关键词: 烤烟单叶重, 气象要素, 算法模型, BP神经网络, 随机森林

Abstract:

The meteorological and tobacco leaf weight data from 53 districts (counties) in the tobacco-growing region of central and eastern Guizhou from 2010 to 2021 were selected to analyze the influence of meteorological factors on the leaf weight of flue-cured tobacco. Four linear and non-linear models were established using four meteorological factors on an annual scale (four-factor model) and multiple meteorological factors on a ten-day scale (multi-factor model), respectively. The accuracy and stability of the models were validated to explore the advantages and disadvantages of different models in the central and eastern regions of Guizhou. The results indicated: (1) the average single leaf weight of the lower, cutters, and upper leaves in the tobacco-growing areas of central and eastern Guizhou in the past 12 years were 5.7g, 9.2g, and 10.8g, respectively. The variation range in single leaf weight was smaller in the lower leaves than in the cutters and upper leaves. (2) Based on the modeling with four factors, the BP neural network algorithm model for the single leaf weight of lower leaves showed the best simulation effect, with R² of 0.26 and RMSE of 0.84g. The simulation effect of the random forest algorithm model exhibited the best simulation performance, with R² of 0.33 and RMSE of 1.08g for single leaf weight in the cutters leaf, and R² of 0.16 and RMSE of 1.59g for single leaf weight in the upper leaf. When model with multiple factors, the random forest algorithm model for the single leaf weight of the lower and upper leaves performed best, with R² of 0.22 and RMSE of 0.85g for the lower leaves and R² of 0.16 and RMSE of 1.57g for the upper leaves. For the cutters leaves, the BP neural network algorithm model demonstrated the optimal simulation effect, with R² of 0.18 and RMSE of 1.14g. (3) The accuracy of the multiple-factor stepwise regression algorithm for predicting the single-leaf weight of the lower leaves was 34.86%, while the simulation accuracy of other algorithms for the same leaf position was generally above 70%. The multi-factor BP neural network model predicted the highest accuracy of 86.24% in the lower single leaf weights, while the random forest model predicted the highest accuracy of 89.91% in the cutters single leaf weights under four-factor. The random forest algorithm with multifactor had the highest prediction accuracy of 84.4% for the upper leaf weight. (4) Based on the four-factor algorithm model for predicting the single-leaf weight in the central and eastern regions of Guizhou in 2021, the accuracy of the four algorithmic models for predicting the single-leaf weight of the cutters and lower leaves showed little difference. The accuracy of the random forest algorithm for predicting the single-leaf weight of the upper leaves was the highest, reaching 91%, which was significantly higher than the other three algorithmic models. Under the multi-factor condition, the average simulated accuracy of the various models for the lower, middle, and upper parts was 81%, 74%, and 85% respectively. (5) At the city-level dimension, under the condition of four factors, the average accuracy of the BP neural network model for simulating the single-leaf weight of the lower leaves was 84%. There was no significant difference in the simulation results of the four algorithmic models for the cutters leaves. The upper single leaf weight simulation resulted in the highest average accuracy of 92% with the random forest algorithm. Under the condition of multiple factors, the average accuracy of the linear regression model for simulating the single leaf weight of the lower leaves was 91%, while for the upper leaves, the linear regression model also had the highest average accuracy of 88%. In summary, for the average single-leaf weight of tobacco leaves in the central and eastern regions of Guizhou, using the four-factor BP neural network and random forest algorithm can better simulate the impact of meteorological factors on the single-leaf weight of tobacco leaves, providing scientific basis for the regulation of tobacco leaf production.

Key words: Single leaf weigh of flue-cured tobacco, Meteorological factors, Algorithmic model, BP neural network, Random forest