Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Abstract

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

“…At the same time, the correct model and processing method need to be used, making prediction difficult. For the modeling methods, the overall ranking of model performance is as follows: RF > BPNN > SVM > PLSR, which is consistent with the findings of previous studies, but the order of XGBoost is usually not fixed [37,45,52,53]. The prediction of different soil parameters is different for different data preprocessing methods and modeling approaches.…”

“…Kawamura et al [35,44] found that the machine learning model showed higher accuracy than the traditional linear model (e.g., multiple linear stepwise regression and partial least squares) when predicting soil parameters (e.g., N and P) using Vis-NIR. Previous research in their study on predicting soil TN using Vis-NIR technology combined with support vector machines, random forests, extreme gradient boosting (XGBoost), and backpropagation neural networks discovered that all machine learning methods could achieve accurate TN predictions, with the model accuracy ranking as follows: RF > BPNN > SVM [37,45,52,53]. XGBoost's performance may be higher or somewhere in between.…”

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