Precise Automation and Analysis of Environmental Factor Effecting on Growth of St. John's Wort
Bibliographic record
Abstract
The main goal of this study was to enable cultivating St. John's wort not only in Europe and West Asia, but also in Thailand, Southeast Asia despite warmer climate than in the natural growth regions. The challenge then was to control environmental factors with an automatic system. The Internet of Things (IoT) was applied in the sensor devices to control and collect relevant environmental data from the designed greenhouse. Moreover, data analysis by multiple linear regression was applied to enable the control of the designed greenhouse environment. It was used to discover interesting relationships between variables. The proposed system was implemented with hardware, a web application, and a mobile application. The hardware was designed to collect data and implement control of air temperature, air relative humidity, soil moisture, and light from sensors in the field. The web and mobile applications were developed to manipulate the obtained data, for intelligent control, and for real-time monitoring of environmental factors. The control system included an evaporative cooling system, fogging system, irrigation system, and artificial light system. The results show that the proposed system to assist and support the growth of St. John's wort was successful. Moreover, the results from this research can be used to culture St. John's wort, in order to produce medicines that are beneficial to human health in regions with the tropical climate.
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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 | 0.000 | 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".