Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label Rice-Disease-Classification-Dataset@2f1db61320aa14a422107dbb0da9112f9b673286
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label Rice-Disease-Classification-Dataset@2f1db61320aa14a422107dbb0da9112f9b673286Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
🌾 Rice Disease Detection Dataset
Overview
The Rice Disease Detection Dataset is a curated collection of high-resolution images showcasing the health conditions of rice crops. It includes images across four distinct classes, enabling the development of machine learning models for detecting and diagnosing rice diseases effectively. This dataset is intended for agricultural researchers, machine learning enthusiasts, and AI practitioners working towards precision farming solutions.
Labels and Classes
The dataset consists of images categorized into four labels:
Brown Spot
- Caused by Bipolaris oryzae fungus.
- Symptoms: Oval-shaped brown spots on leaves, leading to reduced photosynthesis and crop yield.
Healthy
- Represents rice crops in optimal health conditions without visible signs of disease.
- Serves as a baseline for comparison with diseased samples.
Leaf Blast
- A fungal disease caused by Magnaporthe oryzae.
- Symptoms: Grey or white spindle-shaped lesions with brown margins on the leaves.
Neck Blast
- Another manifestation of Magnaporthe oryzae, attacking the neck of the panicle.
- Symptoms: Dark lesions around the neck that can lead to panicle breakage or grain loss.
Dataset Structure
- Images: High-quality images of rice crops under varied conditions and environments.
- Annotations: Each image is labeled with its respective class (
Brown Spot,Healthy,Leaf Blast,Neck Blast). - Total Size:
The dataset contains 4078 rows including all classes images and has a total size of 2 GB.
Applications
This dataset can be used for:
- Training convolutional neural networks (CNNs) for disease classification.
- Developing mobile or web-based applications for farmers to monitor crop(Rice) health.
- Conducting research on automated plant disease detection.
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