Evaluation of two miniaturized FT‐NIR spectrometers for rapid soil property analysis
Bibliographic record
Abstract
Abstract Utilizing reflectance spectroscopy to generate the necessary soil data to drive innovations in precision agriculture and soil management is an increasing focus of agronomic research. One of the key limitations for widespread practical adoption of reflectance spectroscopy is hardware cost, and lower cost hardware is actively being developed. This study evaluated two inexpensive nano Fourier‐transform near infrared spectrometers in the laboratory. One was a laboratory‐based analyzer (LabFlow) and the second was a field portable analyzer (Field Probe). Soil spectra were collected in the shortwave infrared range and processed using wavelet transforms and machine learning models. The optimal wavelet transforms and machine learning model were selected using cross validation on the training dataset, and performance of the optimal model was evaluated using an independent testing dataset. The Field Probe configuration total nitrogen model had the best performance when compared to the LabFlow laboratory analyzer with an R 2 of 0.91, a concordance correlation coefficient of 0.95, and an root mean square error of 0.03. Soil inorganic carbon did not perform as well with an R 2 of 0.65. However, performance was likely limited by a large number of low values and a limited range in the training dataset. Overall, these results highlight the potential for lower cost spectrometers to provide useful soil data for soil management applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".