Abstract: 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.