Project-AgML/banana_leaf_nutrient_classification

Banana Leaf Nutrient Classification

A dataset for classification of Banana leaf nutrient deficiencies. The dataset contains raw and augmented versions.
The raw dataset contains 5,348 images.
Images per class:

• Boron: 173
• Calcium: 794
• Healthy: 1,584
• Iron: 151
• Magnesium: 288
• Manganese: 24
• Potassium: 381
• Sulphur: 1,240
• Zinc: 713

The augmented dataset contains 12,747 images.
Images per class:

• Boron: 1,384
• Calcium: 1,588
• Healthy: 1,586
• Iron: 1,359
• Magnesium: 1,440
• Manganese: 1,200
• Potassium: 1,524
• Sulphur: 1,240
• Zinc: 1,426

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{sunitha2023fully,
  title={A fully labelled image dataset of banana leaves deficient in nutrients},
  author={Sunitha, P and Uma, B and Channakeshava, S and Suresh Babu, CS},
  journal={Data in Brief},
  volume={48},
  pages={109155},
  year={2023},
  publisher={Elsevier}
}

p, sunitha; B, Uma; Babu C S, Suresh ; S, Channakeshava (2025), “Images of Nutrient Deficient Banana Plant Leaves”, Mendeley Data, V2, doi: 10.17632/7vpdrbdkd4.2