Multimodal Data Fusion Techniques for Intelligent Quality Assessment  

ABSTRACT:
This chapter presents multimodal data fusion as a fundamental technique for intelligent quality assessment. It explains how heterogeneous information from RGB and thermal images, hyperspectral data, sensors, environmental parameters, text, and product metadata can be integrated to obtain a more comprehensive quality representation. The chapter discusses early, intermediate, late, feature-level, decision-level, and attention-based fusion approaches, along with deep-learning-based multimodal architectures. Applications in agriculture, food processing, manufacturing, healthcare, and pharmaceutical quality assessment are examined. The chapter also addresses challenges including data heterogeneity, synchronization, missing modalities, computational complexity, dataset requirements, and interpretability, and discusses future directions involving adaptive fusion, foundation models, federated learning, and Edge AI.