Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System

He Liu, Qinghui Zhu, Xiaomeng Xia, Mingwei Li and Dongyan Huang
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He Liu: Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China
Qinghui Zhu: Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China
Xiaomeng Xia: Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China
Mingwei Li: Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China
Dongyan Huang: Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China

Agriculture, 2021, vol. 12, issue 1, 1-17

Abstract: To improve the accuracy of detecting soil total nitrogen (STN) content by an artificial olfactory system, this paper proposes a multi-feature optimization method for soil total nitrogen content based on an artificial olfactory system. Ten different metal–oxide semiconductor gas sensors were selected to form a sensor array to collect soil gas and generate response curves. Additionally, six features such as the response area, maximum value, average differential coefficient, standard deviation value, average value, and 15th-second transient value of each sensor response curve were extracted to construct an artificial olfactory feature space (10 × 6). Moreover, the relationship between feature space and soil total nitrogen content was used to establish backpropagation neural network (BPNN), extreme learning machine (ELM), and partial least squares regression (PLSR) models were used, and the coefficient of determination ( R 2 ), root mean square error ( RMSE ), and the ratio of performance to deviation ( RPD ) were selected as prediction performance indicators. The Monte Carlo cross-validation (MCCV) and K-means improved leave-one-out cross-validation (K-means LOOCV) were adopted to identify and remove abnormal samples in the feature space and establish the BPNN model, respectively. There were significant improvements before and after comparing the two rejection methods, among which the MCCV rejection method was superior, where values for R 2 , RMSE , and RPD were 0.75671, 0.33517, and 1.7938, respectively. After removing the abnormal samples, the soil samples were then subjected to feature-optimized dimensionality reduction using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural network (GA-BP). The test results showed that after feature optimization the model indicators performed better than those of the unoptimized model, and the PLSR model with GA-BP for feature optimization had the best prediction effect, with an R 2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R 2 and RPD values improved by 14.01% and 50.60%, respectively, compared with those before optimization, and RMSE value decreased by 45.16%, which effectively improved the accuracy of the artificial olfactory system in detecting soil total nitrogen content and could achieve more accurate quantitative prediction of soil total nitrogen content.

Keywords: soil total nitrogen; thermal cracking; artificial olfactory system; abnormal sample removal; feature optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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