Microwave Surface Reflection Method for Soil Moisture Determination Using frequency of 1.7-2.6 GHz
Bibliographic record
Abstract
Soil moisture is one of the critical components to be investigated in civil, geological and agricultural works. This is because the parameter can affect the physical and electromagnetic characteristics of soil, such as density and permittivity and this can further restricts soil application. Unfortunately, finding a suitable non-destructive model for accurate soil moisture determination is challenging. In this article, the concept and development of soil moisture determination via ground penetrating radar (GPR) principle and surface reflection method is explained. The system is designed to be used with standard horn antenna with a frequency of 1.7-2.6 GHz along with vector network analyzer (VNA). The proposed system can measure soil moisture of three types of soil samples such as sand, loamy, and clay with high degree of accuracy. In this research, microwave surface reflection method is applied to analyze the effect of soil moisture with its electrical properties using our novel GPR principle. The result of the research is promising with high percentage of agreement with Topp theoretical value. The values are 31% to 61% for sand, 5% to 42% for clay, and 44% to 54% for loamy. For validation on the system, a new type of soil is used for measurement, and the result has an accuracy of 93%. By using the proposed developed models, soil moisture estimation can be easily determined with minimal data input through a novelty GPR surface reflection method.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".