Mapping the Evolution of Agriculture 4.0: A Bibliometric Analysis of Research Trends
Bibliographic record
Abstract
<title>Abstract</title> The term "agriculture 4.0" refers to integrating artificial intelligence, big data, cloud computing, the Internet of Things and advanced robotics into agriculture. The field of Agriculture 4.0 research has seen a surge in attention as sustainable agriculture has gained more prominence. This study concentrated on conducting a bibliometric analysis of Agriculture 4.0 and its growth. The Dimensions.ai data used in the study was produced using the search terms “Agriculture 4.0," "Smart Farming," "Farming 4.0," and "Digital Agriculture.” A comprehensive dataset consisting of 1,458 relevant documents has been identified, retrieved, and compiled into a CSV format for further analysis. The retrieved data was visualized and analyzed using suitable software. It was that the information and computing sciences field had the maximum number of publications on Agriculture 4.0 (1,015), followed by Agriculture, veterinary and food science (487). The majority of articles (1,074) addressed Sustainable Development Goal 2, which has hunger as its main focus. Based on co-authorship analysis, India, China, and the USA emerged as the leading nations both in impact and research volume, with other countries clustering around them. The University of Guelph, Wageningen University and Research and Anna University were the three organisations with respectively the most impact in terms of total citations. According to the sources' citation analyses, readers were more influenced by the "Computers and Electronics in Agriculture" publication when it came to Agriculture 4.0 research. The Agriculture 4.0 research involves many stakeholders; thus, a broad multidisciplinary approach is necessary. Hence, to solve the issue of Agriculture 4.0, multidisciplinary researchers ought to collaborate rather than act alone.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.146 | 0.192 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".