Research on Detection Methods for Major Soil Nutrients Based on Pyrolysis-Electronic Nose Time-Frequency Domain Feature Fusion and PSO-SVM-RF Model

Against the backdrop of growing demand for rapid soil testing technologies in precision agriculture, this study proposes a detection method based on pyrolysis-electronic nose and machine olfaction signal analysis to achieve precise measurement of key soil nutrients. An electronic nose system comprising 10 metal oxide semiconductor gas sensors was constructed to collect response signals from 112 black soil samples undergoing pyrolysis at 400 °C. By extracting time-domain and frequency-domain features from sensor responses, an initial dataset of 180 features was constructed. A novel feature fusion method combining Pearson correlation coefficients (PCC) with recursive feature elimination cross-validation (RFECV) was proposed to optimize the feature space, enhance representational power, and select key sensitive features. In predicting soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP content, we compared support vector machines (SVM), support vector machine-random forest models (SVM-RF), and particle swarm optimization-enhanced support vector machine-random forest models (PSO-SVM-RF). Results indicate that PSO-SVM-RF demonstrated optimal performance across all nutrient predictions, achieving a coefficient of determination (R²) of 0.94 for SOM and TN, with a performance-to-bias ratio (RPD) exceeding 3.8. For AK and AP, R² improved to 0.78 and 0.74, respectively. Compared to the SVM model, the root mean square error (RMSE) decreased by 25.4% and 21.6% for AK and AP, respectively, with RPD values approaching the practical threshold of 2.0. This study validated the feasibility and application potential of combining electronic nose technology with a time-frequency domain feature fusion strategy for precise quantitative analysis of soil nutrients, providing a new approach for soil fertility assessment in precision agriculture.