Controlled Environment Agriculture, Control Techniques, and Their Relationship with Essential Oil Extraction:: A Scientometric Analysis
Bibliographic record
Abstract
Controlled Environment Agriculture (CEA) has emerged as a fundamental strategy to optimize agricultural production through precise management of environmental variables such as temperature, humidity, light, and nutrients. This research presents a scientometric analysis of the scientific evolution in CEA during the period 2004-2025, with particular attention to its application in specialized crops for essential oil production. Through a systematic search in Web of Science and Scopus databases, 862 unique records were identified and analyzed following the PRISMA methodology. The results reveal exponential growth in research from 2015 onwards, with three distinct periods: initial growth (2004-2009, 23.27%), decline (2010-2014, -15.91%), and accelerated expansion (2015-2025, 50.41%). The United States leads scientific production with 339 publications (40.5%) and 5,998 citations, followed by Canada, India, Germany, and China. Predominant technologies include LED lighting systems, IoT sensors, automated monitoring, and vertical farming. The journal Frontiers in Plant Science positions itself as the main dissemination platform with 41 articles and an h-index of 246. International collaboration analysis evidences consolidated networks between European, Asian, and American countries, facilitating technology transfer. Although the specific relationship between CEA and essential oils remains underexplored, the consolidation of environmental control technologies and the growing demand for high-value products position this field as strategic for addressing global challenges of food security, climate change, and sustainability. It is concluded that CEA represents a research area with great projection, suggesting that future research should focus on the synergy between controlled systems and the production of secondary metabolites, essential oils, and bioactive compounds.
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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 | 0.026 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.090 | 0.143 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".