Importance of Big Data variables in Agriculture: A comprehensive literature review with a particular focus on variables
Bibliographic record
Abstract
The sharp increase of information in our life and in particular in agriculture leads to the development and new opportunities that did not exist a couple of decades ago. At the same time the ability to collect and analyze large volumes of data from remote sensing sources has revolutionized the way farmers make decisions and manage their agricultural activities. The great role in this process corresponds to Big Data, which is not only the data in itself, but a set of strategies for analysis that allow you to benefit from owning it. The goal of this study is to review published articles on big data in agriculture throughout 2017–2023. In line with this goal, we have collected (using Science direct database), reviewed, and analyzed 60 papers published during within this period of time. Our results revealed an increasing number of big data studies during last years, with authors from India, the USA and China dominating in the published outcomes (42 % of total), followed by authors from Australia, Canada and the Netherlands. Another key finding is that from all existing variables for big data only five are really important and there is no need to expand these parameters. It is more optimal to use main variables (volume, velocity, variety, veracity and value) for an in-depth and detailed description of the state of the data. Results also revealed different big data sources and techniques for mail areas of data application.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".