摘要
柑橘黄龙病的传统诊断方法主要依赖于人眼经验及生化技术,前者凭经验,诊断快,但准确性低;后者准确性高,但效率低和成本高.本文采用高光谱成像技术,获取5种症状柑橘叶片的高光谱图像,采用基于主成分分析和BP神经网络相结合的方法,对370-988nm波段范围内的柑橘叶片高光谱图像进行了病状的无损检测.研究结果表明,柑橘叶片的高光谱图像存在很大冗余,前四个主成分累积方差贡献率达到97.42%.数据建模分类得表明：BP神经网络的分类准确率达85%以上,经主成分后再利用BP神经网络的分类准确率绝大部分达到90%以上.因此,利用高光谱成像技术进行柑橘黄龙病的早期诊断具有较高的可行性.
To address the limitations of conventional techniques,a method of principal component analysis and BP neural network was discussed to diagnose and classify citrus HuangLongBing.Data was obtained by a hyperspectral imaging system with the wavelength range of 370-988nm,its high dimension data was reduced by principal component analysis,and then BP neural network was used to model for classification.The results showed that the first four principal components cumulative variance contribution rate achieved 97.42%.On one hand,BP neural network classification accuracy rate achieved 85% or more;on the other hand,after the principal component analysis,classification of BP neural network accuracy substantially was more than 90%.This method for nondestructive testing of citrus HuangLongBing is feasible.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2014年第4期10-17,共8页
Acta Photonica Sinica
基金
国家自然科学基金(青年基金)(No.31201129)
现代农业产业技术体系建设专项资金(No.CARS-27)
广东省教育厅高校优秀青年创新人才培养计划(No.2012LYM_0028)
教育部高等学校博士学科点专项科研基金(No.20124404120006)
广东省科技计划项目(No.2011B-020308009)资助
关键词
柑橘黄龙病
光谱学
高光谱图像
无损检测
主成分分析
BP神经网络
Citrus HuangLongBing
Spectroscopy
Hyperspectral imaging
Nondestructive testing
Principal Component Analysis（PCA）
Back Propagation Neural Network（BPNN）
