| --- |
| license: mit |
| library_name: sklearn |
| tags: |
| - tabular-regression |
| - agriculture |
| - yield-prediction |
| - coffee |
| - vietnam |
| - scikit-learn |
| model_format: skops |
| datasets: |
| - synthetic |
| --- |
| |
| # Coffee Yield Forecasting Model — Điện Biên, Vietnam |
|
|
| Predicts **coffee yield (tons/hectare)** for districts in Điện Biên province, |
| NW Vietnam, from growing-season weather features (rainfall timing by |
| phenological stage, frost/cold days, growing degree days, elevation). |
|
|
| Part of a broader [yield forecasting & harvest planning pipeline](https://github.com/) |
| (weather ingestion → feature engineering → model → harvest-window + risk alerts). |
|
|
| ## Model details |
|
|
| - **Algorithm:** Random Forest |
| - **Format:** [skops](https://skops.readthedocs.io/) (`model.skops`) — safe to |
| load without pickle's arbitrary-code-execution risk |
| - **Cross-validated MAE:** 0.115 t/ha (5-fold CV) |
| - **Cross-validated R²:** 0.471 |
| - **Training samples:** 80 (district-year observations) |
| - **Trained:** 2026-07-18 |
|
|
| ⚠️ **Important:** this model was trained on **synthetic, domain-informed |
| weather+yield data**, not real measured yield records (see the parent repo's |
| README for why, and how to retrain on real GSO/ICO data). Treat predictions |
| as illustrative until retrained on real district-level yield history. |
|
|
| ## Input features |
|
|
| ``` |
| dry_spell_rain_mm |
| flowering_rain_mm |
| fruitdev_rain_mm |
| ripening_rain_mm |
| fruitdev_rain_cv |
| frost_days_sensitive |
| mean_annual_temp_c |
| growing_degree_days |
| mean_sunshine_ripening |
| elevation_m |
| annual_rain_mm |
| ``` |
|
|
| All features are derived from daily weather (`tmax_c, tmin_c, tmean_c, |
| precip_mm, et0_mm, sunshine_hours`) aggregated over crop-specific |
| phenological windows — see `features.py` in the parent repo for exact |
| definitions (e.g. `flowering_rain_mm` = total rainfall during the |
| flowering month(s), `frost_days_sensitive` = count of nights below the |
| frost threshold during frost-sensitive months). |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import skops.io as sio |
| import pandas as pd |
| |
| model_path = hf_hub_download(repo_id="imaflower/dienbien-coffee-yield", filename="model.skops") |
| model = sio.load(model_path, trusted=sio.get_untrusted_types(file=model_path)) |
| |
| X = pd.DataFrame([{ |
| "dry_spell_rain_mm": 30, "flowering_rain_mm": 60, "fruitdev_rain_mm": 450, |
| "ripening_rain_mm": 120, "fruitdev_rain_cv": 1.8, "frost_days_sensitive": 1, |
| "mean_annual_temp_c": 21.5, "growing_degree_days": 4200, "mean_sunshine_ripening": 6.2, |
| "elevation_m": 900, "annual_rain_mm": 1600 |
| }]) |
| predicted_yield_t_ha = model.predict(X)[0] |
| ``` |
|
|
| ## Intended use & limitations |
|
|
| - Scoped to Điện Biên province district-level, seasonal (annual) yield |
| forecasting — not validated for other provinces/climates without |
| retraining. |
| - Does not account for management practices (fertilization, pest control, |
| variety-specific agronomy beyond the multiplier applied in the parent |
| pipeline's `predict.py`). |
| - Synthetic training data encodes plausible agronomic relationships but |
| is not a substitute for real yield statistics. |
|
|
