An IoT-based system for growth optimization of St. John's Wort in controlled tropical agriculture with phytochemical and cytotoxicity screening
Bibliographic record
Abstract
This study explores the application of IoT-based environmental monitoring in optimizing the growth and phytochemical and cytoxicity properties of Hypericum perforatum L. (St. John's Wort) grown in tropical agricultural conditions. The research focused on the implementation of wireless sensor networks (WSNs) to continuously monitor environmental factors, including soil moisture, temperature, air humidity, and light intensity, in controlled greenhouse conditions. By enhancing communication between nodes and gateways, the IoT system improved data transfer efficiency and environmental factor control. The study also investigated the response of Hypericum perforatum to different fertilizer treatments, with a specific emphasis on developing a fertilizer schedule that maximizes nutrient uptake and promotes optimal growth. Phytochemical screening was conducted to assess the presence of key compounds, including terpenoids, flavonoids, and alkaloids, with notable differences observed between plants grown under IoT-monitored conditions and those without. The results indicate that IoT-based environmental control can significantly improve plant growth, survival rates, and phytochemical profiles, offering a promising approach for cultivating medicinal plants in tropical environments. The cells were tested with various concentration of ethanolic extract of St. John Wort plant grown in uncontrolled net house, controlled greenhouse and plant factory. MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-dipenyltetrazolium bromide] assay was performed to assess cytotoxic activity against human cervical cancer cell lines (HeLa and SiHa). The extract from St. John Wort grown in uncontrolled net house showed a high cytotoxicity (IC50 = 13-18 μg/mL) against the cervical cancer cells and an extract from the plant factory experiment were less cytotoxic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".