基于MAFF-CNN-LSTM的雪茄烟叶晾制阶段识别方法OA

Manual visual assessment of the air-curing status of cigar tobacco leaves is highly experience-dependent and may lead to inaccurate stage identification.To address this issue,a MAFF-CNN-LSTM model was developed for identifying the air-curing stage of cigar tobacco leaves.The model integrates a multi-region adaptive feature fusion architecture with a convolutional neural network and long short-term memory networks.Images of cigar tobacco leaves were collected during the air-curing process using an image acquisition system.The image data were classified into five air-curing stages according to changes in leaf status.Sample diversity was increased using conventional data augmentation and Mixup-based augmentation.The proposed model was compared with CNN-LSTM,MAFF-CNN,CNN and ResNet50 models to evaluate its effectiveness.The results showed that:1)MAFF-CNN-LSTM model achieved recognition accuracies of 98.72%and 97.45%on the training and validation sets respectively for the five air-curing stages.On the test set,the model achieved an accuracy of 98.29%and an average F1 score of 98.22%.Compared with the CNN-LSTM and MAFF-CNN models,the proposed model improved the test accuracy by 4.40 and 1.53 percentage points respectively.2)Moreover,the MAFF-CNN-LSTM model produced far fewer misclassified samples than the other four models,and the misclassifications occurred only between adjacent air-curing stages.These results indicate that the proposed model can provide a reliable basis for precise regulation of temperature and humidity parameters at different air-curing stages.The model supports rapid and accurate identification of air-curing stages of cigar tobacco leaves and contributes to digital management of the cigar tobacco air-curing process.