Use of Lighting Technology in Controlled and Semi-Controlled Agriculture in Greenhouses and Protected Agriculture Systems—Part 1: Scientific and Bibliometric Analysis
Bibliographic record
Abstract
This paper examines the essential role of artificial lighting in protected agriculture, a crucial sector in addressing the increasing global food demand and the challenges posed by climate change. It explores how advanced lighting technologies, particularly LED systems, have revolutionized productivity and sustainability in greenhouses and indoor or urban farming systems. These technologies enable precise control over key factors influencing crop growth, optimizing both yield and resource efficiency. The methodology was based on a bibliometric analysis developed in four phases: collection of information in the scientific database Scopus, filtering and selection of relevant documents, quantitative and qualitative analysis of trends, and visualization of the results using tools such as VOSviewer. The study included scientific publications between 1974 and 2024, focusing on keywords related to greenhouse lighting technologies and protected agriculture systems. Key findings identified a significant increase in research over the last two decades, with countries such as the United States, Canada, the Netherlands, and China leading the way in scientific output. The main trends in artificial lighting for protected agriculture include the use of specific light spectra (particularly red and blue) to optimize photosynthesis and morphogenesis, as well as the integration of LED systems with digital sensors and controllers for enhanced precision. However, in developing countries such as Colombia, the adoption of these technologies remains in its early stages, presenting significant opportunities for implementation and expansion. Additionally, this bibliometric analysis provides a robust foundation for identifying key areas for improvement and guiding future research toward more sustainable and efficient agricultural practices.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.158 | 0.254 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".