An Energy-Efficient PAR-Based Horticultural Lighting System for Greenhouse Cultivation of Lettuce
Bibliographic record
Abstract
This paper presents an intelligent horticulture lighting and monitoring system to achieve energy-efficient supplemental lighting while maintaining the light quality and intensity at desired levels in the photosynthesis spectrum. Energy-efficiency is achieved through delivering only the required net light intensity, consisting of sunlight and supplemental LED light, using an intelligent controller that does not depend on the lighting system model. To this end, an online neural-network learning control system is developed, comprised of low-cost light sensors for measuring the photosynthetic photon flux density (PPFD), dimmable LED light fixtures, cameras, and internet-of-things (IoT)-enabled firmware used for crop monitoring and performance evaluation. Experiments performed in a research greenhouse facility on the lettuce crop are presented which indicate that the system can deliver the desired Daily Light Integrals (DLIs) to the plants in the presence of changing daylight conditions. The proposed method can thus deliver the exact amount of light to a specific crop based on the required light recipes during different growth phases. The control performance is further compared with a conventional on-off time-scheduling method in terms of plant health, growth, and energy requirements. The experiments indicate that the proposed solution can reduce energy consumption per unit dry mass of lettuce by 28% when compared to existing time-scheduling methods.
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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) | 0.001 | 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".