The role of technology in food safety: Machine Learning in tot devices to preserve nutrients and improve the quality of Blueberries in postharvest

Abstract

The research aims to improve the quality of the final product and efficiency in the supply chain through the use of IoT technology and machine learning, and in consideration that the lack of real-time monitoring during post-harvest has led to losses in hydration, conservation, nutritional value and firmness of crops, this generates uncertainty in the management of incidents and affects the efficiency in the supply chain, therefore the implementation of IoT technology allows to constantly monitor parameters such as temperature and humidity, in addition, the use of machine learning allows to automatically adjust the environmental conditions according to the needs of the product, improving quality and reducing losses, this evaluated with the research methodology combines the systematization of information with a case study based on IoT, incorporating variables such as temperature, humidity and air quality, to design an efficient process in the post- harvest of blueberries in Guasca, Cundinamarca, since the implementation of IoT technology and machine learning in the supply chain of blueberries is considered essential to ensure product quality, optimize efficiency and reduce food loss.