Design of the Humidity and Temperature Controller Using the Moistures of Leaf and Soil
Bibliographic record
Abstract
A controlling of air temperature and humidity is important issue because environment change has effects on growing stages of plants. In this study a (DHT22, AH-300u, YL-69) sensors and a microcontroller (arduino Uno) have been used to monitoring and controlling the temperature, humidity of air and control irrigation process based on monitoring the moistures of (soil, plant leaf) data inside the greenhouse prototype. The findings during the experiment time (4 days) indicates that the air temperature maintain at reference value 27℃ and relative humidity has a slightly increase by 0.7 from reference value (82). The output voltage of soil moisture sensor has been monitor at a small range (0.8 v - 1 v) roughly stable, and it is near to field capacity. The output voltage of leaf moisture sensor is slightly increased. From this study it has been observed that the proposed system in a greenhouse is a good procedure to maintain air temperature and humidity inside the greenhouses and effective for monitoring air factors as well as soil and leaf moistures. regarding irrigation process, a decision table has been suggested to control the irrigation water flow rate according to output voltage of each of soil and leaf moistures sensors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".