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This paper introduces a methodological framework for auditing the robustness and fidelity of post-hoc explainable AI tools like SHAP and LIME, combining these metrics into a Trust Score. It applies the framework to a food security dataset in Madagascar, highlighting the necessity of auditing XAI outputs for trustworthy decision-making in sensitive domains.
This paper presents the first machine learning study for crop yield forecasting in Sierra Leone, finding that combining freely available satellite climate data (CHIRPS, NASA POWER) with national crop statistics reduces forecast error by a third compared to persistence, though crop statistics alone are insufficient.
China is scaling agricultural robots for 24/7 autonomous harvest, using vision models and robotic arms to improve efficiency and reduce bruising, enhancing food security.