How the Distribution of Photon Delivery Impacts Crops in Indoor Plant Environments: A Review
Bibliographic record
Abstract
Indoor plant production allows for increased controllability over plant growth, particularly with the employment of specific lighting regimes. This article reviews research investigating the influence of various light regimes that provide crops with the same light spectrum and daily light integral (DLI), yet with different lighting schedules and intensities. The structure of these light regimes includes changing photoperiod duration and light intensity, cyclic lighting schedules (increased number of light/dark cycles over 24 h, same total illuminated time and intensity), and intermittent and fluctuating lighting (with intervals ranging from 60 s to 60 min). The most common crops investigated were leafy greens, although some fruiting, ornamental, and model crop Arabidopsis are included. Under constant DLI, extending photoperiod and reducing light intensity generally increased the total amount of biomass accumulation and increased light interception. Increases in the number of shortened light/dark cycles contributed to decreased yield, leaf area, and photosynthetic rate, compared to the more standard single light/dark cycle. Intermittent and fluctuating lighting regimes generally reduced biomass accumulation and light interception. These results indicate that the total amount of light delivered to crops is important, but how that light is delivered can influence crop growth. This review will be useful for growers and researchers when designing lighting systems and regimes to produce horticultural crops in indoor plant environment systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".