Enhancing Agricultural Decision-Making: A Hybrid Machine Learning Approach to Predicting Leaf Wetness Duration in Telangana
Bibliographic record
Abstract
Accurate estimation of Leaf Wetness Duration (LWD) is essential for effective agricultural management, particularly in optimizing spray programs and managing diseases influenced by microclimatic conditions. This study presents a novel approach by integrating Random Forest Regression (RFR) and Support Vector Regression (SVR) through a stacking ensemble technique to predict LWD. Using weather data from Telangana, India—comprising temperature, humidity, rainfall, and solar radiation—extensive preprocessing and feature engineering are applied before tuning the RFR and SVR models. A linear regression meta-regressor then combines the predictions from these models, improving LWD forecast accuracy. Performance is evaluated through cross-validation using metrics such as RMSE, MAE, and R2. Preliminary results demonstrate that the stacked model outperforms traditional methods, providing more precise LWD predictions and supporting sustainable and productive farming practices in the region.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".