Development of an IoT-powered Sensor System for Real-Time Food Freshness Monitoring

Thesis Abstract

The rapid deterioration of perishable food items during transportation and storage presents significant challenges to supply chain efficiency, consumer safety, and food waste reduction. Traditional methods for assessing food freshness rely on manual inspections and periodic sampling, which are often subjective, labor-intensive, and incapable of providing continuous monitoring. As global food consumption demand increases, there is an urgent need for innovative technological solutions capable of offering real-time, accurate, and non-intrusive assessment of food freshness throughout the supply chain. This study aims to develop an Internet of Things (IoT)-powered sensor system for continuous monitoring of food freshness, with specific objectives including designing a sensor network capable of detecting key freshness indicators such as pH, volatile organic compounds (VOCs), temperature, and humidity; integrating these sensors into a low-power IoT platform; implementing data transmission protocols suitable for real-time updates; and evaluating system performance through empirical testing. The research adopts a mixed-methods approach combining quantitative experimental design with qualitative system usability assessment. The quantitative component involves deploying the sensor system across a sample of 200 perishable food packages, including fruits, vegetables, and dairy products, stored under varying environmental conditions typical of supply chain stages. Data collection instruments include multi-sensing units equipped with gas sensors, temperature and humidity sensors, and microcontrollers interfaced via Wi-Fi modules. Qualitative usability and user acceptance are gauged through structured interviews with supply chain stakeholders. Data analysis employs regression analysis to establish correlations between sensor readings and laboratory-based freshness assessments, as well as ANOVA to compare system performance across different food types and storage conditions. Thematic analysis of interview transcripts offers insights into system usability and potential barriers to adoption. Expected findings indicate that the IoT sensor system can reliably detect changes in key freshness indicators, with high correlation coefficients (>0.85) between sensor data and laboratory measurements such as microbiological analysis and chemical assays. The system is anticipated to demonstrate an accuracy rate exceeding 90% in predicting freshness deterioration, with robust real-time data transmission and alerts under simulated supply chain conditions. Furthermore, user feedback is expected to reveal high acceptance levels owing to ease of deployment, real-time visibility, and potential for reducing food wastage through timely interventions. These findings are poised to fill existing gaps in literature concerning scalable IoT-based freshness monitoring systems, providing empirical evidence of their operational efficacy and economic viability. The study also proposes a conceptual model integrating sensor data analytics with supply chain decision-making processes rooted in the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory, highlighting pathways for future adoption. The contribution to knowledge includes the development of a validated IoT sensor prototype tailored to actual supply chain scenarios, and an analytical framework to assess food freshness dynamically. This research advances the field of food quality monitoring by demonstrating how sensor fusion, combined with IoT connectivity, can revolutionize traditional practices, enabling proactive management of food safety and quality. The study concludes that the implemented system has significant potential to enhance transparency, reduce food spoilage, and improve consumer trust in fresh produce. Recommendations include scaling system deployment across diverse supply chain contexts, integrating machine learning algorithms for predictive analytics, and fostering stakeholder collaborations for commercial adaptation. Furthermore, future research should explore the integration of blockchain technology for traceability, as well as the economic analysis of system implementation to inform policy formulations promoting sustainable food supply chains.