Bitesize Biosecurity: A tool and framework for curating and summarising expert biosecurity advice for farmers using artificial intelligence.

Abstract

Background: Biosecurity practices mitigate disease risk on farms; however, adoption and consistent implementation remain variable across farmers. Improved communication of biosecurity information could increase uptake, reduce disease incidence and improve productivity, profitability and animal welfare.

Methods: This project developed a framework leveraging large language models (LLMs) to transform complex biosecurity guidance into accessible information for livestock farmers. The system condenses expert advice from publicly available UK government and veterinary advisory sources to generate more accessible summaries on a purpose-built platform. Initial implementation targets three UK livestock health challenges for cattle and sheep farmers with the purpose of demonstrating a proof of concept: sheep scab, liver fluke and Johne's disease.

Results: Bitesize Biosecurity provides a centralised repository where farmers can access evidence-based recommendations without navigating complex literature or multiple websites. The system maintains scientific integrity while presenting guidance in practical, actionable formats.

Limitations: The effectiveness of the framework depends on the quality and availability of source materials. In this study, implementation was limited to three diseases and the UK context, with system performance reliant on LLM accuracy in content transformation process and summary generation.

Conclusion: This tool addresses critical gaps in agricultural communication by streamlining knowledge transfer between research institutions and practitioners. The scalable architecture enables expansion to additional diseases, farm types and regions, with the potential to transform biosecurity information communication across the agricultural sector.