Food Degradation Analysis Using Multimodal Fuzzy Clustering

Food safety is essential for those who are vulnerable to foodborne illnesses. This study explores food degradation analysis using computer vision techniques combined with unsupervised machine learning. We extract visual features related to shape, texture, and color and apply fuzzy clustering to identify meaningful degradation states, capturing the gradual nature of food decay without rigid class boundaries. Additionally, we extract separate features using a Vision-Language Model (VLM) and integrate them into the clustering analysis. This multimodal approach enables both low-level visual feature analysis and high-level semantic interpretation of food degradation. Our study yields meaningful insights and lays the foundation for future research in food monitoring and safety.