Supplemental Lighting Orientation and Red-to-blue Ratio of Light-emitting Diodes for Greenhouse Tomato Production
Bibliographic record
Abstract
Current greenhouse supplemental lighting technology uses broad-spectrum high-pressure sodium lamps (HPS) that, despite being an excellent luminous source, are not the most efficient light source for plant production. Specific light frequencies in the 400- to 700-nm range have been shown to affect photosynthesis more directly than other wavelengths (especially in the red and blue ranges). Light-emitting diodes (LEDs) could diminish lighting costs as a result of their high efficiency, lower operating temperatures, and wavelength specificity. LEDs can be selected to target the wavelengths used by plants, enabling growers to customize the light produced, to enable maximum plant production and limit wavelengths that do not significantly impact plant growth. In our experiment, hydroponically grown tomato plants ( Solanum lycopersicum L.) were grown using a full factorial design with three light intensities (high: 135 μmol·m −2 ·s −1 , medium: 115 μmol·m −2 ·s −1 , and low: 100 μmol·m −2 ·s −1 ) at three red (661 nm) to blue (449 nm) ratio levels (5:1, 10:1, and 19:1). Secondary treatments for comparison were 100% HPS, 100% red LED light supplied from above the plant, 100% red LED light supplied below the plant, a 50%:50% LED:HPS mixture, and a control (no supplemental lighting). Both runs of the experiment lasted 120 days during the Summer–Fall 2011 and the Winter–Spring 2011–12. The highest biomass production (excluding fruit) occurred with the 19:1 ratio (red to blue) with increasing intensity resulting in more growth, whereas a higher fruit production was obtained using the 5:1 ratio. The highest marketable fruit production (fruit over 90 g) was obtained with the 50%:50% LED:HPS followed by 5:1 high and 19:1 high. Consistently the 5:1 high performed well in every category. LEDs have been shown to be superior in fruit production over HPS alone, and LEDs can improve tomato fruit production when mixed with HPS. LEDs provide a promising mechanism to enhance greenhouse artificial lighting 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".