ArticleSensors (Basel, Switzerland)2019
Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 55 citations in OpenAlex.
- Rapid and Non-Destructive Detection of Moisture Content in Dried Areca Nuts Based on Near-Infrared Spectroscopy Combined with Machine Learning.Foods (Basel, Switzerland) · 2026Article
- Prediction of Soil Properties Using Vis-NIR Spectroscopy Combined with Machine Learning: A Review.Sensors (Basel, Switzerland) · 2025Review
- Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.Food science & nutrition · 2024Article
- Performance Comparison of Tungsten-Halogen Light and Phosphor-Converted NIR LED in Soluble Solid Content Estimation of Apple.Sensors (Basel, Switzerland) · 2023Article
- Spatial-Spectral Constrained Adaptive Graph for Hyperspectral Image Clustering.Sensors (Basel, Switzerland) · 2022Article
- Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry.Sensors (Basel, Switzerland) · 2021Article
- Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy.Sensors (Basel, Switzerland) · 2020Article
- Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of 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.