Integrating artificial intelligence in shelf-life prediction and packaging design for sustainable food systems

Rising global populations and changing environmental conditions continually challenge the stability of food supply chains, making the reduction of food waste while maintaining product quality a critical priority. Traditional approaches to shelf-life prediction and supply chain management are often static, labour-intensive, and resource-heavy. This paper explores the integration of artificial intelligence (AI) with advanced packaging technologies to create sustainable, data-driven food networks. Machine learning (ML) models, including artificial neural networks (ANN), and deep learning (DL) frameworks such as convolutional neural networks (CNN), analyse complex datasets to provide accurate, real-time shelf-life predictions. Complementing these computational tools, innovative packaging solutions are examined. Active packaging, incorporating oxygen scavengers and antimicrobial agents, enhances food preservation, while intelligent packaging systems equipped with sensors and data carriers communicate real-time freshness information to stakeholders. The paper also highlights the adoption of biobased and biodegradable materials as eco-friendly alternatives to conventional plastics, reducing environmental impact. By combining AI-driven analytics with adaptive, environmentally conscious packaging, continuous monitoring of food quality becomes feasible, significantly minimizing spoilage. Despite challenges such as high implementation costs and regulatory hurdles, the synergy of predictive modeling and smart packaging establishes a robust framework for resilient, efficient, and sustainable food supply chains.