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Artificial Intelligence is transforming food safety systems by detecting contamination, predicting risks, and enhancing traceability across the supply chain.
Image Source: Getty
Access to safe, nutritious, and affordable food is essential to achieving the 2030 Agenda for Sustainable Development—especially its objective to ‘end hunger, achieve food security. and improved nutrition.’ However, unsafe food has been linked to causing over 200 different types of diseases, with nearly 600 million people succumbing to illness after consuming unsafe food every year.
The theme of this year’s World Food Safety Day—‘Food Safety: Science in Action’—highlights the vital role of scientific knowledge in ensuring safe food for all. Against this backdrop, this commentary explores how Artificial Intelligence (AI) can become a powerful tool to strengthen food safety systems globally and address the implementation challenges associated with such apparatus. Conventional food safety systems are struggling to keep up with sophisticated, globalised food systems and the emerging risks they present. While surveillance gaps continue to widen, AI-enabled technology is driving new methodologies in detecting, tracing, and preventing contamination.
Comprehending the role of AI in enhancing food safety requires an examination of how food moves from farms to our tables. The food supply chain consists of interconnected stages—production, processing, distribution, consumption, and disposal—with each stage dependent on the smooth functioning of the others. Multiple stakeholders participate in this chain, including farmers, seed producers, merchants, transporters, wholesalers, retailers, financial institutions, and insurance providers.
As food moves systematically through the supply chain, it generates vast amounts of data at each stage. AI solutions can be applied to this data to improve prediction accuracy, maintain quality, and enhance overall supply chain efficiency, making it useful across the supply chain—from agriculture, food production, and retail to delivery.
As food moves systematically through the supply chain, it generates vast amounts of data at each stage. AI solutions can be applied to this data to improve prediction accuracy, maintain quality, and enhance overall supply chain efficiency.
There are primarily three ways in which AI seeks to enhance food safety: first, by automating routine tasks to boost efficiency and productivity; second, by analysing large data sets to improve decision-making and product quality; and third, by performing advanced functions beyond human capability, such as predictive analytics. With the growth of AI, food safety professionals will play an increasingly important role in shaping AI solutions that are deployed in the food industry. In this context, capacity building through upskilling and cross-disciplinary training becomes imperative. This ensures that regulators, industry personnel, and technologists can collaborate effectively to develop and deploy AI systems that are both safe and contextually relevant.
AI is currently enhancing food safety through a range of targeted applications. AI-powered sensors enable real-time detection of contaminants, allowing for swift intervention against microbes, chemical residues, and physical adulterants in food products. Integrated AI and blockchain solutions strengthen traceability, making product recalls faster and more accurate. Additionally, AI supports predictive maintenance by identifying potential equipment failures early, resulting in better hygiene and operational efficiency. These technologies also contribute to waste reduction by accurately predicting shelf life and optimising supply chain processes.
AI is also equipped to predict foodborne illness outbreaks and detect anomalies such as mislabelling or adulteration in the supply chain. It improves quality control through accurate defect detection and predicts shelf life and spoilage using classification algorithms. Additionally, AI leverages Natural Language Processing (NLP) to scan ingredient lists and labels for allergen risks, helping manufacturers comply with regulations and prevent recalls.
Use of AI for predictive analysis of food demand is another important use case. By applying machine learning algorithms on historical datasets—including consumer behaviour, past sale trends, seasonality, weather patterns, and other socio-economic factors—AI solutions can produce demand forecasts for various food items. Furthermore, these forecasts also help producers and retailers optimise logistics across the entire supply chain—adjusting production volumes, scheduling timely procurement, and streamlining distribution to ensure that perishable goods reach markets before their expiry. By aligning supply more closely with actual demand, AI can help reduce overproduction and underutilisation—the two major causes of spoilage. Additionally, AI can identify bottlenecks or inefficiencies in logistics and suggest real-time interventions, thereby preserving the freshness and safety of food products throughout their journey.
AI is also equipped to predict foodborne illness outbreaks and detect anomalies such as mislabelling or adulteration in the supply chain. It improves quality control through accurate defect detection and predicts shelf life and spoilage using classification algorithms.
In addition, AI-powered machines can be implemented to reduce dependence on manual labour in food handling processes. This helps minimise human error and potential contamination. By automating tasks traditionally performed by humans, such systems enhance hygiene and operational consistency. AI-based machines can also enhance real-time monitoring and provide proactive interventions, ensuring a safer and more compliant environment in retail and food services.
India has taken several steps to deploy AI and machine learning based innovative solutions to strengthen the food regulatory system, including the use of AI for obtaining traceability information. Raman 1.0 was launched by Oak Analytics—a startup mentored by the Food Safety and Standards Authority of India (FSSAI). It is a handheld spectrometer that leverages micro-optics, mobile, and AI to take spectroscopy out of the lab and into the field.
AI deployment in food safety is constrained by several technical challenges. A primary challenge is the lack of high-quality domain-specific datasets that cover the various food environments. Relevant data is often fragmented across heterogeneous formats and storage systems, limiting accessibility and usability. Lack of interoperability further complicates the integration of various datasets, affecting the efficient development and deployment of AI models. Furthermore, the insufficient participation of food safety professionals in the AI development cycle results in concerns regarding the relevance and effectiveness of the solutions. A lack of domain-specific input during model design often leads to solutions that are poorly adapted to real-world food environments.
Relevant data is often fragmented across heterogeneous formats and storage systems, limiting accessibility and usability. Lack of interoperability further complicates the integration of various datasets, affecting the efficient development and deployment of AI models.
Several legacy food safety systems are known to rely on outdated infrastructure, rendering upgrades both costly and resource-intensive. Modernisation requires substantial investment in hardware, software, and specialised expertise, often accompanied by significant operational downtime.
AI solutions deployed in food safety must also adhere to stringent and evolving regulatory frameworks. It is important to ensure that the solutions so developed can comply with and adapt to the regulatory updates without extensive redevelopment. Failure to address these factors not only risks legal penalties but can also undermine stakeholder trust and system reliability in critical safety applications.
Food safety is a critical component in the global effort to end hunger, and AI offers advanced analytical and automation capabilities across the food supply chain to enhance food safety. While it possesses the potential to improve several aspects of food safety, significant challenges hinder its deployment in food safety. It is important to ensure the availability of appropriate datasets and adequate infrastructure. Furthermore, prioritising open data standards to ensure interoperability and accessibility across stakeholders is key to developing successful AI-based food safety solutions. In addition, regulatory sandboxes can be implemented to allow controlled testing of such solutions. AI-based compliance audits can also be used to detect violation of food safety violations. It is also important to take a multi-disciplinary approach in developing such a solution, considering the expertise of experts on food safety. With coordinated efforts and continued innovation, AI can become a cornerstone technology in building safer, more efficient, and sustainable food systems worldwide.
Basu Chandola is an Associate Fellow with the Centre for Digital Societies at the Observer Research Foundation.
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Basu Chandola is an Associate Fellow. His areas of research include competition law, interface of intellectual property rights and competition law, and tech policy. Basu has ...
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