ArticleSensors (Basel, Switzerland)2020
Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR).
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 112 citations in OpenAlex.
- Article
- Scalable solution for agricultural soil organic carbon measurements using laser-induced breakdown spectroscopy.Scientific reports · 2024Article
- Smart Sensors and Smart Data for Precision Agriculture: A Review.Sensors (Basel, Switzerland) · 2024Review
- Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture-A Case Study.Sensors (Basel, Switzerland) · 2023Article
- Comparison of Performance of Predicting the Wear Amount of Tire Tread Depending on Sensing Information.Sensors (Basel, Switzerland) · 2023Article
- Characterizing natural variability of lignin abundance and composition in fine roots across temperate trees: a comparison of analytical methods.The New phytologist · 2022Article
- Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review.Sensors (Basel, Switzerland) · 2022Review
- Determination of Soil Constituents Using Shifted Excitation Raman Difference Spectroscopy.Applied spectroscopy · 2022Article
- Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS.Scientific reports · 2021Article
- Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network.Frontiers in plant science · 2021Article
- Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination.Sensors (Basel, Switzerland) · 2020Article
- Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation.Sensors (Basel, Switzerland) · 2020Article
- Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV-Vis Spectrometry and SPSE-XGBoost Method.Sensors (Basel, Switzerland) · 2020Article
- Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS).Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
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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.