Authors: Gunjan and Rishita Vohra, Mr.Ritiesh Kumar Chandel, Dr. Akhilesh Das
Abstract: Food spoilage and the premature disposal of edible food are persistent problems for public health, the economy and the environment. Fixed best-before and expiry dates are set under controlled conditions and ignore the temperature fluctuations and handling that food experiences between farm and fridge. This paper reviews recent artificial intelligence (AI) approaches to freshness detection and shelf-life prediction, covering convolutional neural network (CNN) image classifiers and low-cost gas-sensor systems with machine-learning models, and compares their reported performance. Building on the gaps identified, we formulate the problem mathematically and propose a four-layer vision–sensor fusion framework that combines a transfer-learned CNN with a regression model to estimate remaining shelf life, together with a confidence-aware decision rule that sends uncertain items to manual inspection. We also describe an edge-deployment architecture, a planned experimental protocol and the main risks. Reported accuracies in the reviewed sensor-based studies range from 91.9% to 97.05%. The framework is conceptual; experimental validation on a multi-product dataset is identified as future work.
