How Machine Learning is Redefining Agricultural Sciences: An Approach to Predict Apple Crop Production of Kashmir Province
Bibliographic record
Abstract
One of the earliest and most popular (and effective) machine learning methods is decision trees.Different decision tree changes have been suggested and put into practice over time.Selecting the model that best fits the situation is essential while examining the data.Numerous classification and regression specialists have suggested ensemble tactics for tabular data as well as diverse methods for solving classification and regression issues.In this study, the raw historical apple crop dataset is transformed into a discrete dataset using the Gini Index and information gain.On the resulting discrete dataset, the decision tree algorithm is used.Information Gain is determined for each attribute, and the attribute with the highest information gain is used as the splitting node, which is then applied recursively.With an accuracy of 84.54%, the decision tree algorithm used predicts the apple yield in Kashmir province.Later, a comparison between the accuracy of decision tree and other algorithms has also been made and it was observed that the decision tree performed better in accuracy and other statistics than all the other implemented algorithms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".