ArticleSensors (Basel, Switzerland)2022
Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Wild Emmer Wheat B3 Transcription Factor Regulates Seed Growth and Nutrient Allocation.Plant, cell & environment · 2026Article
- Microbial diversity, metabolic specialization, and genomic novelty across polyextreme saline lakes of the Central Dry Andes.Environmental microbiome · 2026Article
- In Situ Rapid Detection of Soil Nitrate Nitrogen via Dielectric Spectroscopy Using a Dual-Band Frequency-Splitting Coupled Sensor.ACS sensors · 2026Article
- Soil total nitrogen prediction based on multi-temporal synthetic Sentinel-2 images under different land use types.Scientific reports · 2025Article
- PCA- and PLSR-Based Machine Learning Model for Prediction of Urea-N Content in Heterogeneous Soils Using Near-Infrared Spectroscopy.Sensors (Basel, Switzerland) · 2025Article
- Near-Infrared Spectroscopy Combined with Explainable Machine Learning for Storage Time Prediction of Frozen Antarctic Krill.Foods (Basel, Switzerland) · 2025Article
- Trends of Soil and Solution Nutrient Sensing for Open Field and Hydroponic Cultivation in Facilitated Smart Agriculture.Sensors (Basel, Switzerland) · 2025Review
- Functional stratification and enzymatic arrangement in microbial communities across a hypersaline depth gradient.Frontiers in microbiology · 2025Article
- NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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 R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.