kasunUdayanga/srilanka_tea_yield

Sri Lankan Tea Yield Prediction Dataset 🍃🇱🇰

Dataset Description

This dataset contains 60,000 synthetic records representing tea plantation parameters in the context of Sri Lanka's hill country. It is designed for training Machine Learning models (Regression) to predict the tea leaf harvest (Yield) based on environmental factors and agricultural inputs.

This dataset simulates real-world conditions where rainfall and fertilizer positively impact yield, while extreme temperatures (too hot or too cold) negatively impact production.

• Total Rows: 60,000
• Format: CSV
• Context: Sri Lankan Tea Industry (Nuwara Eliya / Upcountry)
• Task: Regression (Predicting Yield_kg)

Data Fields

The dataset consists of the following columns:

Column Name	Data Type	Description	Unit
Rainfall_mm	Integer	Monthly average rainfall received.	Millimeters (mm)
Temperature_C	Float	Average temperature during the month.	Celsius (°C)
Fertilizer_kg	Integer	Amount of fertilizer applied to the plot.	Kilograms (kg)
Yield_kg	Integer	(Target Variable) Total tea leaf harvest.	Kilograms (kg)

Usage Example (Python)

You can load this dataset directly using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("kasunUdayanga/srilanka_tea_yield")
print(dataset['train'][0])