Datasets:

Project-AgML
/

fruit_leaf_variety_classification

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
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Aegle marmelos
            '1': Black plum
            '2': Custard Apple
            '3': Guava
            '4': Jackfruit
            '5': Lotkon
            '6': Lychee
            '7': Mango
            '8': Plum
            '9': Star Fruit
  splits:
    - name: train
      num_bytes: 13373984193
      num_examples: 3173
  download_size: 10452397888
  dataset_size: 13373984193

Fruit Leaf Variety Classification

A dataset for variety classification of fruit leaves. The dataset contains 3,173 images across 10 classes: Aegle marmelos, Black plum, Custard Apple, Guava, Jackfruit, Lotkon, Lychee, Mango, Plum, Star Fruit.
Images per class:

Aegle marmelos: 336
Black plum: 304
Custard Apple: 304
Guava: 325
Jackfruit: 311
Lotkon: 306
Lychee: 312
Mango: 330
Plum: 302
Star Fruit: 343

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

Citation

@article{abedin2025comprehensive,
  title={Comprehensive data of 10 fruit leaf classes captured for agricultural AI applications},
  author={Abedin, Minhajul and Islam, Sujon and Sultana, Naznin},
  journal={Data in Brief},
  pages={111879},
  year={2025},
  publisher={Elsevier}
}

Abedin, Minhajul ; Islam, Md. Sujon ; Sultana, Dr. Naznin (2025), “Multi-Class Fruit Leaf Classification Dataset (10 Classes)”, Mendeley Data, V2, doi: 10.17632/4gxzx6h7gv.2