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A subsample of the Koklu & Ozkan (2020) dry beans dataset produced by imaging a total of 13,611 grains from 7 varieties of dry beans. The original dataset contains 13,611 observations, but here we include a random subsample of 1000.

Usage

minibeans

Format

minibeans

A data frame with 1000 rows and 17 columns:

Area

The area of a bean zone and the number of pixels within its boundaries.

Perimeter

Bean circumference is defined as the length of its border.

Major axis length

The distance between the ends of the longest line that can be drawn from a bean.

Minor axis length

The longest line that can be drawn from the bean while standing perpendicular to the main axis.

Aspect ratio

Defines the relationship between L and l.

Eccentricity

Eccentricity of the ellipse having the same moments as the region.

Convex area

Number of pixels in the smallest convex polygon that can contain the area of a bean seed.

Equivalent diameter

The diameter of a circle having the same area as a bean seed area.

Extent

The ratio of the pixels in the bounding box to the bean area.

Solidity

Also known as convexity. The ratio of the pixels in the convex shell to those found in beans.

Roundness

Calculated with the following formula: (4piA)/(P^2).

Compactness

Measures the roundness of an object: Ed/L.

ShapeFactor1

Shape factor 1.

ShapeFactor2

Shape factor 2.

ShapeFactor3

Shape factor 3.

ShapeFactor4

Shape factor 4.

Class

Seker, Barbunya, Bombay, Cali, Dermosan, Horoz, and Sira.

Source

Koklu, M, and IA Ozkan. 2020. Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques. Computers and Electronics in Agriculture, 174: 105507. doi: 10.1016/j.compag.2020.105507, https://doi.org/10.24432/C50S4B