Enhanced Selectivity Electronic Nose Systems for Agricultural Ammonia Gas Detection via a co-designed WO3-ZnO Sensor Array and Convolutional Neural Networks

Electronic noses (e-noses) offer a practical solution for real-time monitoring of ammonia (NH₃) in agricultural environments, where NH₃ often coexists with interfering gases such as CO₂, CH₄, and H₂S. However, semiconductor-based gas sensors commonly used in e-nose systems suffer from inherent cross-sensitivity, which reduces measurement accuracy. This study investigates the cross-sensitivity of NH₃ detection and introduces a mitigation strategy through convolutional neural networks (CNNs) for sensor data fusion. Experimental results show that WO₂-based sensors exhibit strong NH₃ selectivity, with response ratios of 7.3:1 against CH₄ and 17.8:1 against H₂S. Density functional theory (DFT) analysis confirmed that the WO₃ sensor exhibited strongest NH₃ binding energy (- 1.45 eV), compared to SnO₂ (- 1.10 eV), explaining the observed selectivity. Measurement uncertainties (± 8%) were quantified under varying humidity (30-90% RH) and temperature (10-40 °C) using a weighted least squares error propagation model. A quasi-2D sensor array improved NH₃ classification accuracy to 96.4% (7.2% increase) while reducing concentration errors by 50.8%, as validated by linear discriminant analysis. Long-term stability tests demonstrated that SnO₂ sensors maintained a low baseline drift of 0.18%/day over 180 days, outperforming CH₄ (0.31%/day) and ZnO (0.42%/day) sensors. Furthermore, the CNN model, trained on multi-sensor time-series data, achieved 91.7% accuracy in mixed-gas environments by capturing non-linear response patterns, ensuring reliable NH₃ quantification despite interferents. These findings highlight the promise of CNN-enhanced e-nose systems for precise NH₃ monitoring in complex agricultural settings, addressing key challenges of cross-sensitivity and environmental stability.