The JRC has developed an explainable AI (xAI) model to identify and evaluate multiple climate hazards impacting crops. This tool is designed to enhance risk management and bolster agricultural resilience against extreme weather. European farmers face challenges from erratic weather, with cold snaps, heatwaves, and extended droughts becoming more common.
These climatic issues can impact crop yields and food security. The xAI model of JRC integrates artificial intelligence with agro-climatic knowledge, providing a transparent method for forecasting and explaining agricultural threats. Unlike traditional AI systems, this model clarifies its predictions, pinpointing areas of concern (AOCs) where specific hazards could affect agricultural productivity. It processes vast agro-meteorological datasets curated by experts, elucidating the climatic factors behind its alerts to facilitate informed decision-making.
A notable feature of the xAI model is its probabilistic risk assessment. Rather than binary alerts, it offers likelihood estimations of climate hazards and presents uncertainty measures for each prediction. This enables users to gauge both threat severity and the confidence level of warnings. For instance, a high probability of drought in a particular area during a critical crop growth period could prompt early interventions or resource adjustments.
The JRC intends to advance the model by incorporating more data sources, exploring advanced AI architectures, and refining communication strategies. These enhancements aim to make the model's outputs more accessible and useful, aiding in the protection of European agriculture from future climate challenges.
Source: PataFest
