Artificial intelligence (AI) offers a breakthrough in food safety by enabling real-time monitoring and contaminant detection across the supply chain.
Study: AI-Powered Innovations in Food Safety from Farm to Fork. Image Credit: Attasit saentep/Shutterstock.com
AI is reshaping food safety by enabling real-time monitoring, rapid contaminant detection, and end-to-end traceability according to a new study published in the journal Foods.
The study explores how AI is being applied across every stage of the food supply chain. From detecting pests and spoilage to ensuring compliance in processing and enabling blockchain-backed traceability, AI systems are driving smarter, faster, and more reliable food safety solutions. The researchers also highlight AI’s emerging role in personalized nutrition and its ability to enhance testing accuracy while reducing time and costs.
A Persistent Global Challenge
Food safety remains a critical issue worldwide. Contaminated food is responsible for around 600 million cases of illness and 420,000 deaths annually. Conventional approaches—like lab-based testing and manual inspections—are often slow, labor-intensive, and limited in scope. While mechanization has boosted food production, it’s also increased exposure to risks like microbial contamination and pesticide overuse, underscoring the need for more scalable and responsive safety systems.
This is where AI steps in. Recent advances are helping to fill long-standing gaps by combining tools such as spectral analysis, machine vision, and blockchain into intelligent, integrated platforms. Unlike earlier systems that lacked real-time oversight, AI enables continuous monitoring, predictive analytics, and automated decision-making throughout the food chain.
The study outlines how AI is creating a shift from reactive, lab-heavy practices toward proactive, data-driven governance, transforming food safety into a dynamic, real-time process.
Mapping the AI Landscape in Food Safety
To understand the full scope of AI’s impact, the researchers reviewed 1,528 academic papers from major databases including Web of Science, X MOL, and IEEE Xplore, published through April 2025. After applying seven practical criteria—covering areas like food source management, contamination detection, storage monitoring, traceability, and personalized nutrition—they selected 276 high-quality and 129 medium-quality studies, filtering them via a PRISMA flowchart.
Their analysis found that AI consistently outperforms traditional methods in areas like sampling speed, traceability, and real-time data processing. Key technologies include computer vision, IoT, natural language processing, deep learning, and blockchain. These systems enable immediate identification of defects, prediction of spoilage, compositional analysis, and secure product tracking—bringing together regulators, producers, and consumers around shared, actionable data.
Each AI method brings different strengths. Supervised learning works best when labeled data is available, while unsupervised learning helps uncover unknown patterns. Semi-supervised models balance accuracy and cost, and deep learning is suited for handling complex, high-dimensional data—though it often requires greater computational power and can be harder to interpret.
Real-world examples from the study include walnut impurity detection with over 96 % accuracy in milliseconds and smartphone-based hydrogel tests assessing meat freshness with 96.2 % accuracy.
From Farm Precision to Personalized Nutrition
Traditional food safety systems often fall short in detecting pathogens or contaminants early enough to prevent harm. AI bridges this gap by integrating technologies like hyperspectral imaging, blockchain, and sensor networks for greater precision and responsiveness.
On farms, AI systems classify plant diseases, such as cassava leaf blight, with 93 % accuracy and monitor pesticide residues with 98.4 % accuracy using SERS combined with transformer models. During food processing, hyperspectral imaging identifies adulterants with 95.71 % accuracy, while blockchain platforms ensure transparent and immutable traceability.
Portable AI-powered tools detect pathogens like Staphylococcus aureus within 10 hours (96 % accuracy), and smartphone-based sensors can identify spoilage with 99.6 % accuracy. AI also improves storage conditions, cutting grain spoilage prediction errors to 15–20 %.
Beyond safety, AI contributes to personalized nutrition by predicting how diets affect individual health outcomes—achieving 84 % accuracy in linking foods to disease pathways—and helps reduce food waste by 14–52 % through real-time demand forecasting and inventory management.
Challenges and What’s Ahead
Despite its promise, AI in food safety still faces significant challenges. Fragmented data systems, limited algorithm transparency, high implementation costs, and immature integration across platforms all slow progress.
Future advances are expected in areas like edge computing, which brings processing closer to data sources; lightweight AI models that reduce hardware requirements; and standard governance frameworks that improve consistency and trust. Trends also point toward miniaturized detection devices, explainable AI to enhance decision-making clarity, and the development of global food safety databases.
With continued investment in research, ethics, and infrastructure, AI has the potential to move food safety from passive oversight to intelligent prevention, building a system that’s faster, more resilient, and better aligned with public health needs.
Conclusion
AI is redefining how we approach food safety, bringing speed, accuracy, and scalability to a complex, global challenge. By integrating machine vision, spectral analysis, sensor data, and blockchain, AI is enabling smarter detection, secure traceability, and more tailored nutrition strategies.
While hurdles remain, developments in explainable AI, edge computing, and standardized data systems are opening the door to a more responsive, proactive approach to keeping our food safe.
Journal Reference
Yin, B., Tan, G., Muhammad, R., Liu, J., & Bi, J. (2025). AI-Powered Innovations in Food Safety from Farm to Fork. Foods, 14(11), 1973. DOI:10.3390/foods14111973. https://www.mdpi.com/2304-8158/14/11/197
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.