玉溪烤烟‘K326’主要化学成分生态预测模型  

Ecological prediction model of main chemical components of Yuxi flue-cured tobacco‘K326’

作　　者：朱安琪 景元书[1] 胡保文 谢新乔 李湘伟 朱云聪 ZHU Anqi;JING Yuanshu;HU Baowen;XIE Xinqiao;LI Xiangwei;ZHU Yuncong(Collaborative Innovation Center of Meteorological Disaster Forecasting Warning and Assessment/College of Applied Meteorology,Nanjing University of Information Science&Technology,Nanjing 210044,China;Raw Material Department,Hongta Tobacco Co.,Ltd.,Yuxi 653100,China)

机构地区：[1]气象灾害预报预警与评估协同创新中心/南京信息工程大学应用气象学院,南京210044 [2]红塔烟草(集团)有限责任公司原料部,玉溪653100

出　　处：《中国生态农业学报（中英文）》2021年第5期880-889,共10页Chinese Journal of Eco-Agriculture

基　　金：国家自然科学基金项目(41575111);江苏省高校优势学科建设工程(PAPD)项目(2017-NY-038);红塔烟草集团有限责任公司项目(S-6019001)资助。

摘　　要：为了解烟叶化学成分与生态因子之间的定量化关系,提高烤烟品质评价的智能化程度,使用2009—2017年玉溪市9个烤烟‘K326’典型定位点烟叶主要化学成分(烟碱、总糖、还原糖、总氮、钾、氯)数据与对应不同生育期的生态因子(气象和土壤)数据,分析得到生态因子影响综合指数,在此基础上建立了烟叶各化学成分机理生态预测模型。根据2018年生态因子数据,预测了各定位点烟叶主要化学成分含量,并与实测值进行比较。同时,使用相同的90个烤烟定位点数据,利用最大信息系数(maximum information coefficient, MIC)筛选输入变量,使用经过灰狼算法优化的BP神经网络建立智能算法的烟叶化学成分生态预测模型。机理算法的生态预测模型R~2平均值为0.29, RMSE平均值为0.13,只有还原糖RMSE略大于0.2;智能算法的生态预测模型R~2均大于0.95, RMSE均小于0.1。结果表明智能算法的生态模型预测效果优于机理算法的生态模型,能够为烤烟品质提升与调优栽培管理提供一定理论支撑。Due to national policies and adjustments to the industrial structure,the tobacco industry has implemented“quality optimization,planting regionalization,and technological intelligence”process requirements.To better meet these requirements,understand the quantitative relationships between tobacco chemical components and ecological factors,and improve the intelligence degree of flue-cured tobacco quality evaluation,it is necessary to develop an ecological prediction model of the chemical composition of tobacco leaves that corresponds with the actual production of Yuxi flue-cured tobacco.While prior research has only considered the impact of a single ecological factor(weather or soil)on the chemical composition of tobacco leaves,this study used the main chemical components(nicotine,total sugar,reducing sugar,total nitrogen,potassium,and chlorine)of flue-cured tobacco‘K326’in nine typical locations from 2009 to 2017 in the Yuxi area and ecological data(weather and soil)corresponding to the different growth periods.These factors were analyzed to obtain a comprehensive index of the influential ecological factors and to establish an ecological prediction model of the chemical composition mechanisms of tobacco leaves.Using the ecological data from 2018,the content of main chemical components in the tobacco leaves was predicted and compared with the observed values.Data from 90 flue-cured tobacco samples were used to calculate the maximum information coefficient(MIC)to screen the input variables;this method ensures the integrity of the input parameters and is not limited to specific function types(e.g.,a linear function)as long as there is a significant functional relationship between the ecological factors and chemical components.To overcome the shortcomings of the back-propagation(BP)neural network(i.e.,it is easy to fall into local minima and slow convergence speed),the Grey Wolf optimizer was used in the modeling process to optimize the weights and thresholds of the neural network.To establish an intelligent al

关 键 词：烤烟‘K326’ 化学成分 生态因子 预测模型 

分 类 号：S572[农业科学—烟草工业] P49[农业科学—作物学]