Development of calibration equations for capacitance sensors to measure soil water content using an IoT-based network
Bibliographic record
Abstract
Soil Water Content (SWC) plays a vital role in agriculture. Knowledge of SWC helps the farmers understand the crop’s water requirement and achieve a better yield. Due to commercialization, well-developed sensors are expensive for large-scale agriculture. There is a need for low-cost soil sensors to measure SWC precisely with spatial and temporal resolution. Low-cost soil sensors like SEN0193 needed to be calibrated and validated using the gravimetric method to obtain SWC. The SEN0193 sensors were integrated with the raspberry-pi stand-alone system, with mini-controllers (Arduino) to log the data from the sensors. The SEN0193 sensors were tested under a controlled environment with target VWC samples (Sand: 10, 20, 25, 30%, Loam: 10, 20, 30, 40, 50%, Clay: 10, 20, 30, 40%) prepared in the laboratory. The calibration equations were developed and validated both under laboratory and field conditions to find out the accuracy and precision. The SEN193 sensors demonstrated poor precision on fine particle-sized soils such as loam (0.003 m3/m3) and clay (0.003 m3/m3) and moderate precision on sand (0.002 m3/m3). Validation of sensors at different depths of 20 cm and 40 cm during the field study proved that the surface contact of the sensor with soil was affected by the air gap. Each sensor provided a different precision, which confirms the SEN0193 sensor’s fragile nature. A comparison of TEROS T10 sensors with SEN0193 sensors showed that the T10 sensors work with better accuracy and precision. Monitoring the airgap is, a better installation method with careful handling of the SEN0193 sensor could increase its performance. A water uptake pattern study of the canola root zone was conducted using the SEN0193 sensor at the Winkler site. Rainfall and the presence of crops influenced the upward groundwater flux. The shallow groundwater table with saline content adversely affects the crop. Using the sub-surface drainage system to remove the excess water will help the crop to attain a higher yield by limiting the upward flux of saline groundwater.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".