Development of a Mobile App for Real-Time Freshness Monitoring of Fresh Produce

Thesis Abstract

Food spoilage and quality degradation during transportation, storage, and retailing pose significant challenges to ensuring the safety and freshness of fresh produce, leading to economic losses and health risks. Despite advances in storage technology, consumers and distributors lack accessible, real-time monitoring tools to assess produce freshness at point of use, which hampers timely decision-making and reduces waste. This study aims to develop a mobile application that provides real-time freshness monitoring of fresh produce, integrating sensor data and analytical models to deliver accurate, user-friendly freshness assessments. The primary objectives include identifying key biometric and environmental indicators indicative of produce freshness, designing and implementing a mobile app interface that displays these indicators dynamically, and evaluating the app’s accuracy and usability among target users. The research adopts a mixed-methods design combining quantitative experimentation with qualitative usability assessment to explore the technical and user-centric dimensions of the solution. The population targeted comprises 300 fresh produce samples across five categories—leafy greens, berries, citrus fruits, root vegetables, and herbs—collected from local markets and farms. A sample size of 150 fresh produce items was selected using stratified random sampling, ensuring representation across categories. Additionally, 50 consumers and supply chain personnel participated in usability testing. Data collection instruments included portable multisensor devices capturing parameters such as temperature, humidity, ethylene emission, volatile organic compounds (VOCs), and microbial activity, complemented by structured questionnaires and semi-structured interviews assessing user experience and perceptions. Sensor data were analyzed via multiple linear regression and principal component analysis (PCA) to identify the most predictive indicators of freshness. The app performance was evaluated using accuracy metrics—including mean absolute error and receiver operating characteristic (ROC) curves—and user satisfaction was assessed through thematic analysis of qualitative feedback. The app's interface design was refined iteratively based on usability testing results following Human-Computer Interaction (HCI) principles grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Preliminary findings are expected to demonstrate that specific combinations of ethylene emissions and microbial load levels serve as reliable proxies of internal freshness, correlating significantly (p < 0.01) with visual deterioration scores. The app is anticipated to achieve an accuracy of over 85% in classifying produce into fresh, borderline, and spoiled categories. Usability testing is projected to reveal high acceptance among users, with positive feedback on ease of use, usefulness, and integration potential into existing supply chain practices. This research makes a substantial contribution to knowledge by embedding sensor-based analytics into a mobile platform tailored for the food supply chain, thereby bridging technological gaps in produce freshness monitoring. It extends existing literature on smart agriculture and food safety technology by operationalizing real-time, accessible assessments, potentially reducing food waste and enhancing consumer trust. The study concludes with recommendations for scaling the app prototype for commercial deployment, integrating cloud-based data analytics for broader contextual insights, and expanding sensor capabilities to encompass additional spoilage indicators. Future research is suggested to evaluate long-term impact on supply chain efficiency and consumer behavior, as well as exploring adaptations for different climatic and regional contexts. Overall, the study underscores the potential for ICT-driven solutions to revolutionize freshness management and contribute meaningfully to sustainable food systems.