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The agriculture industry lacks novel techniques for analyzing risks facing its workers. Although injuries are common in this field, existing datasets and tools are insufficient for risk assessment and mitigation for two primary reasons: they provide neither immediate nor long-term risk mitigation advice, and they do not account for hazards which fluctuate daily. The purpose of Demeter is to collect safety data about hazards on farms and produce risk analysis and mitigation reports. This application uses a combination of formula-based risk calculations and state-of-the-art graph neural networks (GNNs) to perform risk analysis and reduction. The formula-based risk calculations had a mean absolute error (MAE) of 0.2110, and the GNN had an accuracy of 94.9%, a precision of 0.3521, and a recall of 0.8333. Demeter has the potential to reduce the number of injuries and fatalities among agriculture workers by alerting them to risks present in their daily workflow and suggesting safety precautions. [ABSTRACT FROM AUTHOR]