Balancing Yield and Sustainability: A Comparative Analysis of Supplemental Lighting in Commercial-Scale Cucumber Cultivation
Bibliographic record
Abstract
Lighting is a fundamental driver of plant productivity in controlled-environment agriculture (CEA), directly affecting physiological processes, resource efficiency, and sustainability. This study evaluates the effects of distinct lighting systems, industrial Light-Emitting Diodes (iLEDs), horticultural LEDs (hLEDs), high-pressure sodium (HPS) lamps, and controls (no supplemental light), each providing unique light spectra, on cucumber (Cucumis sativus L.) growth, physiology, and environmental impact under a controlled light intensity of 250 µmol m−2 s−1 in a commercial CEA setup. The results indicated that iLEDs enhance intrinsic water use efficiency (35.65 µmol CO2/mol H2O) and reduce transpiration, reflecting superior physiological resource use. Electrophysiological measurements indicated significantly more stable stress responses in plants subjected to iLEDs and hLEDs as compared to HPS and control treatments, indicating the effectiveness of LED light spectra in mitigating stress-related physiological impacts. Furthermore, compact growth and shorter stem internodes were observed under iLEDs as well as hLEDs, highlighting the spectral effects on photomorphogenesis, likely caused by a balanced light spectrum. HPS lighting achieved the highest yield (42.86 kg m−2) but at a significant environmental cost, with 342.65 kg CO2e m−2 emissions compared to 204.29 kg CO2e m−2 for iLEDs, with competitive yield of 38.84 kg m−2. Economic analysis revealed that iLEDs also offered the most cost-effective solution due to lower energy consumption and extended lifespan. This study focused on the interaction between light spectra, photosynthetic performance, stress resilience, and resource efficiency, advancing sustainable strategies for energy-efficient food production in CEA 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".