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kasunUdayanga/
Tea_Yield_Prediction_Model

Name: Tea_Yield_Prediction_Model
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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

kasunUdayanga/Tea_Yield_Prediction_Model