摘要
基于高光谱图像技术与极限学习机(Extreme learning machine,ELM)模式识别方法构建一套生菜叶片氮素水平鉴别模型。利用3种不同氮浓度的营养液无土栽培各氮素水平生菜,在莲座期采集每类氮素水平生菜叶片各84片,利用高光谱图像采集系统采集生菜叶片高光谱图像,并在每个高光谱图像上选取叶片4个不同位置的60×60像素的感兴趣区域(ROI),求取感兴趣区域光谱数据平均值作为叶片样本的原始光谱,利用标准正态变量校正对原始光谱进行预处理,采用主成分分析法对光谱进行降维。采用ELM对训练样本进行建模,并与传统的BP及SVM算法模型进行对比。从实验结果可以看出,ELM模型训练时间和分类正确率分别为0.623 04 s和100%,在训练时间相当的情况下,ELM分类正确率高于SVM模型,在分类正确率相当的情况下,ELM模型的训练时间比BP模型要短。研究结果表明,基于高光谱图像技术及ELM可以构建生菜叶片氮素水平分类模型。
Discrimination of crop＇ s nitrogen level can contribute to reasonable and effective fertilization. Lettuces of various nitrogen levels were planted in three soilless nutrient solutions of different nitrogen concentrations. In the rosette stage, 84 lettuce leaves of each nitrogen level were collected and scanned by the hyperspectral imaging acquisition system. In every hyperspectral image of lettuce leaf, four different positions of 60 × 60 pixel were selected as regions of interest （ROI）. The average spectral data of the ROI were used as the original spectra of the leaf samples. The original spectra were preprocessed by the standard normal variate correction （SNV）, and their dimensionalities were reduced through principal component analysis （PCA）. ELM algorithm was used to establish model for the training samples, and then was compared with BP algorithm model and SVM algorithm model. The results show that the running time of ELM model is 0. 623 04 s and its classification accuracy rate is 100%. During the same running time, the classification accuracy rate of ELM model is higher than that of SVM model. At the same classification accuracy rate, the running time of ELM model is shorter than that of BP model.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2014年第7期272-277,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(31101082
61075036)
江苏高校优势学科建设工程资助项目PAPD(苏政办发2011 6号)
农业部农业信息技术重点实验室开放课题资助项目(2013007)
关键词
生菜叶片
高光谱图像
极限学习机
氮素
Lettuce leaf Hyperspectral imaging technology Extreme learning machine Nitrogen status
作者简介
孙俊，副教授，博士，主要从事计算机技术在农业工程中应用研究，E-mail：sun2000jun@ujs．edu．cn
