| --- |
| 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 |
|
|
| --- |
|
|
| # Dataset Card Contact |
|
|
| For questions or feedback, please open an issue on the Hugging Face repository. |
