Intensity of Sole-source Light-emitting Diodes Affects Growth, Yield, and Quality of Brassicaceae Microgreens
Bibliographic record
Abstract
Indoor farming is an increasingly popular approach for growing leafy vegetables, and under this production system, artificial light provides the sole source (SS) of radiation for photosynthesis and light signaling. With newer horticultural light-emitting diodes (LEDs), growers have the ability to manipulate the lighting environment to achieve specific production goals. However, there is limited research on LED lighting specific to microgreen production, and available research shows that there is variability in how microgreens respond to their lighting environment. The present study examined the effects of SS light intensity (LI) on growth, yield, and quality of kale ( Brassica napus L. ‘Red Russian’), cabbage ( Brassica oleracea L.), arugula ( Eruca sativa L.), and mustard ( Brassica juncea L. ‘Ruby Streaks’) microgreens grown in a walk-in growth chamber. SS LEDs were used to provide six target photosynthetic photon flux density density ( PPFD ) treatments: 100, 200, 300, 400, 500, and 600 μmol·m −2 ·s −1 with a photon flux ratio of 15 blue: 85 red and a 16-hour photoperiod. As LI increased from 100 to 600 μmol·m −2 · s −1 , fresh weight (FW) increased by 0.59 kg·m −2 (36%), 0.70 kg·m −2 (56%), 0.71 kg·m −2 (76%), and 0.67 kg·m −2 (82%) for kale, cabbage, arugula, and mustard, respectively. Similarly, dry weight (DW) increased by 47 g·m −2 (65%), 45 g·m −2 (69%), 64 g·m −2 (122%), and 65 g·m −2 (145%) for kale, cabbage, arugula, and mustard, respectively, as LI increased from 100 to 600 μmol·m −2 · s −1 . Increasing LI decreased hypocotyl length and hue angle linearly in all genotypes. Saturation of cabbage and mustard decreased linearly by 18% and 36%, respectively, as LI increased from 100 to 600 μmol·m −2 ·s −1 . Growers can use the results of this study to optimize SS LI for their production systems, genotypes, and production goals.
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 | 0.000 |
| 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".