Intelligent Agro-Based Retail Monitoring System: A Systematic Literature Review and System Design

Abstract

Agricultural retail forms the last operational link between producers and consumers, and its efficiency has a direct bearing on food security and on how much edible produce is lost before it ever reaches a shopping cart. For decades, this link has been managed through periodic, manual shelf inspection—an approach that is labor-intensive, inconsistent across staff, and structurally reactive, since spoilage is normally noticed only after a batch has already degraded. This review surveys eight recent software- and hardware-oriented approaches to agro-retail and agricultural monitoring, spanning automated freshness tracking, database-driven decay modeling, food-spoilage classification research, web-based supply-chain dashboards, dynamic markdown pricing, multi-tenant retail platforms, spatial floor-mapping systems, and cloud-based crop-cultivation frameworks. Read together, these works show a consistent shift away from static, spreadsheet-driven inventory logs and toward continuous, predictive, and increasingly visual monitoring. At the same time, nearly every reviewed approach depends on the completeness and accuracy of manual data entry at the point of receiving, and only one line of work attempts to translate predictive output into an intuitive, colour-coded physical layout of the store. Building on these findings, this paper presents the design of an Intelligent Agro-Based Retail Monitoring System: a three-tier, software-only platform that combines a Python analytics engine for automated data cleaning and shelf-life prediction, a conversational orchestration layer capable of interpreting natural-language and voice queries, and a React.js dashboard that renders inventory health onto an interactive, colour-coded storefront map. The resulting architecture is intended to move agro-retail operations from speculative, labor-bound management toward real-time, evidence-based decision-making.