Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI DOI
Petros Chavula, Fredrick Kayusi

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 318 - 318

Published: May 8, 2025

Sub-Saharan Africa (SSA) faces persistent food insecurity due to low agricultural productivity, limited access modern technologies, and growing climate variability. This study explores the transformative potential of Artificial Intelligence (AI) enhance systems across SSA. The objective is assess how AI applications—such as machine learning, remote sensing, big data analytics—can address systemic inefficiencies in cereal crop production, with a focus on barley, millet, sorghum. Using systematic review approach aligned PRISMA guidelines, literature from 2015–2025 was analyzed multiple databases identify empirical studies models related SSA agriculture. Results reveal that can significantly improve monitoring, yield forecasting, resource optimization. However, adoption barriers such inadequate infrastructure, financial constraints, digital divide persist. concludes while holds significant promise, its success depends inclusive policies, capacity building, localized governance. It recommends interdisciplinary research, investment rural participatory innovation frameworks empower smallholder farmers ensure equitable deployment. provides roadmap for integrating into resilience, security.

Language: Английский

AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges DOI Creative Commons
Fredrick Kayusi, Petros Chavula,

Gilbert Lungu

et al.

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 332 - 332

Published: March 25, 2025

Climate models are fundamental for predicting future climate conditions and guiding mitigation adaptation strategies. This study aims to enhance the accuracy reliability of modeling by integrating artificial intelligence (AI) techniques validation uncertainty mapping. AI-driven approaches, including machine learning-based parameterization, ensemble simulations, probabilistic modeling, offer improvements in model precision, quality assurance, quantification. A systematic review methodology was applied, selecting peer-reviewed studies from 2000 2023 that focused on validation, estimation. Data sources included observational records, satellite measurements, global reanalysis datasets. The analyzed key methodologies used improving accuracy, statistical downscaling deep prediction frameworks. Findings indicate AI-enhanced significantly improve projections refining enhancing bias correction, optimizing Machine learning applications facilitate more accurate predictions meteorological phenomena, temperature precipitation variability. However, challenges remain addressing biases, inter-model inconsistencies, computational limitations. concludes advancements provide critical reliability, yet ongoing refinements necessary address persistent uncertainties. Enhancing datasets, techniques, strengthening frameworks will be essential reducing uncertainty. Effective communication outputs, mapping, is crucial supporting informed policy decisions. a rapidly evolving field, continuous innovation predictive resilience

Language: Английский

Citations

0
Challenges in Sub-Saharan Africa’s Food Systems and the Potential Role of AI DOI
Petros Chavula, Fredrick Kayusi

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 318 - 318

Published: May 8, 2025

Sub-Saharan Africa (SSA) faces persistent food insecurity due to low agricultural productivity, limited access modern technologies, and growing climate variability. This study explores the transformative potential of Artificial Intelligence (AI) enhance systems across SSA. The objective is assess how AI applications—such as machine learning, remote sensing, big data analytics—can address systemic inefficiencies in cereal crop production, with a focus on barley, millet, sorghum. Using systematic review approach aligned PRISMA guidelines, literature from 2015–2025 was analyzed multiple databases identify empirical studies models related SSA agriculture. Results reveal that can significantly improve monitoring, yield forecasting, resource optimization. However, adoption barriers such inadequate infrastructure, financial constraints, digital divide persist. concludes while holds significant promise, its success depends inclusive policies, capacity building, localized governance. It recommends interdisciplinary research, investment rural participatory innovation frameworks empower smallholder farmers ensure equitable deployment. provides roadmap for integrating into resilience, security.

Language: Английский

Citations

0