The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion

The diverse visual features of tobacco leaves during various curing stages are influenced by multiple factors such as the origin of the tobacco and the environment of the curing room, making precise identification challenging with single features or models. To address this issue, this study proposes a Bayesian Mixture Expert Recognition Model for Tobacco Leaf Curing Stages based on feature fusion. First, deep learning models (ResNet34, MobileNetV2, EfficientNetb0) are utilized to extract deep features and traditional features positively correlated with curing stages from a constructed tobacco leaf image dataset. Various feature fusion methods (concatenate fusion, scaled fusion, adaptive gated fusion) are employed to construct multi-level feature representations. Next, different feature fusion methods of the same model are optimized to select the best-performing model as the foundational model for ensemble learning. Finally, Bayesian optimization is applied to integrate three optimized models, and comparisons are made with voting and weighted averaging methods. The proposed model achieves a recognition accuracy of 93.96% on the test set, with other performance metrics surpassing those of the base models. This research efficiently captures and robustly recognizes the complex dynamic visual features of the tobacco curing process through the integration of diverse features, adaptive adjustments, and expert collaboration mechanisms, thereby enhancing the system's adaptability and interpretability in complex environments. This provides strong support for the intelligent upgrading of the tobacco industry.