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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