Datasets:

AbhinavManoj
/

Learning

Exception:    HfHubHTTPError
Message:      (Request ID: Root=1-6a672ee6-7392108922a05bce5356c8de;43e77268-f635-4a3f-b1d2-f84d54c8b887)

429 Too Many Requests: you have reached your 'api' rate limit.
Retry after 74 seconds (0/500 requests remaining in current 300s window).
Url: https://huggingface.co/api/datasets/AbhinavManoj/Learning/revision/719d072f6bb0705a30e4f8577d4139db4727471e.
We had to rate limit your IP (52.1.96.215). To continue using our service, create a HF account or login to your existing account, and make sure you pass a HF_TOKEN if you're using the API.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
                  builder = load_dataset_builder(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/src/services/worker/src/worker/utils.py", line 390, in safe_load_dataset_builder
                  dataset_module = dataset_module_factory(
                      repo_dir,
                      revision=revision,
                      download_config=download_config,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 608, in get_module
                  standalone_yaml_path = cached_path(
                      hf_dataset_url(self.name, config.REPOYAML_FILENAME, revision=self.commit_hash),
                      download_config=download_config,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 180, in cached_path
                  ).resolve_path(url_or_filename)
                    ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 339, in resolve_path
                  repo_and_revision_exist, err = self._repo_and_revision_exist(parsed.type, parsed.id, revision)
                                                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 252, in _repo_and_revision_exist
                  self._api.repo_info(
                  ~~~~~~~~~~~~~~~~~~~^
                      repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 88, in _inner_fn
                  return fn(*args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_api.py", line 3598, in repo_info
                  return method(
                      repo_id,
                  ...<4 lines>...
                      files_metadata=files_metadata,
                  )
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 88, in _inner_fn
                  return fn(*args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_api.py", line 3360, in dataset_info
                  hf_raise_for_status(r)
                  ~~~~~~~~~~~~~~~~~~~^^^
                File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 868, in hf_raise_for_status
                  raise _format(HfHubHTTPError, message, response) from e
              huggingface_hub.errors.HfHubHTTPError: (Request ID: Root=1-6a672ee6-7392108922a05bce5356c8de;43e77268-f635-4a3f-b1d2-f84d54c8b887)
              
              429 Too Many Requests: you have reached your 'api' rate limit.
              Retry after 74 seconds (0/500 requests remaining in current 300s window).
              Url: https://huggingface.co/api/datasets/AbhinavManoj/Learning/revision/719d072f6bb0705a30e4f8577d4139db4727471e.
              We had to rate limit your IP (52.1.96.215). To continue using our service, create a HF account or login to your existing account, and make sure you pass a HF_TOKEN if you're using the API.

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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

@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.

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