Multimodal Sensing and Machine Learning for Nondestructive Quality and Safety Assessment of Agricultural Products

Abstract

The demand for non-destructive evaluation of agricultural products has stimulated the emergence of multimodal sensing technologies capable of integrating diverse quality- and safety-related information. Recent advances in multimodal sensing and machine learning (ML) have enabled intelligent platforms capable of integrating diverse physicochemical, structural, and biochemical information for comprehensive product characterization. This review summarizes the principles, data-processing strategies, and applications of multimodal sensing systems combining machine vision, visible-near-infrared spectroscopy, hyperspectral imaging, Raman and fluorescence techniques, electronic noses, and auxiliary information with ML algorithms. The complementary capabilities of different sensing modalities for evaluating external appearance, internal quality, chemical composition, freshness, spoilage, and safety hazards are discussed. Meanwhile, the roles of conventional ML and deep learning in feature extraction, pattern recognition, and multimodal data fusion are highlighted. Although advanced algorithms and increased data dimensions improve analytical performance, reliable deployment requires robust datasets, effective calibration transfer, model interpretability, uncertainty assessment, and independent validation. Future advances should prioritize self-supervised and few-shot learning, domain adaptation, physics-informed models, lightweight edge intelligence, and standardized benchmarking to improve model reliability, transferability, and scalability. These innovations will facilitate the deployment of intelligent sensing systems from laboratory environments to real-world agricultural quality and safety management.