原文：

Dry Bean Dataset

Data Set 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.

Attribute 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)

译：

数据集信息：

本研究使用了七种不同类型的干豆，根据市场情况考虑了形状、形状、类型和结构等特征。为了获得统一的种子分类，开发了一个计算机视觉系统来区分七个具有相似特征的不同登记的干豆品种。对于分类模型，使用高分辨率相机拍摄了7种不同登记的干豆的13611粒图像。通过计算机视觉系统获得的大豆图像经过分割和特征提取阶段，共有16个特征；从晶粒中获得12个尺寸和4个形状。

属性信息：

1.）Area （A）：bean区域的面积及其边界内的像素数。

2）Perimeter （P）：豆周长定义为其边界的长度。

3.）Major axis length（L）：可以从豆子上绘制的最长直线的两端之间的距离。

4.）Minor axis length （l）：当垂直于主轴站立时，可以从豆子上画出的最长线。

5.）Aspect ratio（K）：定义L和L之间的关系。

6.）Eccentricity（Ec）：与区域具有相同力矩的椭圆的偏心率。

7.）Convex area（C）：可以包含豆种子区域的最小凸多边形中的像素数。

8.）Equivalent diameter（Ed）：具有与豆子面积相同面积的圆的直径。

9.）Extent（Ex）：边界框中的像素与bean区域的比率。

10.）Solidity ：也称为凸度。凸包中的像素与豆子中的像素之比。

11.）Roundness （R）：用以下公式计算：（4piA）/（P^2）

12.）Compactness （CO）：测量物体的圆度：Ed/L

13.）ShapeFactor1（SF1）

14.）ShapeFactor2（SF2）

15.）ShapeFactor3（SF3）

16.）ShapeFactor4（SF4）

17.）Class（塞克、巴本亚、孟买、卡利、德莫桑、霍罗斯和西拉）

示例数据：