Towards a tailored indoor horticulture: a functional genomics guided phenotypic approach
Bibliographic record
Abstract
As indoor horticulture gathers momentum, electric (also termed artificial) lighting systems with the ability to generate specific and tunable wavelengths have been developed and applied. While the effects of light quality on plant growth and development have been studied, authoritative and reliable sets of light formulae tailored for the cultivation of economically important plants and plant traits are lacking as light qualities employed across laboratories are inconsistent. This is due, at least in part, to the lack of molecular data for plants examined under electric lights in indoor environments. It has hampered progress in the field of indoor horticulture, in particular, the transition from small-scale indoor farming to commercial plant factories. Here, we review the effects of light quality on model and crop plants studied from a physiological, physical and biochemical perspective, and explain how functional genomics can be employed in tandem to generate a wealth of molecular data specific for plants cultivated under indoor lighting. We also review the current state of lighting technologies in indoor horticulture specifically discussing how recent narrow-bandwidth lighting technologies can be tailored to cultivate economically valuable plant species and traits. Knowledge gained from a complementary phenotypic and functional genomics approach can be harvested not only for economical gains but also for sustainable food production. We believe that this review serves as a platform that guides future light-related plant research.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".