Leaf
Donated on 2/23/2014
This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
340
# Features
-
Dataset Information
Additional Information
For further details on this dataset and/or its attributes, please read the 'ReadMe.pdf' file included and/or consult the Master's Thesis 'Development of a System for Automatic Plant Species Recognition' available at http://hdl.handle.net/10216/67734.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 16
Additional Variable Information
1. Class (Species) 2. Specimen Number 3. Eccentricity 4. Aspect Ratio 5. Elongation 6. Solidity 7. Stochastic Convexity 8. Isoperimetric Factor 9. Maximal Indentation Depth 10. Lobedness 11. Average Intensity 12. Average Contrast 13. Smoothness 14. Third moment 15. Uniformity 16. Entropy
Dataset Files
| File | Size |
|---|---|
| ReadMe.pdf | 1.9 MB |
| RGB/15. Populus alba/iPAD2_C15_EX03.JPG | 261.5 KB |
| RGB/15. Populus alba/iPAD2_C15_EX04.JPG | 260.6 KB |
| RGB/19. Polypodium vulgare/iPAD2_C19_EX03.JPG | 258.3 KB |
| RGB/15. Populus alba/iPAD2_C15_EX02.JPG | 256.2 KB |
0 to 5 of 785
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset leaf = fetch_ucirepo(id=288) # data (as pandas dataframes) X = leaf.data.features y = leaf.data.targets # metadata print(leaf.metadata) # variable information print(leaf.variables)
Silva, P. & Maral, A. (2013). Leaf [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C53C78.
Creators
Pedro Silva
Andr Maral
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.