Addressing the Critical Research Gaps in Image Processing for the Grocery Industry

Abstract

This article provides an in-depth examination of the existing shortcomings and unresolved challenges in the field of image processing for estimating the surface area and volume of grocery items. While significant advances have been made through the integration of 2D and 3D imaging, machine learning, and computer vision, critical research gaps continue to hinder practical application in dynamic retail environments. Current 2D methods, though cost-effective, often fall short in handling irregular shapes, complex textures, and real-time processing requirements. Volume estimation is further complicated by 3D modelling difficulties, variations between packaged and unpackaged goods, and a lack of models that generalize across diverse product types. Additionally, computational constraints, such as the trade-off between algorithmic accuracy and processing speed, limit the scalability of these technologies. Environmental factors like lighting and positioning also impact algorithmic robustness. To overcome these hurdles, this research advocates for interdisciplinary collaboration—combining robotics, AI, and smart packaging—to develop more adaptable, standardized, and scalable image processing solutions for the grocery industry