
引文
[1]RUMPF T,MAHLEIN A K,STEINER U,et al.Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance[J].Computers and Electronics in Agriculture,2010,74(1):91-99.
[2]BASHISH D A,BRAIK M,BANI S.Detection and classification of leaf diseases using K-means-based segmentation and neural-networks based classification[J].Information Technology Journal,2011,10(2):267-275.
[3]ZHANG S W,SHANG Y J,WANG L.Plant disease recognition based on plant leaf image[J].Journal of Animal&Plant Sciences,2015,25(3):42-45.
[4]ZHANG S,WU X,YOU Z,et al.Leaf image based cucumber disease recognition using sparse representation classification[J].Computers and Electronics in Agriculture,2017,134:135-141.
[5]郭小清,范涛杰,舒欣.基于图像融合特征的番茄叶部病害的识别[J].湖南农业大学学报(自然科学版),2019,45(2):212-217,224.
[6]秦立峰,何东健,宋怀波.词袋特征PCA多子空间自适应融合的黄瓜病害识别[J].农业工程学报,2018,34(8):200-205.
[7]XUE Y,LIAO X,CARIN L,et al.Multi-task learning for classification with dirichlet process priors[J].Journal of Machine Learning Research,2007,8(1):35-63.
[8]ZHANG Y,YEUNG D Y.Multi-task warped Gaussian process for personalized age estimation[C]//IEEEConference on Computer Vision and Pattern Recognition.New York:IEEE,2010:2622-2629.
[9]ZHANG Y,YEUNG D Y.A regularization approach to learning task relationships in multitask learning[J].ACMTransactions on Knowledge Discovery from Data,2014,8(3):1-31.
[10]施文,邹锐标,王访,等.基于多重分形的油菜病虫害叶片图像分割[J].湖南农业大学学报(自然科学版),2014,40(5):556-560.
[11]BISHOP C M.Pattern Recognition and Machine Learning(Information Science and Statistics)[M].New York:Springer-Verlag,2006.
[12]杨劲峰,陈清,韩晓日,等.数字图像处理技术在蔬菜叶面积测量中的应用[J].农业工程学报,2002,18(4):155-158.
[13]冈萨雷斯.数字图像处理(中文版)[M].2版.北京:电子工业出版社,2006.
[14]管泽鑫,唐健,杨保军,等.基于图像的水稻病害识别方法研究[J].中国水稻科学,2010,24(5):497-502.
[15]田凯,张连宽,熊美东,等.基于叶片病斑特征的茄子褐纹病识别方法[J].农业工程学报,2016,32(增刊1):184-189.
[16]JI H,LING H B,WU Y,et al.Real time robust L1tracker using accelerated proximal gradient approach[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2012:1830-1837.
[17]MASSA A,ROCCA P,OLIVERI G.Compressive sensing in electromagnetics:a review[J].IEEE Antennas and Propagation Magazine,2015,57(1):224-238.
[2]BASHISH D A,BRAIK M,BANI S.Detection and classification of leaf diseases using K-means-based segmentation and neural-networks based classification[J].Information Technology Journal,2011,10(2):267-275.
[3]ZHANG S W,SHANG Y J,WANG L.Plant disease recognition based on plant leaf image[J].Journal of Animal&Plant Sciences,2015,25(3):42-45.
[4]ZHANG S,WU X,YOU Z,et al.Leaf image based cucumber disease recognition using sparse representation classification[J].Computers and Electronics in Agriculture,2017,134:135-141.
[5]郭小清,范涛杰,舒欣.基于图像融合特征的番茄叶部病害的识别[J].湖南农业大学学报(自然科学版),2019,45(2):212-217,224.
[6]秦立峰,何东健,宋怀波.词袋特征PCA多子空间自适应融合的黄瓜病害识别[J].农业工程学报,2018,34(8):200-205.
[7]XUE Y,LIAO X,CARIN L,et al.Multi-task learning for classification with dirichlet process priors[J].Journal of Machine Learning Research,2007,8(1):35-63.
[8]ZHANG Y,YEUNG D Y.Multi-task warped Gaussian process for personalized age estimation[C]//IEEEConference on Computer Vision and Pattern Recognition.New York:IEEE,2010:2622-2629.
[9]ZHANG Y,YEUNG D Y.A regularization approach to learning task relationships in multitask learning[J].ACMTransactions on Knowledge Discovery from Data,2014,8(3):1-31.
[10]施文,邹锐标,王访,等.基于多重分形的油菜病虫害叶片图像分割[J].湖南农业大学学报(自然科学版),2014,40(5):556-560.
[11]BISHOP C M.Pattern Recognition and Machine Learning(Information Science and Statistics)[M].New York:Springer-Verlag,2006.
[12]杨劲峰,陈清,韩晓日,等.数字图像处理技术在蔬菜叶面积测量中的应用[J].农业工程学报,2002,18(4):155-158.
[13]冈萨雷斯.数字图像处理(中文版)[M].2版.北京:电子工业出版社,2006.
[14]管泽鑫,唐健,杨保军,等.基于图像的水稻病害识别方法研究[J].中国水稻科学,2010,24(5):497-502.
[15]田凯,张连宽,熊美东,等.基于叶片病斑特征的茄子褐纹病识别方法[J].农业工程学报,2016,32(增刊1):184-189.
[16]JI H,LING H B,WU Y,et al.Real time robust L1tracker using accelerated proximal gradient approach[C]//IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE,2012:1830-1837.
[17]MASSA A,ROCCA P,OLIVERI G.Compressive sensing in electromagnetics:a review[J].IEEE Antennas and Propagation Magazine,2015,57(1):224-238.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |