Plant Companion Lighting System to Enhance Energy Efficient Agriculture in Remote Regions
Why this work is in the frame
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Bibliographic record
Abstract
This study presents the development and validation of the Plant Companion Lighting System (PCLS), a smart LED-based solution designed to enhance energy efficiency and optimize lighting conditions in Controlled Environment Agriculture (CEA). In remote and energy-constrained regions such as northern Canada, maintaining consistent light quality while minimizing energy consumption is critical for sustainable food production. The PCLS addresses this by integrating a motorized scissor-lift mechanism and ultrasonic sensors to automatically adjust the LED-to-canopy distance based on plant growth. Using Response Surface Methodology (RSM) and Central Composite Design (CCD), key lighting parameters—including distance, intensity, beam angle, and reflector configuration—were optimized to achieve stable Photosynthetic Photon Flux Density (PPFD) and reduce energy consumption. Regression models showed strong predictive power (R² > 0.99), with light intensity and distance identified as the most influential factors. Experimental validation confirmed that the system could maintain target PPFD (≈164 µmol/m²/s) within ±6% accuracy while reducing power usage by 80% compared to fixed systems. Although the prototype has a higher initial cost, cost analysis suggests mass production could lower this by 30%, making it economically viable long-term. The PCLS offers a scalable, sustainable lighting solution to support local food production in remote, light-sensitive agricultural environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it