AI-Driven Climate-Smart Agriculture for Resilient Food Security

Abstract:

Climate variability is tightening the linkage between production risk, natural resource degradation, and food-system instability. Climate-smart agriculture (CSA) offers a practical framework for balancing productivity, adaptation, and mitigation, but its effectiveness increasingly depends on the quality and timeliness of farm decisions. This review synthesizes recent literature published during 2023-2026, together with official FAO guidance, to examine how artificial intelligence (AI) strengthens CSA through predictive analytics, computer vision, sensor integration, remote sensing, and decision support systems. Across the reviewed corpus, the strongest application clusters were yield forecasting, resource-use optimization, irrigation scheduling, crop surveillance, and risk-aware advisory services. The literature consistently shows that AI is most valuable when coupled with agronomic context, calibrated local data, and institutions capable of translating predictions into farmer-facing action. The review also identifies major constraints - data interoperability, weak digital infrastructure, affordability, skill gaps, and governance concerns - that limit equitable adoption. A practical framework is proposed in which AI is treated not as a stand-alone technology but as a decision layer embedded within climate-smart production systems, extension services, and food-value chains. The article concludes that resilient food security will depend less on algorithm novelty and more on responsible integration of AI with water stewardship, soil health management, localized advisories, and inclusive public policy.