Development of an Automated Real-Time System for Soil Temperature and Moisture Measurement
Bibliographic record
Abstract
This trial goes along with irrigation systems based on the development and use of free software and hardware for direct measurements of soil moisture and temperature throughout the plant cycle. Thus, irrigation systems can optimize water use during the process at lower cost regarding TDR application. Four humidity sensors were used: one was resistive, and three capacitors were interconnected in a mesh network system. Thus, this research was carried out in laboratory and the studied soil was characterized as a typical dystroferric Red Latosol (Oxisol) with very clayey texture (66%). Soil clods were undone and dried in a greenhouse, then divided in 20 containers with addition of known volumes of water in each one. A network of mesh-type node sensors has been developed based on Arduino technology to read and transmit data to a single gateway. The sensor node was designed and built with Arduino Nano, radio NRF24L01, capacitive sensors of type SHT20 and DHT22, in addition to FC-28 that is resistive. The system also featured a Real Time Clock DS1302, three photovoltaic cells and circuit battery charger. Domoticz software was used to store data and make them available on a server connected to the internet. Cubic modeling was one of the results of the relation among each sensor, TDR and the greenhouse method. The resistive sensor showed very close values to the TDR in its model as well as the set of the monitoring system showed low cost in relation to TDR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".