Soil Hardness Measurement Using Fiber Bragg Grating Sensor: Combined Compression Forces Methodology
Bibliographic record
Abstract
Accurate soil hardness measurement is essential for enhancing agricultural productivity and geotechnical stability. Conventional methods, such as cone penetrometers, offer limited precision as they only measure penetration resistance, without considering soil reaction forces. This study introduces a novel Fiber Bragg Grating (FBG) sensor system, which captures both downward penetration and upward push‐back forces, providing a more comprehensive evaluation of soil hardness. By combining theoretical modeling, simulations, and experimental validation, the relationship between soil moisture and penetration resistance is analyzed. Results show a nonlinear correlation between moisture content and force, with forces ranging from 1.2 × 10 4 N at 0.8 moisture content to 7.8 × 10 4 N at 0.2 moisture content, at a depth of 0.01 m. Additionally, the FBG‐embedded beam experiences a peak deformation of 1.25 × 10 −6 m at a depth of 0.25 m, confirming the sensor's high accuracy. The dual‐force measurement approach significantly enhances the precision of soil hardness evaluations, offering real‐time, high‐resolution data for sustainable soil management. Furthermore, new equations specific to FBG‐based soil resistance and Bragg wavelength shifts are proposed for dual‐force measurement addressing limitations of conventional methods. This study advances soil testing technologies, with potential applications in agriculture, geotechnical engineering, and environmental sustainability.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".