基于深度迁移学习的烟叶初烤过程中理化指标的预测方法OA

To achieve nondestructive and efficient monitoring in changes of physicochemical indexes of tobacco leaves during flue-curing process,a lightweight MobileNetV2_CBAM deep transfer learning regression model was proposed and tested.The model employed the pre-trained MobileNetV2 as its feature extraction backbone network and incorporated a convolutional block attention module(CBAM)and a deep regression head to enhance feature expression capability and nonlinear fitting ability.The proposed model was used to predict the contents of moisture,starch,reducing sugar,and total sugar in tobacco leaves during flue-curing,and the results showed that:1)Simultaneously incorporating CBAM and a deep regression head into the MobileNetV2 model significantly improved predictive performance.2)The MobileNetV2_CBAM model achieved 0.561 G floating-point operations(FLOPs)with total parameters of 15.43 M.The coefficients of determination(R²)for 4 predicted physicochemical indexes ranged from 0.865 1 to 0.906 5,the mean absolute error(MAE)ranged from 0.062 2 to 0.983 1,the root mean square error(RMSE)ranged from 0.092 7 to 2.043 5,residual prediction deviation(RPD)ranged from 2.594 3 to 4.646 2,and frames per second(FPS)ranged from 100.578 0 to 121.254 4.This model achieved a good balance between predictive performance,processing speed and deployment cost.3)Across different flue-curing stages,the MobileNetV2_CBAM model achieved an explanatory variance score ranging from 0.73 to 0.91 for the predicted results of the 4 physicochemical indexes,demonstrating strong predictive capability.4)In complex backgrounds,the MobileNetV2_CBAM model still had good predictive ability for the four indexes with an R² range of 0.788 5-0.870 1.This study provides a theoretical reference for online monitoring of physicochemical indexes during tobacco flue-curing.