Statistical and Machine Learning Methods for Crop Yield Prediction in the Context of Precision Agriculture
Bibliographic record
Abstract
<p>It is of critical importance to understand the relationships between crop yield, soil properties, and topographic characteristics for agricultural management. This study's objective was to compare techniques to quantify the relationship between soil and topographic characteristics for predicting crop yield using high-resolution data and novel analytical techniques. The study was carried out across seventeen fields managed by a single cash cropping operation in Southwestern Ontario. Multiple linear regression, artificial neural networks, decision trees, and random forests were investigated to identify methods able to relate soil properties and crop yields on a point-by-point basis. Random forests were the most successful at predicting yield with an R-squared value of 0.93. Multiple linear regression was the least successful with an R-squared of 0.46. Machine learning techniques are often limited by their ability to extract meaningful relationships between variables. Thus, cross-validation techniques were applied to test the models and identify significant soil and topographic attributes when predicting yield.</p>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".