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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".