Transparent Multi-modelling for Climatic Geo-intelligence in Predicting Food Security

Millions of people in Uganda suffer from food insecurity as a result of the country’s scarce resources, extreme poverty, and high rate of childhood malnutrition. Women, who play a critical role in home management, are particularly vulnerable to food insecurity brought on by climate change. The vision is to foster a thriving agricultural sector in Uganda grounded in sustainability, with a mission to empower farmers, including women, through innovative tools and knowledge to adapt and prosper in a changing climate. This study employs cutting-edge technologies such as geospatial data analysis, machine learning, and environmental science to analyze weather patterns, soil conditions, and crop data. By providing personalized insights and recommendations to farmers, the research enables informed decision-making, optimizes resource management, and mitigates climate-related risks in agriculture. The research utilizes a multimodal architecture incorporating 13 traditional base machine learning models including Linear Regression, Lasso, Ridge, Elastic Net, Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Gaussian Process Regression, Gradient Boosting Regressor, XGBoost, LightGBM, and MLPRegressor. These base models are integrated into an ensemble approach using Stacking and Voting Regressors to harness collective intelligence and enhance predictive accuracy in optimizing agricultural productivity and resilience. The findings reveal that among the base models, LightGBM demonstrates superior performance in terms of mean squared error (MSE) of and R-squared (R2) metrics with MSE of 0.024 and R2 of 0.274, underscoring its effectiveness in predicting food security levels while the MLPRegressor demonstrates worst performance with MSE of 670.742 and R2 of -20636.457. The ensemble stacking model from the ensemble emerged as the best overall performer with MSE of 0.022 and R2 of 0.312, surpassing individual models and highlighting the significance of ensemble and multimodal learning approaches. These insights contribute to advancing precision agriculture and fostering resilience in farming communities, with implications for sustainable development and improved food security in Uganda. Furthermore, this study incorporates eXplainable Artificial Intelligence (XAI) techniques to interpret and visualize model predictions, providing farmers with transparent insights into the factors and key features influencing agricultural outcomes. XAI methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are employed to enhance the transparency and trustworthiness of machine learning models, empowering farmers with actionable insights for sustainable farming practices.