Detection of Soil Moisture, Humidity, and Liquid Level Using CPW-Based Interdigital Capacitive Sensor
Bibliographic record
Abstract
The measurement of soil moisture and air humidity is essential to many technological and environmental applications. Sensors provide information that is useful in order to effectively manage and control various processes. This study presents an energy-efficient, highly reliable, ultra-high frequency (UHF) capacitive sensor that can measure moisture/humidity, and liquid levels. As a sensitive part, an interdigital capacitor (IDC) is fed via a coplanar waveguide (CPW) for efficient power transfer. Aside from the sensitive part of the IDC, the structure of the sensor is insensitive to variations in surrounding permittivity, which reduces uncertainty in results and thus improves accuracy. The principle of sensing is based on a reactive phase variation of the input signal upon reflection, whereas its amplitude and phase are actively changing with variations in the dielectric constant of the test medium. Practical testing was conducted on the sensor to determine soil moisture, air humidity, and liquid level measurement. At a fixed UHF frequency of 915 MHz the sensor offers a capacitive sensitivity of 7.5 fF/%WC, 4.5 fF/%RH, and 13.4 fF/mm for soil moisture, air humidity, and water level detection respectively. This new sensor provides high reliability, good sensitivity, low power consumption, and can be implemented in a number of applications, including agriculture, oil, and gas industry, land and water treatment, medical equipment, and biotechnology.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".