Soil Organic Matter Detection Based on Pyrolysis and Electronic Nose Combined with Multi-Feature Data Fusion Optimization

Abstract

Soil organic matter (SOM) is one of the main sources of plant nutrition and promotes plant growth and development. The content of SOM varies in different areas of the field. In this study, a method based on pyrolysis and electronic nose combined with multi-feature data fusion optimization was proposed to realize rapid, accurate and low-cost measurement of SOM content. Firstly, an electronic nose was used to collect response data from the soil pyrolysis gas, and the sensor features (10 × 6) were extracted to form the original feature space. Secondly, Pearson correlation coefficient (PCC), one-way analysis of variance (One-Way ANOVA), principal component analysis algorithm (PCA), linear discriminant analysis algorithm (LDA), and genetic algorithm-backpropagation neural network algorithm (GA-BP) were used to realize multi-feature data fusion optimization. Thirdly, the optimized feature space was used to train the PLSR models, and the predictive performance of the models were used as an indicator to evaluate different feature optimization algorithms. The results showed that the PLSR model with GA-BP for feature optimization had the best predictive performance (R2 = 0.90) and could achieve accurate quantitative prediction of SOM content. The dimensionality of the optimized feature space was reduced to 30 and there was no redundancy in the sensor array.