Energy efficiency optimization strategies for greenhouse‐based crop cultivation: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Worldwide, food scarcity is becoming a debatable concern among the scientific fraternity due to the increased populace, leading to decreased arable land. This has compelled us to explore various innovative and technological solutions, for example, large‐scale greenhouse farming, to meet the surging demand for field production. In this context, research efforts have been continually made by various scientists and researchers to explore more control strategies/algorithms for keeping the indoor climate comfortable and enhancing the greenhouse's energy effectiveness. Considering this, an initiative was made to summarize the documented research findings in the last decade focusing on energy‐efficient greenhouse‐based crop cultivation. The findings of some studies considering selective parametric conditions have been presented in graphs/tables for reader clarity and discussion. Initially, the studies on existing energy efficient strategies, parameters, monitoring systems, sensing networks, and control algorithms have been discussed. A state of the art review found that control strategies are essential in low‐energy greenhouses since they influence crop yield and cost. It was observed that advanced control algorithms and energy conservation in greenhouses received more attention due to wide spread application, high compatibility, low‐cost, and user‐friendly operations. In terms of future perspectives, it is anticipated that the development of machine learning, big data, and artificial intelligence, combining these technologies with traditional and advanced control strategies would lead to a revolution in the management of greenhouse energy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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