The competition invites participants to develop machine learning (ML) and deep learning (DL) models that forecast and detect food safety risks across the supply chain using curated datasets from both academic and industry sources.

Leveraging the Cornell Food Safety ML Repository, newly contributed datasets from Agroknow’s Foodakai platform, and publicly available computer vision datasets generously permitted by Dr. Yi from Michigan State University, competitors will apply AI/ML tools to real-world data to address contamination risks in food production environment, food risk in global food trade, and rapid detection of bacteria.

This initiative aims to foster collaboration across domains, drive innovation, and demonstrate the potential of predictive analytics to protect public health.

The competition organized through the Cornell University Food Safety Lab and supported by the AI Institute for Next Generation Food Systems (AIFS), Agroknow, AI4Food at University of Guelph, and the IAFP Student PDG.

Winners may have the opportunity to attend the Annual Meeting of the International Association for Food Protection (IAFP).

Competition Objectives and Tasks

Main Objectives:

• Build predictive models for identifying or forecasting food safety hazards in various environments and supply chain stages.
• Generate actionable insights for food safety surveillance and risk mitigation.
• Define the benchmarks for the performance of ML/DL models in food safety

Tasks:
 Participants will:

• Select one or more datasets from the Cornell Food Safety ML Repository, Agroknow’s Foodakai risk intelligence platform, and computer vision-based pathogen detection datasets.
• Develop and document an end-to-end ML/DL pipeline that includes:
  • Data preprocessing and feature engineering
  • Model training and validation
  • Interpretation of model outputs
• Address one of the following challenge themes:
  • Predicting pathogen presence in food production environment
  • Forecasting food safety incidents based on Agroknow’s historical global food safety incidents (recalls, border rejections) and publicly available supply chain data (price data, trade data, laboratory test results, weather data, etc)
  • Image-based bacterial classification and detection

Please refer to the competition flyer for full details, and join us on Discord (highly recommended) for live announcements and support throughout the competition.