Shaping Kale Morphology and Physiology Using Precision LED Light Recipes
Bibliographic record
Abstract
Summary Light serves as a fundamental factor in plant development, both as an energy source and as an environmental cue. With the advent of light-emitting diode (LED) technology, light can be precisely manipulated to influence key plant traits. Here, we assess effects of light intensity and spectral composition on the growth and physiology of Kale ( Brassica oleracea ). Kale is known for its phenotypic plasticity and nutritional composition, making it a crop well-suited for indoor cultivation either as microgreens or as large leafy plants. Here, we employ a combination of advanced phenotyping, computer vision, gas chromatography-mass spectrometry (GC-MS) metabolomics, and liquid chromatography-mass spectrometry (LC-MS)-based quantitative proteomics to characterize the molecular changes that underpin light-dictated differences in the growth and metabolism of two different commercially grown kale cultivars under different light intensities and spectral compositions. We identify time-of-day and cultivar-specific light intensity and spectral composition-induced changes related to growth, shade avoidance, photosynthesis and several aspects of nutritional composition, including amino acids, glucosinolates and carotenoids. Our results offer a key resource to the plant community and demonstrate the translational potential of light manipulation in tailoring kale growth and nutritional content for enhanced crop productivity and/or health benefits, while simultaneously offering a more cost-effective solution for contemporary agricultural challenges. Significance Statement The effects of light intensity and spectral composition differentially affect the diel molecular responses of Kale ( Brassica oleracea ). Our results demonstrate the translational potential of light manipulation in tailoring plant growth and nutritional content for enhanced crop productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".