Plant Companion Lighting System to Enhance Energy Efficient Agriculture in Remote Regions
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".