Advancing sustainable food Packaging: Integrating machine learning, deep learning, and artificial intelligence

Background

The implementation of artificial intelligence (AI) in food packaging is transforming the food industry with groundbreaking technologies to improve food safety, quality, and sustainability. A growing assortment of AI technologies, including machine learning (ML) and deep learning (DL), are being integrated into packaging strategies.

Scope and approach

This paper provides an in-depth overview of the development in smart, active, and intelligent packaging systems, with a focus on AI-based technologies such as machine learning (ML) and deep learning (DL). Further, new smart technologies like hyperspectral imaging (HSI) and robotics are discussed to identify their applications in optimizing packaging processes. The various applications of AI in food packaging, including shelf-life prediction, quality assessment, and defect detection, are thoroughly examined. In addition, there are challenges, including data heterogeneity, model capabilities, and scalability, that highlight the need for a robust AI framework for successful deployment. Overall, the food packaging sector has the ability to leverage AI to increase productivity, improve food safety, and minimize food wastage.

Key findings and conclusions

The application of AI in food packaging offers significant benefits, including increased productivity, improved food safety, and reduced waste. Resolving data and model performance difficulties is necessary to fully leverage these technologies. The development of completely automated inspection solutions and real-time monitoring systems should be the top priorities of future research to enhance the sustainability and authenticity of food packaging significantly.