A Real-Time IoT-Enabled Machine Learning for Quality Prediction of Perishable Beef Product

Abstract

The cold chain industry in Indonesia is experiencing rapid growth, especially for perishable products such as beef. Ensuring product quality during distribution requires accurate monitoring of storage environmental factors, including temperature, humidity, and gas exposure. This study aims to develop an IoT-based quality monitoring system for perishable beef products and implement a machine learning approach for quality prediction. An IoT-enabled e-Sense device was developed to collect real-time environmental parameters and RGB colour information as quality parameter from tenderloin beef samples. The collected data were analysed using three regression-based machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR). Data preprocessing and hyperparameter tuning were applied to improve model performance. The results show that SVR consistently outperformed RF and DT in predicting RGB colour in presenting beef quality based on prescribed parameters. SVR achieved an R² of 0.973 for RED and 0.992 for both GREEN and BLUE channels. These findings confirm the effectiveness of integrating IoT technology with machine learning for real-time perishable products quality prediction. This research contributes to combining real-time multi-sensor IoT data with regression-based models to provide improved continuous quality monitoring compared to previous single-parameter or offline approaches