An Intelligent IoT-enabled Lighting System for Energy-efficient Crop Production
Bibliographic record
Abstract
In this paper, an intelligent lighting instrumentation and automation system is presented with the objective of achieving high energy-efficiency in greenhouse supplemental lighting based on the Internet of Things (IoT) technology. The system runs on a Raspbian operating system which interacts with wireless-enabled light emitting diode (LED) fixtures for plant growth, an online data server, and different light sensors including RGB and quantum sensors. The communication is achieved through RestFul API, UART, and I2C. The system is utilized to implement a feedback controller that automatically adjusts the light dimming levels and, in particular, the ratio of red and blue light intensities based on the plants’ needs. A series of experiments involving plant growth were conducted which indicate that the proposed system can achieve energy-savings up to 34%, when compared to a conventional time scheduling scheme. Additionally, the experiments demonstrate that the system can achieve a highly uniform light distribution under unpredictable natural lighting conditions while saving energy due to supplemental lighting.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".