Research on Crop Planning Based on Data Mining and Genetic Algorithms
Bibliographic record
Abstract
Data mining techniques can be employed to extract information that is not immediately apparent from large amounts of data, and to construct predictive models based on this extracted information. These models can then be used as a basis for decision-making. In order to expand the scope of its application, this paper combines data mining with genetic algorithms and orthogonal experiments and applies it to the optimization of planting decisions. In particular, this study initially gathered and structured data on planting conditions, crop sales, per-mu yields, planting costs, and selling prices in a village through data mining techniques and subsequently analyzed the intrinsic relationships between these variables. On this basis, this paper constructs a planning function with the goal of maximizing profits and uses genetic algorithms to solve optimization problems. Overall, this study has successfully applied data mining techniques to practical planting decision-making problems, which not only has strong practicality, but also provides a reference for solving other complex planning problems. In the future, further exploration of the integration of additional optimization algorithms into the data-driven decision-making analysis framework may yield more comprehensive solutions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".