Artificial intelligence in poultry processing: applications, validation gaps, and pathways toward intelligent and autonomous processing systems

Artificial intelligence in poultry processing: applications, validation gaps, and pathways toward intelligent and autonomous processing systems. Artificial intelligence (AI) is emerging as a transformative tool for poultry processing by enabling rapid, nondestructive, and adaptive interpretation of images, spectra, sensor signals, and production records. This review critically examines AI applications across the poultry-processing chain, including live-bird receiving, slaughter, scalding, defeathering, evisceration, carcass inspection, chilling, antimicrobial control, cut-up, deboning, meat-quality assessment, breast-muscle abnormality detection, further processing, packaging, microbial monitoring, and predictive food-safety management. Particular attention is given to computer vision, machine learning, deep learning, near-infrared and hyperspectral imaging, electronic sensing, multimodal data fusion, robotics, and predictive modeling. Reported studies demonstrate strong potential for defect classification, carcass and portion localization, foreign-material detection, microbial-load estimation, freshness assessment, yield prediction, and process optimization. However, the literature establishes technical feasibility more convincingly than commercial reliability. Many models are developed from small, single-source datasets, use random data partitioning that may permit leakage, rely on uncertain reference labels, or report overall accuracy without class-specific performance, calibration, uncertainty, and external validation. Domain shifts caused by differences among flocks, plants, seasons, equipment, lighting, product orientation, and processing conditions remain major barriers to deployment. AI should therefore be implemented as part of an integrated sensor-model-decision-actuator system rather than as an isolated algorithm or replacement for validated process controls and human expertise. Future progress requires multi-plant datasets, prospective commercial-line te…