Forecasting Ginger Harvest Yields: A Comparative Study of Double Exponential Smoothing and Long Short-Term Memory Models
Bibliographic record
Abstract
Ginger, a vital herbal commodity, experiences low yield rates, necessitating intensive cultivation and rigorous evaluation by farmers to ensure financial viability and alignment with market demands.This study was conducted to devise a harvest forecasting system that supports decision-making through minimal error rates by comparing double exponential smoothing (DES) and long short-term memory (LSTM) forecasting methods.The efficacy of these methods was assessed through a series of trials, analyzing data collected from 2015 to 2019, comprising 250 datasets.The evaluation focused on two primary metrics: the Mean Absolute Percentage Error (MAPE) and the Root MSE (RMSE), to determine the precision of forecast models.It was observed that the LSTM model outperformed the DES method, yielding a MAPE of 38.99% and an RMSE of 1244.85432, in contrast to the DES method which resulted in a MAPE of 43.49% and an RMSE of 12997.34261, at an alpha level of 0.4 and an optimal beta of 0.1.Given these findings, the LSTM model is recommended for the forecast of ginger yields due to its superior accuracy and lower standard error compared to the DES method.This comparative analysis underscores the importance of selecting appropriate forecasting models to enhance agricultural planning and productivity, particularly in crops with fluctuating yields such as ginger.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".