Datasets:
File size: 1,848 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '2'
'1': '3'
'2': '4'
'3': '5'
- name: image_source
dtype: string
splits:
- name: train
num_bytes: 48420852
num_examples: 2785
download_size: 75551129
dataset_size: 48420852
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Paddynet Lcc Classification
This dataset features field images of paddy crops collected across multiple locations in Bangladesh during the rice growing season (mid-April to late June). Images were captured using handheld RGB cameras on consumer smartphones (Nokia 3 and Samsung S8) and include a mix of real field observations and synthetic augmentations. The dataset contains 2,785 images across 4 classes: 2, 3, 4, 5.
Images per class:
- 2: 692
- 3: 1,103
- 4: 513
- 5: 477
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{siddique2023paddynet,
title={Paddynet: An organized dataset of paddy leaves for a smart fertilizer recommendation system},
author={Siddique, Md. Moradul and Islam, Torikul and Tusher, Yeasir Arefin and Ema, Romana Rahman and Adnan, Md. Nasim and Galib, Syed Md.},
journal={Data in Brief},
volume={50},
pages={109516},
year={2023},
publisher={Elsevier}
}
```
Siddique, Md Moradul; Islam, Torikul; Tusher, Yeasir Arefin ; Md. Galib, Syed (2023), “PaddyNet: An Organized Dataset of Paddy Leaves for a Smart Fertilizer Recommendation System ”, Mendeley Data, V2, doi: 10.17632/ksz57tk5vc.2
*This dataset was reformatted from its original format to match HuggingFace standards.*
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