Adaptive classification and grading model of cigar wrapper leaf based on improved ResNet algorithm.

The grading of cigar wrapper leaves is critical for ensuring product quality, yet lacks a mature intelligent grading system, relying predominantly on manual sorting with inherent limitations such as low efficiency and inconsistent standards. This study proposed a deep learning-driven framework for automated classification of cigar wrapper leaves, using the dataset comprising 8,637 leaf images across nine conventional purchasing grades of the popular cultivar FX-01 grown in Longyan, Fujian Province, China. The proposed model integrated a Mask4Ch (Mask-augmented Fourth Channel) module into the ResNet-50 backbone, incorporating leaf segmentation masks to enhance feature extraction. A dual-head training strategy combining Cross-Entropy (CE) for multi-class classification and Cumulative Ordinal Regression (CORAL) for ordinal constraint modeling was employed, with inference results fused using a 0.7:0.3 probability weighting consistent with training. Training optimizations included weighted random sampling (WRS) for class balancing and exponential moving average (EMA) with delayed activation for improved generalization. The model achieved 94.39% accuracy, 0.950 macro-averaged F1-score, 0.964 weighted Kappa (QWK), and 0.985 mean Average Precision (mAP) on the test set, validating the power of image-based deep learning for cigar wrapper grading. This research provided technical foundations for optimizing grading systems and mobile deployment, and advanced automation and standardization in cigar production.