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基于BP神经网络的烤烟外观质量预测模型

Construction of Flue-cured Tobacco Appearance Quality Prediction Model Based on Conventional Chemical Composition
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摘要 【目的】为探究烟叶常规化学成分与各单项外观质量评价指标之间的关系,为外观质量评价的智能精准化发展提供科学依据。【方法】对选取的2017年湖南烟区具有代表性的初烤烟叶样品进行常规化学成分的测量及外观质量评定,通过因子分析法对作为BP神经网络输入变量的常规化学成分进行筛选,分别构建拓扑结构为7-10-1的各单项外观质量指标预测模型。【结果】所选烟叶样品的常规化学成分含量和外观质量得分的统计分析符合正态分布,网络模型对样本的训练结果表明:各个单项外观质量评价指标预测模型中,网络模拟值与实际目标值之间的误差区间在0~0.5范围内的比例均达到60%以上,误差区间在0~1.0范围内的样本比例均达到90%以上,其中成熟度和色度的决定系数达到显著水平;颜色、身份、油分、叶片结构的决定系数达到极显著水平。【结论】基于烟叶常规化学成分含量,利用BP神经网络构建的各项外观质量指标预测模型具有较高的精准性。 【Objective】The present paper aimed to explore the relationship between the conventional chemical composition of tobacco and the evaluation indexes of the individual appearance quality,thus to promote the intelligent development of appearance quality evaluation.【Method】The measurement of the conventional chemical composition and the appearance quality assessment of the selected samples of the selected tobacco leaves in Hunan tobacco area in 2017 were conducted. Through factor analysis, we selected the regular chemical components as input variables of BP neural network, and constructed a single appearance quality index prediction model with 7-10-1 topology.【Result】The statistical analysis of the conventional chemical composition and the appearance quality of the selected tobacco samples accorded with the normal distribution. The network model of the sample training results showed that the evaluation index of each individual appearance quality prediction model, the proportion of the error between the actual value and the target value of the interval in the range of 0-0.5 were more than 60 % network simulation, the proportion of the sample in the range of 0-1.0 error interval reached more than 90 %. The R~2 of maturity and chromaticity reached a significant level, and the R~2 of color, identity, oil and leaf structure reached a very significant level.【Conclusion】The designed BP neural network model can accurately predict the appearance quality through the conventional chemical composition of the tobacco.
作者 李峥 王建峰 程小强 段史江 史文强 胡蓉花 肖荣贵 申洪涛 LI Zheng;WANG Jian-feng;CHENG Xiao-qiang;DUAN Shi-jiang;SHI Wen-qiang;HU Rong-hua;XIAO Rong-gui;SHEN Hong-tao(College of Tobacco Science,Henan Agricultural University,Henan Zhengzhou 450002,China;Jian Tobacco Company of Jiangxi Province, Jiangxi Jian 343009,China;Henan Tobacco Industry Co.,Ltd.,Henan Zhenghzhou 450016,China)
出处 《西南农业学报》 CSCD 北大核心 2019年第3期653-658,共6页 Southwest China Journal of Agricultural Sciences
基金 中国烟草总公司江西省公司资助项目(赣烟司[2017]66号) 河南中烟科技创新项目(ZW2015006).
关键词 烤烟 BP神经网络 外观质量 常规化学成分 预测模型 Flue-cured tobacco BP neural network Appearance quality Conventional chemical composition Prediction model
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