AGRIHUB

AI-Based Food Ecosystem Transformation to Overcome Inefficiency and Achieve National Food Self-Sufficiency

Abstract

National food security continues to face significant challenges, including high levels of food loss and food waste, inefficient food distribution systems, and limited data integration across the food supply chain. These issues may undermine food availability and hinder the development of a sustainable food system. This study aims to analyze AGRIHUB AI as a community-driven smart food ecosystem that integrates artificial intelligence, predictive data analytics, and community participation to strengthen national food security. The research employs a qualitative descriptive approach through literature review and conceptual analysis of technology-based food system development. The findings indicate that AGRIHUB AI is structured around three integrated components: Production Intelligence, Simulation System, and Food Marketplace & Distribution. The platform is supported by an AI-Based Predictive Food Governance approach that enhances data-driven decision-making and a Pentahelix collaboration model involving government, academia, business actors, society, and media. Furthermore, the SWOT analysis reveals substantial opportunities for implementation through digital literacy enhancement, data governance improvement, and phased deployment using pilot projects. Conceptually, AGRIHUB AI has the potential to reduce food loss and food waste, improve distribution efficiency, strengthen urban farming initiatives, and support the four pillars of food security defined by the Food and Agriculture Organization (FAO): availability, access, utilization, and stability. Therefore, AGRIHUB AI may serve as a strategic digital innovation model for developing a more inclusive, adaptive, and sustainable national food system.