Enhanced Internet of Things-Based Intelligent Sensors for Instantaneous Food Quality Monitoring using Deep Neural Network

Abstract

Food product quality and safety are critical in the global supply chain, necessitating new techniques of effective monitoring. This paper presents an integrated system for real-time food quality monitoring based on advanced analytical algorithms and IoT-enabled smart sensors. The proposed methodology asks for the selection and deployment of sensors to monitor critical quality indicators such as temperature, humidity, pH levels, and gas concentrations across the food supply chain, from manufacturing to sale. These sensors collect data, which is wirelessly transmitted to a centralized computer for sophisticated processing and analysis using machine learning techniques and agricultural chemistry principles. The technology uses models like Random Forest, Support Vector Machines (SVM), and Neural Networks to effectively estimate food quality. In forecasting shelf life, the Random Forest model has an accuracy of 0.92, recall of 0.89, and F1-score of 0.90, with a mean absolute error (MAE) of 1.5 days. Additional study will concentrate on lowering installation costs, improving real-time response capabilities, and customizing the system to different food types and supply chain circumstances. To improve the system's accuracy and dependability, new data fusion techniques and evaluation criteria must be adopted. This study represents a significant advancement in the integration of computer science, chemical agriculture, and Internet of Things technologies for improving food safety and reducing waste in the supply chain.