广东农业科学2026，Vol.53Issue(1)：96-108,13.DOI:10.16768/j.issn.1004-874X.2026.01.009
基于GMM-PSO-RF的烟叶变黄程度预测与烤房环境因子耦合分析
Prediction of Yellowing Degree of Tobacco Leaves and Coupled Analysis of Environmental Factors in Curing Barn Based on GMM-PSO-RF
摘要
Abstract
[Objective]In order to improve the prediction accuracy of yellowing degree of tobacco leaves during curing process,the influence of environmental factors on the yellowing degree of upper and lower tobacco leaves was studied.[Method]Based on color threshold segmentation and gaussian mixture model(GMM),a stage segmentation algorithm was proposed to extract the yellowing degree of upper and lower tobacco leaf images during baking process.The particle swarm optimization(PSO)algorithm was used to optimize the hyperparameters of three machine learning algorithms:random forest(RF),support vector machine(SVR)and back propagation neural network(BPNN),and the prediction model of tobacco yellowing degree was constructed by combining the environmental factors(temperature,humidity and curing time)of curing barn.The SHAP method was used to interpret the optimal prediction model,and the relationship between the environmental factors of the curing barn and the yellowing degree of tobacco leaves was revealed.[Result]The mean absolute error(MAE)and mean square error(MSE)of the segmentation algorithm in the stage were 0.02407 and 0.00058,respectively,which were smaller than those of the single color threshold segmentation algorithm(0.07657,0.00588)and the single GMM algorithm(0.06541,0.00429).It has high extraction accuracy in extracting the yellowing degree of tobacco leaves.In the five-fold cross-validation,the PSO-RF model had the best prediction accuracy for the yellowing degree of the upper and lower tobacco leaves.For the upper layer model,the standard deviation and coefficient of variation for MAE,MSE,and r² were the smallest,which were 0.0073,0.0058,0.0066 and 0.0440,0.1246,0.0069,respectively.The standard deviation and coefficient of variation of MAE,MSE and r² in the lower layer model were also the smallest,which were 0.0062,0.0051,0.0052 and 0.0403,0.1181,0.0053,respectively.In the analysis of model prediction results,the accuracy of the model PSO-BPNN and PSO-SVR were r²<0.90,MSE>0.15,r²>0.90,MSE<0.15,respectively.The model PSO-RF has the highest accuracy(r2>0.95,MSE<0.06).SHAP analysis of the optimal PSO-RF model revealed that upper layer temperature and curing time were the key environmental factors influencing the yellowing degree of the upper and lower layers of tobacco leaves,respectively.[Conclusion]The GMM-PSO-RF model can accurately predict the yellowing degree of tobacco leaves in different sheds under complex baking environment,and provide scientific basis for the adjustment of baking process.关键词
图像处理/机器学习/烟叶/变黄程度/环境因子/阶段分割算法/模型解释Key words
image processing/machine learning/tobacco leaves/yellowing degree/environmental factors/stage segmentation algorithm/model explanation分类
农业科技引用本文复制引用
葛维一,周超毅,李静超,宋江雨,王廷贤,杨占伟,魏硕,林勇..基于GMM-PSO-RF的烟叶变黄程度预测与烤房环境因子耦合分析[J].广东农业科学,2026,53(1):96-108,13.基金项目
福建省烟草公司南平市分公司资助项目(NYK2023-03-03) （NYK2023-03-03）
