Tiny Machine Learning for Improved Food Traceability and Security

Abstract

Food traceability and security are critical aspects of the modern food supply chain. Ensuring the safety and authenticity of food products is essential for both consumer health and the integrity of the industry. This paper explores the application of Tiny Machine Learning (TinyML) as a transfor

mative technology to enhance food traceability and security. TinyML leverages machine learning algorithms on resource-constrained devices, making it a promising solution for the challenges posed by the food supply chain. This study reviews the current landscape of food traceability and security, identifying the limitations and vulnerabilities in existing systems. It then introduces the concept of TinyML and its suitability for deployment in various stages of the food supply chain. The paper delves into case studies and practical implementations of TinyML in food production, processing, transportation, and distribution. It highlights how edge AI and sensor technologies can provide real-time data analysis, enabling rapid identification of contaminants, spoilage, and counterfeit products. Furthermore, the use of TinyML in IoT-enabled devices enhances supply chain visibility and transparency. We present results from experimental deployments and highlight the benefits of TinyML, including reduced response times to incidents, enhanced traceability, and the prevention of food fraud. Additionally, we discuss the challenges associated with implementing TinyML in the food industry, including model optimization, data privacy, and regulatory compliance.