A Novel 1D-DenseNet-Like Architecture for Enhanced Raisin Grain Classification

This study introduces a novel sparse one-dimensional dense convolutional network-like (1D-DenseNet) architecture, optimized explicitly for raisin variety classification. By combining advanced deep learning techniques with a concise set of seven pre-extracted morphological features, our method significantly enhances classification accuracy. The 1D-DenseNet-like model employs a consistent number of filters across its Dense and Transition blocks, optimizing performance while minimizing complexity. Experimental results demonstrate the superiority of our approach, achieving a remarkable average accuracy of 92.55%. This significant improvement outperforms the best models in previous studies, including Competitive Layer Neural Network (CLNN), as well as top traditional machine learning models like Random Forest and Support Vector Machine, which achieved accuracies of 88.34%, 85.22%, and 86.44%, respectively. These findings highlight the effectiveness of our method in addressing the complexities of raisin variety classification and emphasize the potential of combining deep learning with morphological features for more accurate and efficient solutions in this domain.