Rapid Assessment of Soil Nutrient-Related Environmental Risks and Safety Using Near Infrared Spectroscopy and Machine Learning
Bibliographic record
Abstract
Presented work aimed to improve near infrared spectroscopic (NIRS) prediction models for rapid and simultaneous estimation of N, P, K, pH, Mg, and Ca contents in agricultural soils.We compared partial least square regression (PLSR) and support vector machine (SVM) approaches applied to multiplicative scatter correction (MSC) corrected spectral data.The results demonstrated that grid search optimized radial basis function (RBF) kernel SVM models consistently outperformed PLSR models for all soil nutrients analyzed.The SVM models achieved excellent predictive performance, with coefficient of determination (R 2 ) and ratio of prediction to deviation (RPD) values from external prediction datasets as follows: N (R 2 = 0.83, RPD = 2.80), P (R 2 = 0.96, RPD = 4.33), K (R 2 = 0.91, RPD = 3.00), pH (R 2 = 0.96, RPD = 2.89), Mg (R 2 = 0.98, RPD = 4.34), and Ca (R 2 = 0.99, RPD = 4.99).These results indicate good to excellent predictive performance for simultaneous estimation of agricultural soil nutrients using optimized SVM-based NIRS models.This novel approach offers a rapid, non-destructive method with significant potential for improving precision agriculture and environmental monitoring by enhancing soil quality assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".