An Update on Plant Photobiology and Implications for Cannabis Production
Bibliographic record
Abstract
This review presents recent developments in plant photobiology and lighting systems for horticultural crops, as well as potential applications for cannabis (Cannabis sativa and C. indica) plant production. The legal and commercial production of the cannabis plant is a relatively new, rapidly growing, and highly profitable industry in Europe and North America. However, more knowledge transfer from plant studies and horticultural communities to commercial cannabis plant growers is needed. Plant photosynthesis, photomorphogenesis are influenced by light wavelength, intensity, and photoperiod via plant photoreceptors that sense light and control plant growth. Further, light properties play a critical role in plant vegetative growth and reproductive (flowering) developmental stages, as well as in biomass secondary metabolite synthesis and accumulation. Advantages and disadvantages of widespread greenhouse lighting systems that use high pressure sodium lamps (HPS) or light emitting diode (LED) lighting are known. Some artificial plant lighting practices will require improvements for cannabis production. By manipulating LED light spectra and stimulating specific plant photoreceptors, it may be possible to minimize operation costs while maximizing cannabis biomass and cannabinoid yield, including tetrahydrocannabinol (or Δ9-tetrahydrocannabinol) and cannabidiol for medicinal and recreational purposes. The basics of plant photobiology (photosynthesis and photomorphogenesis) and electrical lighting systems are discussed, with an emphasis on how the light spectrum and lighting strategies could influence cannabis production and secondary compound accumulation.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".