Artificial Intelligence-Enabled Computer Vision for Food Inspection: From Validated Measurements to Process Control

Artificial intelligence-enabled computer vision can increase the frequency and spatial coverage of food inspection, but high predictive accuracy does not establish reliable production decisions. This critical narrative review examines the evidence connecting image measurements with grading, rejection, and process control. Representative studies across produce, grains and nuts, meat and seafood, microbiology, and packaging are compared by sensing modality, reference endpoint, validation unit, and operational action. The synthesis distinguishes visible quality from specific hazards, prepared defects from natural variation, and image-level performance from verified product outcomes. An author-proposed evidence map organizes analytical feasibility, independent-sample testing, operational challenges, external transfer, and prospective impact as partly independent dimensions. It is unvalidated and is not a readiness score or regulatory standard. Task-dependent reporting considerations address uncertainty, prevalence, false rejection, and abstention. Safety-related uses require separate hazard-specific validation, sampling, and acceptance criteria. Process integration additionally requires acquisition-to-actuation testing, accepted-product verification, and controlled updates. The purposive evidence base cannot establish field-wide effectiveness or rank modalities. Research priorities include traceable datasets, mechanistic challenges, and prospective intervention studies. Progress toward closed-loop control should be judged by stable measurements and demonstrated effects on defined product and process outcomes within a stated operating domain.