AI-Driven Strategies for Rebuilding Food Security in Post-Conflict Northern Nigeria: Opportunities, Challenges, and Policy Implications - Dadi Jonathan Abba; Mafeng Jamima Dudari; Raliyah Umar Alkaleri; Jimmy Nirat Jakawa; Habibu Aminu Sani (Mikailalsys Journal of Advanced Engineering International, 2026)
Years of conflict in Northern Nigeria have displaced communities, disrupted agricultural production, and weakened market systems, creating urgent challenges for food security recovery. Traditional rehabilitation approaches alone are insufficient to address these multidimensional problems. This article aims to examine the potential of artificial intelligence (AI) as a transformative tool for rebuilding food security in post-conflict settings in Northern Nigeria. The study analyzes how AI-enabled technologies, including predictive modeling, climate monitoring, automated crop assessment, and data-driven supply-chain management, can support agricultural productivity, timely decision-making, and food system resilience. The findings indicate that AI can contribute to post-conflict recovery by strengthening early-warning systems, improving agricultural planning, enhancing supply-chain coordination, and supporting more sustainable food security interventions. However, the effective adoption of AI remains constrained by inadequate infrastructure, limited technological skills, and governance challenges. The study concludes that responsible, context-sensitive, and locally adapted AI strategies can accelerate food system recovery and contribute to sustainable food security in Northern Nigeria. This article contributes to the discourse on digital agriculture and post-conflict reconstruction by highlighting the strategic role of AI in strengthening resilience, improving recovery planning, and supporting evidence-informed food security interventions in fragile contexts.