A WOA-Stacking Grading Mode Based on Multi-heterogeneous Classifiers Ensemble

In recent years, there has been extensive research and application of artificial intelligence technology to replace manual grading. The intelligent classification device for tobacco leaves has been designed to ensure standardization and improve the intelligence of tobacco leaf grading. However, the existing tobacco leaf grading technology suffers from the problem of limited grade discrimination and low identification accuracy of single classifiers. To address these issues, we proposed a tobacco leaf grading method based on Whale Optimization Algorithm and multi-heterogeneous classifier Stacking ensemble (WOA-Stacking). In this paper, we leveraged the differences in data observation and training principles among heterogeneous classifiers and integrate them using the Whale Optimization Algorithm (WOA) to create a stacked ensemble model. The fitness function is calculated based on the classification performance matrix and the discriminant function. The optimal combination of base classifier and meta classifier is selected from the classifier pool composed of 9 heterogeneous classifiers, and a data-driven classification model is constructed. Subsequently, hyperparameter tuning and hierarchical coupling are performed by Grid Search (GS) and K-fold Cross-Validation (K-CV). Finally, the model is evaluated and validated by applying the tobacco leaf grade dataset. Comparative experimental results demonstrate that the WOA-Stacking model outperforms traditional single classification models, and achieves high accuracy in tobacco leaf grade classification. The model constructed in this paper can be applied as a data-driven general model for tobacco leaf grade classification in different regions, providing technical support towards industrialization of tobacco leaf grading.