Battery-Free Pork Freshness Estimation Based on Colorimetric Sensors and Machine Learning
Dong-Eon Kim, Yudi April Nando, Wan‐Young Chung
Abstract
In this study, a compact smart-sensor tag is developed for estimating pork freshness. The smart sensor tag can be placed in areas where packaged meat is stored or displayed. Antennas and simulated models were developed to maximize the efficiency of radio frequency (RF) energy harvesting. The proposed smart sensor tag includes a red, green, and blue sensor that detects changes in the freshness of meat. To detect the color changes in pork stored at a perishable hot temperature in an outdoor environment, this study applies Hue, Saturation, and Value conversion using machine learning, through which the freshness can be determined with a high degree of accuracy. Validation experiments of the sensor tag performance demonstrate that meat freshness can be detected at distances up to 50 cm from the RF using only the RF energy harvesting without changing the battery source. The 1D convolutional neural network model outperforms the traditional MLP and ConvLSTM models in terms of accuracy and loss.