玉溪烤烟‘K326’主要化学成分生态预测模型

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朱安琪^1,,
景元书^1,,,
胡保文²,
谢新乔²,
李湘伟²,
朱云聪²
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
基金项目: 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

详细信息

作者简介:朱安琪, 研究方向为农业气象、生态环境。E-mail: 2499295176@qq.com

通讯作者:景元书, 主要从事农业气象和生态环境研究。E-mail: jingyshu@163.com

中图分类号:S572;P49

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出版历程

收稿日期:2020-10-23
录用日期:2020-12-16
刊出日期:2021-05-01

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

ZHU Anqi^1,,
JING Yuanshu^1,,,
HU Baowen²,
XIE Xinqiao²,
LI Xiangwei²,
ZHU Yuncong²
1. Collaborative Innovation Center of Meteorological Disaster Forecasting Warning and Assessment/College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. Raw Material Department, Hongta Tobacco Co., Ltd., Yuxi 653100, China
Funds: the National Natural Science Foundation of China41575111
the Priority Academic Discipline Development ofJiangsu Higher Education Institutions2017-NY-038
Hongta Tobacco Group Co., Ltd. ProjectS-6019001

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Corresponding author:JING Yuanshu, E-mail: jingyshu@163.com

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摘要
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R²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R²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。
关键词:烤烟‘K326’/
化学成分/
生态因子/
预测模型
Abstract: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 algorithm for the tobacco leaf chemical composition ecological prediction model, the absolute error was used to intuitively show the difference between the simulated value of the BP neural network optimized by the Grey Wolf algorithm and that before optimization. The results showed that the prediction model of the mechanism algorithm could judge the degree of influence of the ecological factors on the tobacco chemical composition and indicate whether the influence was positive (promoting effect) or negative (adverse effect) by the size and the positive and negative values of the comprehensive index. The average R² value of the ecological prediction model of the mechanism algorithm was 0.29, the average root mean square error (RMSE) was 0.13, and only the RMSE of the reducing sugar was slightly greater than 0.2. These results indicated that the model understood the chemical composition characteristics of Yuxi flue-cured tobacco under particular ecological conditions in a given year. The absolute error of the ecologyical prediction model of the optimized intelligent algorithm was significantly smaller than that before optimization, indicating a better simulation effect for the optimized intelligent algorithm of the ecological prediction model. All R² values were greater than 0.95, and the R² values of the other prediction models (except for total nitrogen) were as high as 0.99. This suggested a very high degree of fit and that the model did well to explain the variability in the chemical composition; each RMSE was less than 0.1, and some values were less than 0.01, suggesting accurate prediction results.
Key words:Flue-cured tobacco 'K326'/
Chemical compositions/
Ecological factors/
Prediction model

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图1玉溪烤烟‘K326’烟碱含量(a, b)、总糖含量(c, d)、还原糖含量(e, f)、总氮含量(g, h)、钾含量(i, j)和氯含量(k, l)的智能算法生态预测模型结果和绝对误差
Figure1.Results and absolute errors of ecological prediction model based on intelligent algorithm for contents of nicotine (a, b), total sugar (c, d), reducing sugar (e, f), total nitrogen (g, h), potassium (i, j) and chlorine (k, l) of tobacco cultivar 'K326' in Yuxi

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表1玉溪烤烟‘K326’化学成分与生态因子的相关性
Table1.Correlation between chemical components of Yuxi flue-cured tobacco 'K326' and ecological factors

生态因子Ecological factor	相关系数Correlation coefficient
生态因子Ecological factor	烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine
4—9月降水量Precipitation from April to September	0.016	?0.577^**	0.209^*	0.309^**	0.346^**	?0.523^**
4—9月日照时数Sunshine hours from April to September	?0.354^**	0.514^**	0.053	?0.521^**	?0.355^**	0.217^*
4—9月平均气温Average temperature from April to September	?0.250^*	0.221^*	0.371^**	?0.273^**	?0.140	?0.129
最高气温Maximum temperature	?0.142	0.217^*	0.336^**	?0.276^**	?0.036	?0.098
最低气温Lowest temperature	?0.313^**	0.178	0.401^**	?0.243^*	?0.184	?0.179
日较差Daily range	0.327^**	0.085	?0.110	?0.078	0.285^**	0.152
相对湿度Relative humidity	0.128	?0.206	0.205	0.029	0.239^*	0.060
最小湿度Minimum humidity	0.135	?0.501^**	0.034	0.334^**	0.197	?0.207
7月平均气温Average temperature in July	?0.132	0.218^*	0.326^**	?0.222^*	?0.212^*	?0.103
伸根期降水量Precipitation during root extension period	0.247^*	?0.562^**	0.011	0.338^**	0.156	?0.562^**
伸根期日照时数Sunshine hours during root extension period	0.147	0.095	?0.110	?0.055	?0.220^*	?0.041
伸根期平均气温Average temperature during root extension period	?0.186	0.206	0.298^**	?0.228^*	?0.234^*	?0.139
旺长期降水量Precipitation during prosperous period	0.076	?0.020	?0.086	0.076	?0.438^**	0.102
旺长期日照时数Sunshine hours during prosperous period	?0.508^**	0.337^**	0.160	?0.437^**	0.115	0.238^*
旺长期平均气温Average temperature during prosperous period	?0.306^**	0.220^*	0.398^**	?0.310^**	0.005	?0.122
成熟期降水量Precipitation during maturity period	?0.028	?0.437^**	0.252^*	0.192	0.590^**	?0.373^**
成熟期日照时数Sunshine hours during maturity period	?0.204	0.505^**	?0.078	?0.457^**	?0.367^**	0.292^**
成熟期平均气温Average temperature during maturity period	?0.206	0.230^*	0.363^**	?0.262^*	?0.152	?0.107
土壤pH Soil pH	?0.243^*	0.378^**	?0.070	?0.270^**	?0.343^**	0.269^*
土壤有机质含量Soil organic matter content	0.027	?0.230^*	?0.041	?0.061	0.130	?0.026
土壤有效磷含量Soil available phosphorus content	?0.215^*	0.243^*	0.064	?0.107	?0.255^*	0.185
土壤速效钾含量Soil available potassium content	?0.009	0.121	?0.207	0.004	?0.329^**	0.087
土壤全氮含量Soil total nitrogen content	0.430^**	?0.263^*	?0.348^**	0.230^*	0.073	0.072
: P < 0.05; *: P < 0.01.

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表2气象因子与土壤因子对烟叶化学成分贡献率
Table2.Contribution rates of meteorological factors and soil factors to the chemical composition of tobacco leaves

	烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine
气象因子Meteorological factor (c)	0.7951	0.8486	0.8207	0.7769	0.7537	0.7418
土壤因子Soil factor (d)	0.2049	0.1514	0.1793	0.2231	0.2463	0.2582

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表3玉溪烤烟‘K326’烟叶化学成分最适含量值(C₀)
Table3.Optimal content values (C₀) of chemical componentsof Yuxi flue-cured tobacco 'K326'?%

烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine
烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine	2.5	28	22	2.5	2	0.3

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表4玉溪烤烟‘K326’化学成分含量预测结果
Table4.Prediction results of chemical composition content of flue cured tobacco 'K326' in Yuxi

统计量Statistic	烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine
实测值平均值Average of measured value (%)	2.53	28.50	22.10	2.19	2.10	0.45
预测值平均值Average of predicted value (%)	2.23	30.60	24.50	2.89	2.23	0.42
RMSE	0.09	0.16	0.20	0.16	0.09	0.07

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表5玉溪烤烟‘K326’化学成分与生态因子的最大信息系数
Table5.Maximum information coefficients of chemical components and ecological factors of flue cured tobacco 'K326'in Yuxi

生态因子Ecological factor	最大信息系数Maximum information coefficient
生态因子Ecological factor	烟碱Nicotine	总糖Total sugar	还原糖Reducing sugar	总氮Total nitrogen	钾Potassium	氯Chlorine
4—9月降水量Precipitation from April to September		0.7211	0.5119		0.5837	0.6645
4—9月日照时数Sunshine hours from April to September		0.5968		0.5106	0.6041	0.5164
4—9月平均气温Average temperature from April to September	0.6528	0.7976	0.6919	0.6862	0.8918	0.7605
最高气温Maximum temperature	0.7780	0.8251	0.8937	0.8467	0.7264	0.7593
最低气温Lowest temperature	0.6645	0.9599	0.6535	0.8834	0.9279	0.8947
日较差Daily range	0.6929	0.7422		0.5588	0.7003	0.9435
相对湿度Relative humidity	0.9400	0.6840	0.8753	0.9461	0.9694	0.7328
最小湿度Minimum humidity	0.7362	0.8542	0.7519	0.7493	0.7841	0.6663
7月平均气温Average temperature in July		0.5835	0.5669		0.5607	0.6004
伸根期降水量Precipitation during root extension period		0.6975				0.6895
伸根期日照时数Sunshine hours during root extension period		0.5443				0.6661
伸根期平均气温Average temperature during root extension period	0.5038	0.5634	0.5366	0.5776	0.6446	0.7373
旺长期降水量Precipitation during prosperous period		0.5979			0.6308	0.5921
旺长期平均气温Average temperature during prosperous period	0.5938	0.5564		0.5084	0.6346	0.5302
成熟期降水量Precipitation in maturity period		0.5968			0.7059	0.4998
成熟期日照时数Sunshine hours during maturity period		0.6758	0.5298	0.4989	0.5715	0.6282
成熟期平均气温Average temperature during maturity period	0.5357	0.5919	0.6308	0.6375	0.6806	0.5840
土壤全氮含量Soil total nitrogen content		0.7240			0.6159	0.7455

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表6两种算法的玉溪烤烟‘K326’化学成分生态预测模型验证
Table6.Verification of ecological prediction models of chemical compositions based on mechanism algorithm and intelligent algorithm of tobacco cultivar 'K326' in Yuxi

化学成分Chemical composition	机理算法Mechanism algorithm		智能算法Intelligent algorithm
化学成分Chemical composition	R²	RMSE	R²	RMSE
烟碱Nicotine	0.2621	0.0914	0.9986	0.0134
总糖Total sugar	0.3735	0.1622	0.9992	0.0559
还原糖Reducing sugar	0.1368	0.2041	0.9998	0.0173
总氮Total nitrogen	0.2091	0.1557	0.9499	0.0302
钾Potassium	0.3814	0.0873	0.9925	0.0187
氯Chlorine	0.3682	0.0746	0.9956	0.0041

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