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Crop Production Prediction Dataset
Data Science and Analytics
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About
This dataset is designed for predicting crop yield in agricultural farms, based on environmental and agricultural factors. It aims to simulate the relationship between these factors and crop productivity, providing a valuable resource for exploring regression models, testing machine learning algorithms, and building predictive models for agricultural analysis. The dataset reflects realistic ranges for agricultural farms across different climates and farming practices.
Columns
- rainfall_mm: The average amount of rainfall (in millimetres) during the growing season. Values range from 500 mm to 2000 mm.
- soil_quality_index: A numeric rating of soil quality, on a scale from 1 (poor) to 10 (excellent). Values range from 1 to 10.
- farm_size_hectares: The size of the farm in hectares. Values range from 10 to 1000 hectares.
- sunlight_hours: The average daily hours of sunlight during the growing season. Values range from 4 to 12 hours per day.
- fertilizer_kg: The amount of fertilizer used per hectare (in kilograms). Values range from 100 to 3000 kg/hectare.
- crop_yield: The predicted yield of the crop in tons per hectare, calculated using a linear equation based on the input features. Values range from 46 to 628 tons per hectare.
Distribution
The dataset is typically provided in a CSV format, specifically
crop_yield_data.csv, with a file size of 65.15 kB. It contains 3000 data points across 6 distinct columns. All columns are 100% valid, with no mismatched or missing values. There is no expected update frequency, meaning this dataset is static.Usage
This dataset is well-suited for a variety of applications, including:
- Machine Learning: Training regression models to predict crop yield based on various environmental and agricultural inputs.
- Predictive Analytics: Developing models to estimate potential crop yields using weather forecasts, farm characteristics, and farming practices.
- Data Science Projects: Practising data preprocessing, feature engineering, and model evaluation techniques within the agricultural domain.
- Exploring regression models and gaining insights into farming efficiency.
Coverage
The dataset's values are designed to reflect realistic ranges for agricultural farms across diverse climates and farming practices. The features related to rainfall and sunlight hours pertain specifically to the growing season. No particular geographic, time range, or demographic notes are provided regarding data availability for specific groups or years beyond these general statements.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for:
- Researchers: Those studying agricultural productivity and environmental impacts on crops.
- Data Scientists: Individuals working on predictive modelling and machine learning in the agricultural sector.
- Agricultural Professionals: Experts seeking to experiment with crop prediction models or gain insights into farming efficiency and optimise practices.
- Students and practitioners engaged in data science projects focusing on agricultural analysis.
Dataset Name Suggestions
- Simulated Crop Yield Prediction Dataset
- Agricultural Productivity Factors
- Farm Yield Forecasting Data
- Crop Production Prediction Dataset
- Environmental Agriculture Factors
Attributes
Original Data Source: Crop Production Prediction Dataset
QUALITY