Dry Bean Dataset

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

Multiclass classification of dry beans using computer vision and machine learning techniques

By M. Koklu, Ilker Ali Özkan. 2020

Published in Computers and Electronics in Agriculture

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
AreaFeatureIntegerThe area of a bean zone and the number of pixels within its boundariespixelsno
PerimeterFeatureContinuousBean circumference is defined as the length of its border.no
MajorAxisLengthFeatureContinuousThe distance between the ends of the longest line that can be drawn from a beanno
MinorAxisLengthFeatureContinuousThe longest line that can be drawn from the bean while standing perpendicular to the main axisno
AspectRatioFeatureContinuousDefines the relationship between MajorAxisLength and MinorAxisLengthno
EccentricityFeatureContinuousEccentricity of the ellipse having the same moments as the regionno
ConvexAreaFeatureIntegerNumber of pixels in the smallest convex polygon that can contain the area of a bean seedno
EquivDiameterFeatureContinuousEquivalent diameter: The diameter of a circle having the same area as a bean seed areano
ExtentFeatureContinuousThe ratio of the pixels in the bounding box to the bean areano
SolidityFeatureContinuousAlso 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

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download
1 citations
530203 views

Keywords

image processing

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy