Growth and Appearance Quality of Four Microgreen Species under Light-emitting Diode Lights with Different Spectral Combinations
Bibliographic record
Abstract
To investigate plant growth and quality responses to different light spectral combinations, cabbage ( Brassica oleracea L. var. capitata f. rubra ), kale ( Brassica napus L. ‘Red Russian’), arugula ( Eruca sativa L.), and mustard ( Brassica juncea L. ‘Ruby steak’) microgreens were grown in a controlled environment using sole-source light with six different spectra: 1) FL: cool white fluorescent light; 2) BR: 15% blue and 85% red light-emitting diode (LED); 3) BRFR L : 15% blue, 85% red, and 15.5 µmol·m −2 ·s −1 far-red (FR) LED; 4) BRFR H : 15% blue, 85% red, and 155 µmol·m −2 ·s −1 FR LED; 5) BG L R: 9% blue, 6% green, and 85% red LED; and 6) BG H R: 5% blue, 10% green, and 85% red LED. For all the light treatments, the total photosynthetic photon flux density ( PPFD ) was set at ≈330 µmol·m −2 ·s −1 under a 17-hour photoperiod, and the air temperature was ≈21 °C with 73% relative humidity (RH). At harvest, BR vs. FL increased plant height for all the tested species except arugula, and enlarged cotyledon area for kale and arugula. Adding high-intensity FR light to blue and red light (i.e., BRFR H ) further increased plant height for all species, and cotyledon area for mustard, but it did not affect the fresh or dry biomass for any species. Also, BRFR H vs. BR increased cotyledon greenness for green-leafed species (i.e., arugula, cabbage, and kale), and reduced cotyledon redness for red-leafed mustard. However, BG L R, BG H R, and BRFR L , compared with BR, did not affect plant height, cotyledon area, or fresh or dry biomass. These results suggest that the combination of 15% blue and 85% red LED light can potentially replace FL as the sole light source for indoor production of the tested microgreen species. Combining high-intensity FR light, rather than low-level (≤10%) green light, with blue and red light could be taken into consideration for the optimization of LED light spectral quality in microgreen production under environmental conditions similar to this experiment.
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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".