Kim Yee LeeGobi Vetharatnam0000-0002-1435-4163Yong-Hong Lee0000-0001-5301-1115Eng-Hock LimCheng Ee MengKok Yeow You2026-10-152026-10-152025-10-27979833153772210.1109/TENCON66050.2025.11375129https://dspace-cris.utar.edu.my/handle/123456789/11756This study presents an AI-driven modeling approach for a microwave elliptical sensor used in dielectric measurements. Traditional analytical and numerical methods often face challenges such as high computational complexity, calibration difficulties, and reduced accuracy at higher frequencies, particularly with irregular sensor geometries like elliptical probes. To address these limitations, models were trained using reflection coefficient (S11) data from simulations across 0.5 - 8 GHz. Three techniques were evaluated: Multiple Linear Regression (MLR), Random Forest (RF), and artificial neural networks using Levenberg-Marquardt (ANN-LM). Performance analysis involved varying dataset sizes and input feature configurations (2 -input and 3-input). The trained models were then tested on real agricultural samples and compared with results from a standard open-ended coaxial measurement. The findings show that the proposed AI models accurately predict the complex permittivity of samples under test, enhancing modeling efficiency and adaptability, especially for sensors with non-standard geometries. This capability supports real-time dielectric characterization in agricultural applications. © 2025 IEEE.en-USArtificial IntelligenceDielectric MeasurementMicrowave Elliptical SensorNeural NetworksAgricultureLinear regressionNanotechnologyNumerical methodsSignal processingAgricultural samplesAnalytical and numerical methodsBased modellingDielectric measurementsEllipticalsHigh frequency HFMeasurements ofMicrowave elliptical sensorModeling approachNeural-networksNeural networkstext::conference output::conference proceedings::conference paper