Title: A Predective Agricultural Market Price Information Systems
Authors: Christian D.J, Leila S.K, Ashrey G.M, Damari Oguwi, Ismail Masoud,Gidion Mugabo
Volume: 10
Issue: 2
Pages: 89-95
Publication Date: 2026/02/28
Abstract:
In emerging economies, agricultural market instability and information asymmetry remain significant barriers to the economic empowerment of smallholder farmers. While traditional Market Information Systems (MIS) have provided historical price data, they often lack the proactive intelligence required for strategic decision-making. This paper explores the development and integration of Predictive Agricultural Market Price Information Systems that leverage Machine Learning (ML) and Progressive Web Application (PWA) technologies. By analyzing various predictive architectures including Long Short-Term Memory (LSTM) networks and Time-Series Analysis the study identifies how data-driven forecasting can mitigate the risks of price volatility. Furthermore, the research addresses the "connectivity gap" by proposing a framework that utilizes offline-first PWA capabilities, ensuring that predictive insights remain accessible in resource-constrained rural environments. The findings suggest that transitioning from reactive data dissemination to an integrated, AI-driven predictive ecosystem can significantly reduce post-harvest losses and improve market transparency.