Artificial intelligence for crop yield prediction: a bibliometric analysis
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
The synergy between artificial intelligence (AI) and agricultural sciences has garnered substantial attention, especially in the realm of crop yield prediction.The present bibliometric analysis examines the worldwide research trends about the application of AI in predicting crop yields.The global literature on crop yield prediction using AI published between 1997 and 2022 is searched in the Scopus database.Five hundred and forty research articles were used to compile the analysis; they were located in the Scopus database and processed through the VOSviewer.Our research reveals a significant surge in scholarly publications, particularly focusing on countries including China, the United States, India and Canada.These research endeavours aim to apply AI methodologies for forecasting agricultural produce yields in tandem with developments in remote sensing technologies that facilitate more accurate yield predictions.These insights offer a valuable reference for researchers and illuminate potential directions for future investigations in the domain of AI-based crop yield prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.203 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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