Edge machine learning over IoT for chipless RFID environmental sensing in smart agriculture

Abstract

Chipless Radio Frequency Identification (RFID) has emerged as a promising technology for battery-free and maintenance-free sensing in Internet of Things (IoT) applications, particularly in smart agriculture where large-scale deployment and long-term autonomy are essential. However, practical agricultural sensing requires more than isolated tag or sensor designs; it demands an integrated system that jointly supports high-capacity identification, reliable multi-parameter environmental monitoring, and robust data interpretation within realistic radio-frequency (RF) environments. This work presents an IoT-ready chipless RFID framework that unifies resonator-based tag design, functional sensing materials, and physics-guided machine learning within a coherent hardware–analytics architecture. A compact 24-bit chipless RFID identification tag based on T-shaped resonators is designed within a 60 × 40 mm2 footprint, achieving dense encoding with approximately 100 MHz spectral spacing. The tag is validated through full-wave CST simulations and anechoic-chamber radar cross-section (RCS) measurements, demonstrating high-Q, well-isolated spectral notches. Building on this platform, a 12-resonator sensing variant is developed for dual-parameter microclimate monitoring, exploiting the temperature-sensitive permittivity of Taconic RF-35 and the humidity responsiveness of a Kapton HN/PVA bilayer, with approximately 160 MHz reserved per sensing slot to prevent spectral overlap. To enable reliable operation under deployment-realistic interference, a physics-driven, edge-deployable machine learning framework is introduced, operating on interpretable RCS features rather than raw spectra. A hybrid ensemble combining Random Forest, Support Vector Regression, and XGBoost models are developed and augmented with k-means–based anomaly detection. This framework achieves 96.2% temperature-bin classification accuracy, with mean errors of ± 1.3 °C for temperature and ± 2.1% for relative humidity under frequency jitter, attenuation, and multipath distortions. The proposed co-design demonstrates a scalable, interpretable, and energy-autonomous solution for precision agriculture. It is compatible with edge gateways and cloud or serverless IoT infrastructures.