This study presents the development and evaluation of surface functionalized solidly mounted resonators (SMRs), including custom UWAR devices and commercial Sorex sensors, for the detection and classification of plant-emitted volatile organic compounds (VOCs). The sensors were tested against linalool, trans-2-hexenal (T2H), and D-limonene at different concentrations under both dry and humid conditions (up to 33% RH). A Python-based signal-processing workflow was established to filter frequency responses and extract key features, such as baseline, saturation point, and frequency shift (Δf). Adsorption behaviour was modelled using the Freundlich isotherm, showing good agreement with experimental data and suggesting heterogeneous, multilayer adsorption on CH₃-terminated EC surfaces. A 2D polar classification framework combining vector-normalized Δf values from UWAR and Sorex sensors enabled clear separation of the VOCs. The results highlight the complementary performance of the two types of SMR sensors and demonstrate that feature-engineered resonant devices, combined with computational classification, offer strong potential for future use in plant health monitoring systems.