Authors : Bhushan Chaudhari; Aryan Patil; Pradyumna Sawkar; Shrutika Ghube; Shobha Raskar

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/4v9e4spf

Scribd : https://tinyurl.com/3r27cr9z

DOI : https://doi.org/10.38124/ijisrt/26jun1633

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This conceptual paper introduces AI Enabled Smart Packaging, an integrated, data-driven framework designed to intelligently recommend optimal packaging materials and generate consumer-attractive design templates for perishable food products. The system leverages artificial intelligence and ma- chine learning to automate the decision-making process in food packaging, which traditionally relies on manual expertise and generalized standards. The proposed framework incorporates a Convolutional Neural Network (CNN) for automatic food product identification from user-uploaded images, eliminating the need for manual product entry. Once identified, intrinsic compositional attributes such as moisture content, water activity, pH level, and fat content are retrieved through a knowledge-based lookup layer derived from standardized food composition data. Using these product-specific parameters along with environmental conditions such as temperature and storage type, a Random Forest–based classification and regression approach predicts the most suitable packaging material while simultaneously estimating the expected shelf life duration. In parallel, a dedicated API-driven design recommendation module translates these material predictions into visually ap- pealing and functionally compatible package layouts, offering scalable design customization for manufacturers. By bridging computer vision, predictive analytics, and creative automation, AI Enabled Smart Packaging aims to reduce food spoilage, minimize material waste, and improve sustainability through intelligent material selection. Furthermore, it supports enhanced marketability by integrating aesthetic design principles within the packaging process. The proposed framework details the end-to-end methodology, including image-based food recognition, dataset creation logic, preprocessing pipeline, feature engineering, CNN-based classification, Random Forest-based material selection and shelf-life estimation, and the design recommendation API. Ethical and sustainability considerations are also emphasized to ensure environmental responsibility and transparency in decision-making. The contribution of this work lies in presenting a reproducible conceptual blueprint that demonstrates how artificial intelligence can enhance packaging innovation, paving the way for sustain- able, data-driven, and consumer-centric food packaging systems of the future.

Keywords : Smart Packaging, Random Forest, Convolutional Neural Network, Design Recommendation API, Food Preservation, Intelligent Packaging, Sustainability.

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