Diverse Flowering Response to Blue Light Manipulation: Application of Electric Lighting in Controlled-Environment Plant Production
Bibliographic record
Abstract
Blue light is an important light wavelength in regulating plant flowering. In a controlled environment (CE) plant production systems, blue light can be manipulated easily and even precisely through electric lighting, especially with the advancement of light-emitted diode (LED) technologies. However, the results of previous studies in the literature about blue-light-mediated flowering are inconsistent, which would limit its practical application in CE plant production while implying that an in-depth study of the relevant physiological mechanism is necessary in the future. This review consolidates and analyzes the diverse findings from previous studies on blue light-mediated plant flowering in varying high-value crops from ornamental plants to fruits, vegetables, and specialty crops. By synthesizing the contrasting results, we proposed the possible explanations and even the underlying mechanisms related to blue light intensity and exposure duration, its co-action with other light wavelengths, background environment conditions, and the involved photoreceptors. We have also identified the knowledge gaps based on these studies and outlined future directions for research and potential application in this promising field. This review provides valuable insights into the important and diverse role of blue light in plant flowering and offers a foundation for further investigations to optimize plant flowering through lighting technologies.
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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 | 0.001 |
| 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".