Multi-modal expert system for automated durian ripeness classification using deep learning - Santi Sukkasem; Watchareewan Jitsakul; Phayung Meesad (Intelligent Systems with Applications)
Accurate classification of durian ripeness is essential for quality control and minimizing post-harvest losses. Manual inspection remains subjective and inconsistent, prompting the need for automated methods. We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for image-based classification and Recurrent Neural Networks (RNNs) for automatic textual descriptions. Trained on 16,000 annotated images across four ripeness stages, the model achieved high classification accuracy (MobileNetV2: 95.50%) and superior captioning performance (ResNet101 + Bi-GRU: BLEU 0.9974, METEOR 0.9949, ROUGE 0.9164). While weighted summation fusion demonstrated superior performance, concatenation was ultimately chosen for its simplicity and real-world deployment feasibility. Statistical validation using one-way ANOVA (p<0.05) confirmed the significance of the findings. These results highlight the potential of the proposed multi-modal approach as a practical and interpretable framework for automated durian ripeness assessment.