Development of an AI-Driven Predictive Quality and Shelf-Life Management System for Poultry Products in Smart Food Manufacturing

Poultry products are highly perishable, and traditional quality indicators, which depend on manual periodic inspections and predetermined expiration dates, do not necessarily meet the needs of today's high-volume, high-speed supply chains. The current study conceptualizes and explains the background and logic of an AI-based predictive quality and shelf life management system specifically for smart poultry manufacturing. The system builds on the groundbreaking work of machine learning, Internet of Things (IoT) sensing, and digital twin technology, combining real-time, non-destructive data collection and ensemble predictive models to forecast spoilage trajectories and optimize decision-making from farm to processing, distribution, and retail. Its elements include hyperspectral and gas sensor arrays for continuous quality monitoring, artificial neural networks, Random Forest, and LSTM architectures for robust shelf-life prediction, and a digital twin layer for generating scenarios and intervening prescriptively. The system also features dynamic labeling, blockchain-based traceability, and explainable AI capabilities, promoting transparency, regulatory adherence, and stakeholder confidence. Data heterogeneity, model generalizability, computational constraints, and barriers to adoption are all explored and quantified, as are benefits of 12–18% spoilage reduction, efficiencies in logistics, and 3–5% carbon footprint reduction. Future avenues for research focus on scalability, personalized Q thresholds, green AI principles, and hybrid physics-informed models in low-resource scenarios. This research represents a new paradigm in poultry quality management: predictive, adaptive, and sustainable by design, by connecting the principles of precision farming with the application of industrial artificial intelligence.