Harnessing controlled-environment systems for enhanced production of medicinal plants
Bibliographic record
Abstract
Medicinal plants are valued for their contributions to human health. However, the growing demand for medicinal plants and the concerns regarding their quality and sustainability have prompted the reassessment of conventional production practices. Controlled-environment cropping systems, such as vertical farms, offer a transformative approach to production of medicinal plants. By enabling precise control over environmental factors, such as light, carbon dioxide, temperature, humidity, nutrients, and airflow, controlled environments can improve the consistency, concentration, and yield of bioactive phytochemicals in medicinal plants. This review explores the potential of controlled-environment systems for enhancing production of medicinal plants. First, we describe how controlled environments can overcome the limitations of conventional production in improving the quality of medicinal plants. Next, we propose strategies based on plant physiology to manipulate environmental conditions for enhancing the levels of bioactive compounds in plants. These strategies include improving photosynthetic carbon assimilation, light spectrum signalling, purposeful stress elicitation, and chronoculture. We describe the underlying mechanisms and practical applications of these strategies. Finally, we highlight the major knowledge gaps and challenges that limit the application of controlled environments, and discuss future research directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".