Self-Powered Meat Freshness Monitoring With GAN-Optimized 3-D Antenna and Ensemble Learning

Automated meat monitoring is crucial for ensuring safety, maintaining quality, reducing economic losses, adhering to regulations, managing inventory effectively, and minimizing environmental impact. This article presents a self-powered system for classifying meat freshness using ensemble learning and a battery-free sensor tag powered by energy harvested from a 3-D quasi-Yagi antenna (QYA). The QYA design features a slotted-bowtie structure at the driven element and a meandering structure at the reflector, optimized through generative adversarial networks (GANs) to address various parameters and reduce design time. Constructed from five 2-D QYAs arranged in a cube and an RF harvester PCB base, the 3-D QYA considerably enhances the energy harvesting performance. Compared to its 2-D counterpart, the 3-D QYA significantly enhances coverage area, gain, energy-scavenging efficiency, and transmission distance, achieving an average gain of 11.43 dBi over a 25-MHz band across prediction, simulation, and measurement. RF harvesting tests at 915 MHz achieved a maximum power conversion efficiency (PCE) of 68.2% at 9.5-dBm input power. This energy supports the high power demands of the integrated gas sensor for battery-free monitoring of meat freshness. Over five days, data on equivalent carbon dioxide, total volatile organic compounds (VOCs), temperature, and storage time were collected and analyzed using ensemble learning models with five primary learners. The system accurately classified meat freshness into fresh, semi-fresh, and rotten categories, tracking transitions between states with over 96% classification accuracy. Our results confirm the effectiveness of this system as a practical solution for industrial applications and for improving health and safety standards in meat consumption.