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<p>Electronic noses (e-noses) offer a practical solution for real-time monitoring of ammonia (NH<sub>3</sub>) in agricultural environments, where NH<sub>3</sub> often coexists with interfering gases such as CO<sub>2</sub>, CH<sub>4</sub>, and H<sub>2</sub>S. 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 NH<sub>3</sub> detection and introduces a mitigation strategy through convolutional neural networks (CNNs) for sensor data fusion. Experimental results show that WO<sub>2</sub>-based sensors exhibit strong NH<sub>3</sub> selectivity, with response ratios of 7.3:1 against CH<sub>4</sub> and 17.8:1 against H<sub>2</sub>S. Density functional theory (DFT) analysis confirmed that the WO<sub>3</sub> sensor exhibited strongest NH<sub>3</sub> binding energy (− 1.45&#xa0;eV), compared to SnO<sub>2</sub> (− 1.10&#xa0;eV), explaining the observed selectivity. Measurement uncertainties (± 8%) were quantified under varying humidity (30–90% RH) and temperature (10–40&#xa0;°C) using a weighted least squares error propagation model. A quasi-2D sensor array improved NH<sub>3</sub> 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 SnO<sub>2</sub> sensors maintained a low baseline drift of 0.18%/day over 180&#xa0;days, outperforming CH<sub>4</sub> (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 NH<sub>3</sub> quantification despite interferents. These findings highlight the promise of CNN-enhanced e-nose systems for precise NH<sub>3</sub> monitoring in complex agricultural settings, addressing key challenges of cross-sensitivity and environmental stability</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Mengying Du,
  • Mukhtar Iderawumi Abdulraheem,
  • Lulu Xu,
  • Yiheng Zang,
  • Yinghang Song,
  • Maryam Abbasi Tarighat,
  • Vijaya Raghavan,
  • Jiandong Hu

摘要

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