Machine Learning-Aided Dual-Function Microfluidic SIW Sensor Antenna for Frost and Wildfire Detection Applications

In this paper, which represents a fundamental step in ongoing research, a new smart low-energy dual-function half-mode substrate integrated waveguide cavity-interdigital capacitor (HMSIWC-DIC) antenna-based sensor is developed and investigated for remote frost and wildfire detection applications at 5.7 GHz. The proposed methodology exploits the HMSIW antenna-based sensor, a microfluidic channel (microliter water channel (50 μL)), interdigital capacitor technologies, and the resonance frequency parameters combined with machine learning algorithms. This allows for superior interaction between the water channel and the TE101 mode, resulting in high sensitivity (∆f/∆ε = 5.5 MHz/ε (F/m) and ∆f/∆°C = 1.83 MHz/°C) within the sensing range. Additionally, it exhibits high decision-making ability and immunity to interference, demonstrating a best-in-class sensory response to weather temperature across two ranges: positive (≥0 °C, including frost and wildfire) and negative (<0 °C, including ice accumulation). To address the challenges posed by the non-linear, unpredictable behavior of resonance frequency results, even when dealing with weak sensor antenna responses, an innovative sensory intelligent system was proposed. This system utilizes resonance frequency results as features to classify and predict weather temperature ranges into three environmental states: Early Frost, Normal, and Early Wildfire, achieving an accuracy of 96.4%. Several machine learning techniques are employed, including artificial neural networks (ANNs), random forests (RF), decision trees (DT), support vector machines (SVMs), and Gaussian processes (GPs). This sensor serves as an ideal solution for energy management through its utilization in RF-based weather temperature sensing applications. It boasts stable performance, minimal energy consumption, and real-time sensitivity, eliminating the necessity for manual data recording.