Designing a Farm Emergency Plan Utilizing Artificial Intelligence.

Abstract

Highlights: Three AI systems were used and analyzed on their ability to create farm emergency plans. AI were presented with three farm emergency scenarios to access their completeness and accuracy of response. AI was not able to present complete farm emergency plans, as human intervention was needed for a complete FEP. AI responded well for individual emergency scenarios presented, containing key safety points.

Abstract: The ability of three artificial intelligence systems (ChatGPT, Microsoft Copilot, and Google Gemini) to generate functional Farm Emergency Plans (FEP) for a typical Midwestern row crop grain farm was evaluated. Four prompts, each of increasing levels of specificity, were used with the three AI systems, yielding twelve distinct FEPs. A rubric was developed to evaluate each of the twelve AI products against the findings of a review of relevant current literature including academic, government, not-for-profit, and insurance sources to identify essential and consistent components of a FEP. Both ChatGPT and Microsoft Copilot were found to provide valuable starting points for developing FEPs when detailed prompts were provided, while Google Gemini results were less useful. However, none of the systems were capable of independently generating FEPs at the time of this study. Plans that were deemed as unreliable or incomplete enough for application were primarily due to the diverse nature of agricultural operations, limited resources on agricultural emergency preparedness, and the lack of maturity of current AI systems. Findings showed the essential need of using AI systems in collaboration with human guidance and input from other evidence-based sources to create effective FEPs. Similar results were confirmed in which the AI systems were prompted for emergency responses to three specific farm-related emergencies as part of the FEP: (1) flowing grain entrapment, (2) hazardous agricultural chemical spills, and (3) anhydrous ammonia exposure. The need for additional input was found to be essential. Outcomes were limited in scope to the particular type of farm selected for testing and the ability of the AI systems when they were queried on 30 September 2024; 12 February 2025; and 7 March 2025. Since AI systems rapidly continue to mature as they are "exercised," further inquiries will, therefore, yield different outcomes, because AI has become more sophisticated and developed every day. It should also be noted that for "best practices," the inquirer should provide AI with any resources that they have found and provide multiple inquiries to gain the best and most accurate results. This study demonstrated the potential that AI offers to agricultural producers, specifically in emergency preparedness and response, while emphasizing prompt development and user competency to verify AI outputs.