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 (R2), 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 R2, 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 R2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R2 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.
Highlights
The sum of the various forms of nitrogen in the soil is called soil total nitrogen (STN)
The initial modeling refers to the development of an evaluation prediction model based on the training set (121 samples × 60 features) of the initial soil total nitrogen feature space (ISTNFS) and the chemically true values of the total nitrogen content of the corresponding soil samples, and the application of a test set to validate the prediction performance of the model
To optimize the effect for general applicability, this study investigated the relationship between soil olfactory characteristics and soil total nitrogen content through the initial modeling calibration effect of three commonly used prediction models for soil olfactory characteristics, backpropagation neural network (BPNN) model, extreme learning machine (ELM) model, and partial least squares regression (PLSR) model
Summary
The sum of the various forms of nitrogen in the soil is called soil total nitrogen (STN). Li et al [8] applied hyperspectral techniques to extract characteristic wavelengths using an uninformative variable elimination algorithm (UVE) and successive projection algorithm (SPA), and combined partial least squares (PLS) and extreme learning machine (ELM) to build a soil total nitrogen prediction model, achieving better prediction results These methods compensate for the shortcomings of classical methods to a certain extent, the high cost of analytical instruments, the influence of the atmosphere, and iron–oxide in the soil severely limit their application [9]. In the second stage of optimization (feature dimensionality reduction), soil olfaction spatial dimensionality reduction was performed using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural netw3oorfk (GA-BP) methods, and BPNN, ELM, and partial least squares (PLSR) were established.
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