Mauritania’s agricultural sector, particularly rice production, faces significant challenges due to climate variability, market inefficiencies, and limited access to technological resources. In response, this study proposes a smart agriculture framework that integrates Artificial Intelligence (AI), Big Data, and simulated Internet of Things (IoT) technologies to enhance rice yield forecasting and farm climate monitoring. Building upon our prior work—including yield prediction via Random Forest and LSTM models, IoT-based digital twin climate monitoring, and national agricultural datasets—we present an improved architecture combining real-time data analytics and scalable decision support.
The framework utilizes historical datasets spanning 1960–2023, covering rice yields, agricultural value added, fertilizer use, population, employment, trade, and rice prices. Simulated IoT data, based on historical temperature and humidity records, serve as a proxy for field sensors in data-scarce environments. Three predictive models were benchmarked: Random Forest achieved the highest R² (0.87), followed by XGBoost and LSTM. Feature importance analysis highlighted temperature, rainfall, and fertilizer use as key yield predictors. A weak correlation (0.08) between retail and wholesale prices indicates limited market integration, which may affect production planning.
Beyond model accuracy, the study emphasizes the practical value of integrating AI-driven prediction with IoT-based monitoring to support precision agriculture in Sub-Saharan Africa. This approach enables farmers to make data-informed decisions on crop scheduling and resource allocation, potentially increasing yields by 10–15% under climate uncertainty. It also lays the groundwork for a future SaaS platform tailored to smallholder needs, incorporating real-time alerts, decision dashboards, and mobile access.
The proposed solution aligns with the goals of climate-smart agriculture and sustainable development. Future work will focus on deploying real sensors, validating predictions in the field, and expanding to other staple crops critical to regional food security.
