A smart food security monitoring and predicting algorithm for rice productivity in Southeast Asia - Chuan, Zun Liang; Tham, Ren Sheng; Abraham Lim, Bing Sern; David Lau, King Luen (AID Conference, 2025)
The rising food insecurity in Southeast Asia demands innovative and data-driven solutions. This study aims to develop a statistical forecasting approach by utilizing a single-stacked predictive framework to model rice productivity, even when data availability is limited, an essential advantage in this region. Specifically, the methodology integrates Simple Linear Regression–Multivariable Linear Regression (SLR-MLR) into a unified predictive algorithm. By incorporating all four dimensions of food security: availability, accessibility, stability, and utilization, the proposed algorithms outperform conventional Artificial Intelligence (AI) predictive algorithms, offering superior accuracy and actionable insights. Empirical analysis reveals that geographical conditions, economic policies, and agricultural strategies shape rice productivity. These findings support targeted, data-driven interventions aligned with Sustainable Development Goals (SDG1: No Poverty, SDG2: Zero Hunger, SDG3: Good Health and Well-being, and SDG8: Decent Work and Economic Growth). This scalable framework strengthens food security and community resilience by enhancing farmer incomes, improving nutrition, and promoting sustainable farming. Its adoption by policymakers, Non-Governmental Organizations (NGOs), and agribusiness leaders could redefine global food security strategies, positioning it as a strategic asset for sustainable development.