Utilizing Artificial Intelligence to Forecast Market Trends and Enhance Supply Chain Strategies in Agriculture
Bibliographic record
Abstract
This investigation elucidates the transformative role of Artificial Intelligence (AI) in revolutionizing agriculture across North America, with a focus on the United States, Canada, and Mexico.By leveraging advanced AI methodologies, particularly Gated Re-current Units (GRUs)-a sophisticated variant of Recurrent Neural Networks (RNNs)-this study addresses pressing agricultural challenges, including market volatility, demand forecasting, and price fluctuations.GRUs were selected for their efficacy in handling sequential data, mitigating issues like vanishing gradients, and delivering precise predic-tions for crops such as maize and potatoes.Performance metrics, including Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), demonstrate exceptional accuracy, notably for maize yields in Mexico (RMSE: 1224) and potato yields in Canada (RMSE: 23145).Utilizing comprehensive crop yield datasets, this research underscores AI's ability to provide actionable insights, enabling farmers, suppliers, and distributors to optimize inventory, reduce waste, and strategically time market entry.The study also explores market scenario simulations, adoption barriers such as data accessibility, and the need for stakeholder training.Through detailed case studies, we illustrate AI's capacity to fortify agricultural supply chains, enhancing adaptability to dynamic market conditions.These findings affirm AI's potential to foster resilience, efficiency, and profitability, offering stakeholders critical tools for resource management and long-term strategic planning.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".