Continuous lighting can improve yield and reduce energy costs while increasing or maintaining nutritional contents of microgreens
Bibliographic record
Abstract
Microgreens represent a fast growing segment of the edible greens industry. They are prized for their colour, texture, and flavour. Compared to their mature counterparts, microgreens have much higher antioxidant and nutrient content categorizing them as a functional food. However, current production practices in plant factories with artificial light are energy intensive. Specifically, the lack of sunlight within the indoor structure means all of the light must be provided via energy consuming light fixtures, which is energy intensive and costly. Plant growth is usually increased with the total amount of light provided to the plants - daily light integral (DLI). Long photoperiods of low intensity lighting (greater than 18h) providing the desired/target DLI can reduce the capital costs for light fixtures and electricity costs. This is achieved by moving the electricity use from peak daytime hours (high price) to off-peak hours (low price) during the night in regions with time-based pricing scheme and lowering the electricity use for air conditioning, if plant growth is not compromised. However, lighting with photoperiods longer than tolerance thresholds (species/cultivar specific) usually leads to plant stress/damage. Therefore, we investigated the effects of continuous 24h white light (CL) at two DLIs (~14 and 21 mol m -2 d -1 ) on plant growth, yield, and antioxidant content on 4 types of microgreens - amaranth, collard greens, green basil, and purple basil to see if it compromises microgreen production. It was found that amaranth and green basil had larger fresh biomass when grown under CL compared to 16h when the DLIs were the same. In addition, purple basil had higher biomass at higher DLI, but was unaffected by photoperiods. Plants grown under the CL treatments had higher energy-use-efficiencies for lighting (10-42%) than plants grown under the 16h photoperiods at the same DLI. Notably, the electricity cost per unit of fresh biomass ($ g -1 ) was reduced (8-38%) in all microgreens studied when plants were grown under CL lighting at the same DLIs. Amaranth and collard greens also had higher antioxidant content. Taken together, growing microgreens under CL can reduce electricity costs and increase yield while maintaining or improving nutritional content.
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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".