Intelligent Food Waste Management in Supply Chains using Deep Dense Networks and Image Processing

Abstract

The utilisation of deep learning techniques or image processing to intelligent waste from food management across supply chains remains under-explored, despite the significant opportunity of Industry 4.0 technologies to optimise supply chain operations. Deep Dense Networks (DDNs) and image recognition models offer innovative solutions to identify and reduce food waste; however, their applicability in food supply chain for waste detection or prediction remains in its infancy. Companies are increasingly integrating AI-driven technologies to reduce food waste; however, the integration or practical application of these innovations have frequently been presented in a superficial manner, with inadequate guidance on how to achieve sustainable results. The field gets better by the establishment in a framework to the integration in deep learning and image processing technology onto food waste management all over the supply chain in this systematic review of the literature. The study presents a research agenda organised around four important themes: technology adoption, waste reduction models, AI-based optimisation, and waste management sustainability. This work offers beneficial knowledge for both academic researchers and industry practitioners into the successful use for deep learning and based on pictures approaches to reduce food waste in supply chains.