Nonuniformity in Multi‑Physical Field Drying of Fruits and Vegetables: Mechanisms, Cascade Amplification, and Artificial Intelligence–Driven Control

Nonuniformity is the most critical yet unresolved bottleneck limiting the industrial adoption of multi‐physical field drying (microwave, radio frequency, infrared, and ultrasound) for fruits and vegetables. While individual physical field technologies and AI applications have been reviewed separately, a conceptual framework that explains how nonuniformity originates, amplifies, and can be intelligently controlled is currently lacking. This review provides a problem‐driven analysis of nonuniformity in physical field drying, proposing a three‐stage cascade framework (Initiation → Amplification → Termination) to elucidate the multi‐scale coupling between microscopic energy defects and macroscopic quality failure. We further critically evaluate the transition from black‐box artificial intelligence (AI) modeling to advanced closed‐loop intelligent control strategies involving physics‐informed neural networks (PINNs) and digital twins, structured along a perception–prediction–control paradigm. The cascade framework reveals that initial minor heterogeneities are self‐reinforced through positive feedback loops, necessitating early‐warning prediction. AI‐driven adaptive control, coupled with synergistic cross‐field designs, may provide opportunities to interrupt this amplification process at early stages. We provide a strategic roadmap emphasizing multimodal sensing and physics‐based hybrid modeling to achieve precision, uniformity, and sustainability in next‐generation industrial drying systems. This review offers a distinctive problem‑oriented perspective that complements prior technology‑centric surveys by placing nonuniformity as the central narrative and offering a conceptual framework for its prediction and control.