---
license: mit
task_categories:
- tabular-classification
language:
- en
tags:
- agriculture
- crop-recommendation
- machine-learning
- classification
- tabular
- soil
- climate
pretty_name: Crop Recommendation Dataset
size_categories:
- 1K<n<10K
---
# Crop Recommendation Dataset

## Dataset Details

### Dataset Description

The Crop Recommendation Dataset is a structured tabular dataset designed for machine learning models that recommend the most suitable crop based on soil nutrient composition and environmental conditions.

Each record consists of seven numerical input features representing soil nutrients and climatic conditions, along with a target label indicating the recommended crop.

The dataset is suitable for supervised classification tasks and can be used for benchmarking machine learning, deep learning, and explainable AI models in precision agriculture.

- **Curated by:** Abhinav Manoj
- **Funded by:** Self Project
- **Shared by:** Gagan Dev, Jyothis C R, Adthyan M C
- **Language(s):** English
- **License:** MIT

---

## Dataset Sources

- **Repository:** Hugging Face Dataset Repository
- **Paper:** Not Applicable
- **Demo:** Not Available

---

# Uses

## Direct Use

This dataset is intended for:

- Crop recommendation systems
- Precision agriculture
- Machine learning classification
- Deep learning research
- Agricultural analytics
- Educational purposes
- Explainable AI (XAI)
- Model benchmarking

Supported algorithms include:

- Logistic Regression
- Decision Tree
- Random Forest
- XGBoost
- CatBoost
- LightGBM
- Support Vector Machine
- K-Nearest Neighbors
- Artificial Neural Networks

---

## Out-of-Scope Use

This dataset should **not** be used for:

- Real-world farming decisions without expert validation
- Predicting crop yield
- Fertilizer recommendation
- Disease detection
- Weather forecasting
- Irrigation planning

---

# Dataset Structure

## Features

| Feature | Type | Description |
|----------|------|-------------|
| N | Integer | Nitrogen content in soil |
| P | Integer | Phosphorus content in soil |
| K | Integer | Potassium content in soil |
| temperature | Float | Temperature (°C) |
| humidity | Float | Relative humidity (%) |
| ph | Float | Soil pH value |
| rainfall | Float | Rainfall (mm) |
| label | String | Recommended crop |

### Target Variable

The **label** column contains the recommended crop category.

Examples include:

- Rice
- Maize
- Chickpea
- Kidney Beans
- Pigeon Peas
- Moth Beans
- Mung Bean
- Black Gram
- Lentil
- Pomegranate
- Banana
- Mango
- Grapes
- Watermelon
- Muskmelon
- Apple
- Orange
- Papaya
- Coconut
- Cotton
- Jute
- Coffee

---

## Dataset Splits

| Split | Description |
|--------|-------------|
| Full | Complete dataset |
| Train | 80% of the dataset used for model training |

---

# Dataset Creation

## Curation Rationale

The dataset was created to facilitate research and development of intelligent crop recommendation systems that leverage soil nutrient information and environmental conditions to predict suitable crops.

It provides a benchmark dataset for evaluating supervised learning algorithms in agriculture.

---

## Source Data

The dataset consists of structured agricultural measurements.

### Data Collection and Processing

The dataset contains numerical observations of:

- Soil Nitrogen
- Soil Phosphorus
- Soil Potassium
- Temperature
- Humidity
- Soil pH
- Rainfall

Standard preprocessing includes:

- Removal of missing values
- Consistent numerical formatting
- Structured tabular representation

---

### Who are the source data producers?

The original data was compiled for agricultural machine learning research.

If redistributed from a public source (such as Kaggle or UCI), users should also acknowledge the original dataset creators.

---

# Annotations

## Annotation Process

No manual annotations were added.

The target crop label is included as part of the original dataset.

---

## Who are the annotators?

Not Applicable.

---

## Personal and Sensitive Information

This dataset contains **no personal, private, or sensitive information**.

No personally identifiable information (PII) is included.

---

# Bias, Risks, and Limitations

Although useful for benchmarking, the dataset has several limitations.

- Limited geographic diversity
- Fixed environmental variables
- Does not include seasonal changes
- Does not account for local farming practices
- Cannot replace agricultural experts
- Limited feature set
- May not generalize globally

---

## Recommendations

Users should:

- Normalize numerical features before training.
- Evaluate models using cross-validation.
- Consider adding weather forecasts, soil texture, and satellite imagery for production systems.
- Validate predictions with agricultural experts before deployment.

---

# Citation

If you use this dataset in your work, please cite it as:

**BibTeX**

```bibtex
@dataset{crop_recommendation_dataset,
  title={Crop Recommendation Dataset},
  author={Abhinav Manoj},
  year={2026},
  publisher={Hugging Face}
}
```

**APA**

Abhinav Manoj. (2026). *Crop Recommendation Dataset*. Hugging Face.

---

# Glossary

**N** – Nitrogen concentration

**P** – Phosphorus concentration

**K** – Potassium concentration

**pH** – Soil acidity or alkalinity

**Humidity** – Relative humidity percentage

**Rainfall** – Rainfall in millimeters

---

# More Information

This dataset is intended for educational, research, and benchmarking purposes.

Researchers are encouraged to extend the dataset with additional environmental variables such as:

- Soil texture
- Soil moisture
- Elevation
- Weather forecasts
- Satellite imagery
- Historical crop yield

---

# Dataset Card Authors

Ambu

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# Dataset Card Contact

For questions or feedback, please open an issue on the Hugging Face repository.