A WOA-Stacking Tobacco Leaf Grading Method Based on Multi-Heterogeneous Classifiers Ensemble

Abstract

Tobacco leaf grading is an essential step for the rational use of tobacco resources and enhancing tobacco benefits. 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 utilized the differences in data observation and training principles of heterogeneous classifiers and integrate them using the Whale Optimization Algorithm (WOA) to build 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. The 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.