Monitoring and Automation of Temperature Control Based on Mobile Application Technology (MAT) for Precision Oyster Mushroom Cultivation
Bibliographic record
Abstract
Oyster mushrooms can usually be found in the forest and grow on rotten logs with temperatures ranging from 21-28℃. The farmers are used the feeling method to measure the range of air temperature in the oyster mushroom cultivation area so that faced the difficulty for controlling the temperature conventionally. This research describes an intelligent device to regulate the air temperature automatically using Peltier TEC-1 12706, which was assisted with ice cubes and aluminum water blocks as cool temperatures automatically online and in real-time. The online system uses the Wi-Fi module type ESP8266-01, and the reading result converts to the digital number as the mobile application shown on the LCD display. Data storage on things speak as a cloud. This research indicates that the level of accuracy of this system compared with similar measuring devices on the market has a standard deviation of error of 0.21 from the total of 30 data in a 1-hour trial. This research was compared with the conventional product (HTC-2) for knowing the sensor accuracy. The experiment result indicated that the sensor accuracy was 93.7%. SHT31 sensor as temperature sensor proved capable to monitor and automatic oyster mushrooms temperature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".