Dry Bean
Donated on 9/13/2020
Images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
Dataset Characteristics
Multivariate
Subject Area
Biology
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
13611
# Features
16
Dataset Information
Additional Information
Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
Has Missing Values?
No
Introductory Paper
By M. Koklu, Ilker Ali Özkan. 2020
Published in Computers and Electronics in Agriculture
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Area | Feature | Integer | The area of a bean zone and the number of pixels within its boundaries | pixels | no |
| Perimeter | Feature | Continuous | Bean circumference is defined as the length of its border. | no | |
| MajorAxisLength | Feature | Continuous | The distance between the ends of the longest line that can be drawn from a bean | no | |
| MinorAxisLength | Feature | Continuous | The longest line that can be drawn from the bean while standing perpendicular to the main axis | no | |
| AspectRatio | Feature | Continuous | Defines the relationship between MajorAxisLength and MinorAxisLength | no | |
| Eccentricity | Feature | Continuous | Eccentricity of the ellipse having the same moments as the region | no | |
| ConvexArea | Feature | Integer | Number of pixels in the smallest convex polygon that can contain the area of a bean seed | no | |
| EquivDiameter | Feature | Continuous | Equivalent diameter: The diameter of a circle having the same area as a bean seed area | no | |
| Extent | Feature | Continuous | The ratio of the pixels in the bounding box to the bean area | no | |
| Solidity | Feature | Continuous | Also known as convexity. The ratio of the pixels in the convex shell to those found in beans. | no |
0 to 10 of 17
Additional Variable Information
1.) Area (A): The area of a bean zone and the number of pixels within its boundaries. 2.) Perimeter (P): Bean circumference is defined as the length of its border. 3.) Major axis length (L): The distance between the ends of the longest line that can be drawn from a bean. 4.) Minor axis length (l): The longest line that can be drawn from the bean while standing perpendicular to the main axis. 5.) Aspect ratio (K): Defines the relationship between L and l. 6.) Eccentricity (Ec): Eccentricity of the ellipse having the same moments as the region. 7.) Convex area (C): Number of pixels in the smallest convex polygon that can contain the area of a bean seed. 8.) Equivalent diameter (Ed): The diameter of a circle having the same area as a bean seed area. 9.) Extent (Ex): The ratio of the pixels in the bounding box to the bean area. 10.)Solidity (S): Also known as convexity. The ratio of the pixels in the convex shell to those found in beans. 11.)Roundness (R): Calculated with the following formula: (4piA)/(P^2) 12.)Compactness (CO): Measures the roundness of an object: Ed/L 13.)ShapeFactor1 (SF1) 14.)ShapeFactor2 (SF2) 15.)ShapeFactor3 (SF3) 16.)ShapeFactor4 (SF4) 17.)Class (Seker, Barbunya, Bombay, Cali, Dermosan, Horoz and Sira)
Class Labels
Seker, Barbunya, Bombay, Cali, Dermosan, Horoz, and Sira
Dataset Files
| File | Size |
|---|---|
| DryBeanDataset/Dry_Bean_Dataset.arff | 3.6 MB |
| DryBeanDataset/Dry_Bean_Dataset.xlsx | 2.9 MB |
| DryBeanDataset/Dry_Bean_Dataset.txt | 3.3 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset dry_bean = fetch_ucirepo(id=602) # data (as pandas dataframes) X = dry_bean.data.features y = dry_bean.data.targets # metadata print(dry_bean.metadata) # variable information print(dry_bean.variables)
Dry Bean [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C50S4B.
Keywords
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.