Q: What is the University of Hawaii project funded by the NSF? A: The University of Hawaii at Manoa received $2 million from the National Science Foundation as part of a four-year, $4 million effort led with the University of Nebraska–Lincoln to develop tools that protect food production systems. Researchers will combine sensors, environmental sampling, genetic analysis, and chemical testing to build AI-based food safety monitoring that detects environmental threats early. Q: How will AI-based food safety monitoring detect risks before people get sick? A: AI-based food safety monitoring pairs field sensors, environmental sampling and lab analyses with machine learning models that learn patterns predicting trouble and send early alerts. Dashboards provide risk scores, trend lines and guided actions so farmers and inspectors can retest, isolate, or treat sites before outbreaks occur. Q: Which farming sectors will the project test first? A: Researchers will initially pilot the technology in aquaculture and beef cattle operations to address water-linked risks in fish farms and feed, dust, or runoff issues in cattle production. These sectors let teams test continuous water checks, environmental DNA sampling and weather-linked alerts in real-world settings. Q: What types of samples and lab methods are used to identify hazards? A: Scientists collect samples from water, soil, feed and surfaces and measure signals like temperature, pH and runoff alongside weather and satellite data. Labs use genetic tools such as qPCR and metagenomics plus chemical testing and mass spectrometry to detect pathogens, parasites like Cyclospora, and harmful residues. Q: What benefits can early detection with AI-based food safety monitoring provide? A: Early detection can reduce recalls and food waste, lower treatment costs and hospital visits, and strengthen trust in local and export markets. By flagging problems sooner, the system helps farmers isolate sources, adjust water or feed, and recover faster after storms or heat waves. Q: Who is leading the project and how is the funding allocated? A: The University of Hawaii at Manoa leads the four-year project in partnership with the University of Nebraska–Lincoln, and the full NSF award totals $4 million with UH receiving $2 million. The funds support sensors, lab work, data systems, pilot testing and research opportunities for students and junior faculty while building partnerships with industry, regulators and communities. Q: What challenges and safeguards are needed for these AI systems? A: The project must manage data quality, avoid excessive false alarms, retrain models to prevent drift, ensure affordability for small and mid-size farms, and protect farm privacy. Good practice pairs machine alerts with human verification, clear playbooks and quality controls to keep responses consistent and quick. Q: What outcomes and next steps should stakeholders expect from this four-year project? A: Over the next four years stakeholders should expect pilot results from aquaculture and cattle sites, user-friendly dashboards for phones, faster lab assays and training programs for students and farmers. If pilots succeed, the approach could expand to produce, dairy and poultry through broader trials and shared protocols.