Title : From prediction to environmental resilience: Artificial intelligence, data scarcity, and adaptive agricultural systems in Sub-Saharan Africa
Abstract:
The growing frequency and intensity of environmental variability in Sub-Saharan Africa (SSA) present formidable challenges to agricultural productivity, ecosystem stability, and food security. Artificial intelligence (AI) technologies offer transformative opportunities to address these challenges through predictive modelling, precision farming, and advanced decision-support systems. This systematic review synthesizes empirical evidence on the scope, effectiveness, and limitations of AI applications in enhancing agricultural resilience under environmental stress across SSA. Guided by the PRISMA framework, studies published between 2010 and 2026 were systematically retrieved from Scopus, Web of Science, ScienceDirect, IEEE Xplore, and AGRIS databases. The review focuses on AI techniques including machine learning, deep learning, and neural networks, applied to climate forecasting, soil and crop management, and resource optimization. Four thematic clusters emerged: (1) AI-driven climate prediction and early warning systems; (2) adaptive crop and soil management; (3) data-enabled decision support; and (4) governance and ethical frameworks. Evidence indicates that AI significantly improves climate prediction accuracy, yield estimation, and environmental monitoring. However, adoption remains constrained by limited data availability, weak digital infrastructure, and inadequate governance mechanisms. The study emphasizes the need for localized, inclusive, and interoperable AI systems tailored to SSA’s agroecological diversity. It concludes by advocating for research that integrates AI with indigenous knowledge, participatory innovation, and robust policy frameworks to advance sustainable agricultural transformation and climate resilience in the region.
Keywords: Artificial Intelligence, Agriculture, Climate Variability, Environmental Change, Sub-Saharan Africa, Systematic Review
