烟草科技2016，Vol.49Issue(7)：8-13,6.DOI:10.16135/j.issn1002-0861.20160702
基于BP神经网络的烟草叶片质体色素高光谱反演
Hyperspectral inversion to estimate plastid pigment contents in tobacco leaves based on BP neural network
摘要
Abstract
To promote the application of hyperspectral remote sensing technology to tobacco quality monitoring, experiments were conducted using different light quality to examine cv. Yunyan 87, the contents of plastid pigments (chlorophyll a, chlorophyll b and carotenoid) and their corresponding hyperspectral reflectances of flue-cured tobacco leaves at different growing stages. Hyperspectral reflectance data was reduced from 2 151 to 21 by wavelet coefficient extraction, then the reduced inversion models based on back propagation neural network for predicting the contents of chlorophyll a, chlorophyll b, carotenoid in tobacco leaves were established with plastid pigments as output factors and reduced hyperspectral reflectance data as input factors. The training function adopted trainlm of L-M optimization algorithm, while the transfer functions of the input layer and output layer were tansig and purelin, respectively. The number of the hidden layer nodes in BP neural network models for chlorophyll a, chlorophyll b and carotenoid was 27, 32 and 45, respectively. The results showed that the determination coefficients of BP neural network models for chlorophyll a, chlorophyll b and carotenoid were 0.84, 0.86 and 0.76; the root mean square errors were 0.12, 0.14 and 0.10; the absolute mean relative errors were 0.23, 0.21, and 0.15, respectively. The three models well predict the contents of plastid pigments with high precisions and low errors.关键词
高光谱/BP神经网络/烟草/质体色素Key words
Hyperspectrum/BP neural network/Tobacco/Plastid pigment分类
农业科技引用本文复制引用
贾方方,张黎明,任天宝,李梦竹,杨艳东,刘国顺..基于BP神经网络的烟草叶片质体色素高光谱反演[J].烟草科技,2016,49(7):8-13,6.基金项目
广东中烟工业有限责任公司基金资助项目“基于‘双喜’品牌原料需求的湘西山地特色烟叶开发关键技术研究”［粤烟工15XM-QK（2013）-01］。 （）
