Project-AgML/PaddyNet_LCC_classification

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

@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.