metadata
language: en
license: mit
tags:
- agriculture
- regression
- crop-yield
- tea
datasets:
- synthetic-tea-yield
model-index:
- name: tea-yield-predictor
results:
- task:
type: regression
name: Tea Yield Prediction
metrics:
- type: r2_score
value: 0.6448
- type: mae
value: 200.27
- type: rmse
value: 254.21
widget:
- example_title: Good Conditions Farm
rainfall_mm: 180
temperature_avg: 24
soil_ph: 5.5
fertilizer_kg_ha: 400
plant_age_years: 7
altitude_m: 1200
- example_title: Challenging Conditions Farm
rainfall_mm: 90
temperature_avg: 28
soil_ph: 4.8
fertilizer_kg_ha: 250
plant_age_years: 15
altitude_m: 800
Tea Yield Prediction Model 🌱
Model Description
This is a Linear Regression model that predicts tea crop yield (in kg/ha) using six key agricultural and environmental factors. The model was selected as the best performer among four algorithms tested, achieving an R² score of 0.6448.
Key Features
- ✅ Best performer among Linear Regression, Decision Tree, Random Forest, and SVR
- ✅ Simple & interpretable linear model
- ✅ Practical application for agricultural planning
- ✅ Ready-to-use with minimal dependencies
Model Performance
| Metric | Value | Description |
|---|---|---|
| R² Score | 0.6448 | Explains 64.48% of yield variance |
| MAE | 200.27 kg/ha | Average prediction error |
| RMSE | 254.21 kg/ha | Error with penalty for large mistakes |
| Training Samples | 47,536 | After preprocessing |
| Features | 6 | Agricultural/environmental factors |
Input Features
| Feature | Type | Range | Description |
|---|---|---|---|
rainfall_mm |
float | 50-220 mm | Monthly rainfall |
temperature_avg |
float | 18-30°C | Average temperature |
soil_ph |
float | 4.5-6.0 | Soil pH level |
fertilizer_kg_ha |
float | 200-500 | Fertilizer application rate |
plant_age_years |
float | 2-25 | Age of tea plants |
altitude_m |
float | 500-2000 | Farm elevation |
Quick Start
Installation
pip install scikit-learn pandas joblib