Ensemble Learning Techniques for Improving Date Fruit Varieties Classification

Date fruits, valued for their nutritional and medicinal benefits, are experiencing increasing global demand. Efficient and accurate classification methods are crucial for maintaining high-quality production, but traditional manual approaches are often inadequate. This study explores the power of ensemble learning to enhance date fruit variety classification. We investigate two prominent ensemble techniques, Voting and Stacking, combined with seven distinct base models. Our results demonstrate the superior performance of ensemble methods, with the Stacking model achieving an impressive 95.56% accuracy. This approach offers a robust solution for addressing challenges like dataset imbalance and highlights the potential of ensemble learning to revolutionize agricultural quality control processes.