Soil Nitrogen Detection Based on Random Forest Algorithm and Near Infrared Spectroscopy

Zheng, Beitian; Xiao, Tengfei; Wang, Mengchen; Tian, Shihao; Tan, Baohua

doi:10.1109/iccbe56101.2022.9888195

Cited by 3 publications

(1 citation statement)

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“…Near-infrared spectroscopy technology has the following characteristics: a fast speed, convenient detection, low cost, no pollution in the detection process, wide detection range, high detection efficiency, non-destructive, able to simultaneously determine multiple groups, etc. With the rapid development of computer application technology, chemometrics methods, statistical theory, and the high integration of multi-disciplinary technologies have been put forward, and their feasibility for NIR detection has been verified [ 20 , 21 , 22 , 23 , 24 ]. At the same time, random forest (RF) is a common machine learning method that is usually used to deal with classification [ 25 , 26 , 27 ] and regression [ 28 , 29 , 30 ] problems.…”

Section: Introductionmentioning

confidence: 99%

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Tan

You

Tian

et al. 2022

Sensors

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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.

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