Implementing AI-Powered Early Warning Systems in Mozambique's Rural Areas to Prevent Crop Failure: A Methodological Approach

Early warning systems (EWS) have been increasingly used to mitigate crop failure risks in developing countries, especially in remote rural areas where traditional monitoring methods are insufficient. Mozambique is a case study of such regions with high vulnerability to climate-related disasters. The methodology involves collecting and preprocessing climate-related data from meteorological stations, integrating it with soil moisture, rainfall, and temperature sensors. A convolutional neural network (CNN) model is trained using historical crop yield data as labels for early warning prediction. A CNN model achieved an accuracy of 85% in predicting potential crop failure within the next three months, identifying areas at higher risk with a spatial distribution pattern across different climatic zones. The AI-powered EWS demonstrated promising results in reducing false positives and negatives through real-time monitoring and feedback loops to improve model performance over time. Future research should focus on integrating user feedback into the system for better decision-making, ensuring data privacy and security, and scaling up deployment across more rural areas of Mozambique. AI, Early Warning Systems, Climate Change, Crop Failure Prevention, Machine Learning Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.