Using pretrained models in ensemble learning for date fruits multiclass classification

Abstract

Date fruits are a primary agricultural product that comes in a variety of textures, colors, and tastes; hence, the correct classification is crucial for quality control, automatic sorting, and commercial applications. Deep learning has surely shown critically improved image classification duties. In this research, the classification of nine different date fruit types by means of four well-known convolutional neural networks (CNNs), that is, DenseNet121, MobileNetV2, ResNet18, and VGG16 as well as an ensemble learning approach was objected. It is evaluated the proposed Dirichlet Ensemble which entails the predictions from the individual CNN models and the baseline architecture across multiple epochs. Toward the assessment, the accuracy, precision, recall, and F1-score were used. The results of the experiments revealed that the Dirichlet Ensemble is better than any single model out there with an accuracy of 98.61%, precision of 98.71%, recall of 98.61%, and an F1-score of 98.62%. DenseNet121 and MobileNetV2 were the standalone models with the highest accuracy of 96.92% and 95.83%, respectively, which is why they are very useful for a limited computing system. ResNet18 was by far the best model with a final accuracy of 92.35% and even outperformed VGG16 by 16%. VGG16's unsatisfactory performance with an accuracy of 73.24% clearly indicates its inability to handle complex classification tasks. The present work also showed the effectiveness of ensemble learning in enhancing the accuracy and robustness of classification. Future research could be investigating more advanced ensemble strategies and fine-tuning techniques to improve the generalization of modeling in food classification applications.