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Wheat kernels Data Set
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Abstract: Measurements of morphological descriptors of wheat kernels from Punjab State. A machine Learning based technique was used to extract 15 features, all are real valued attributes

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Neeraj Julka, Research Scholar, Department of ECE, SLIET Longowal

Data Set Information:

The examined group comprised kernels of wheat : PBW-550U randomly selected for the experiment. High Quality Manual Visualisation of the external Kernel. It is non-destructive technique.The images were recorded using Basler Sca-17fc industrial graded color camera. The dataset can be used for tasks of classification.

Attribute Information:

Area, Major Axis Length, Minor Axis Length, Perimeter, Length, Width, Thinness Ratio, Aspect Ratio, Rectangular Aspect ratio, Area Ratio, Distance Max,Distance Min, Distance Ratio, Std Deviation,Mean

Relevant Papers:

N. Julka and A. P. Singh, “Machine vision based detection of foreign material in wheat Kernels using shape and size descriptors,” published in International Journal of Advanced Science and Technology,, vol. 28, no. 20, pp. 736–749, 2019, ISSN : 2207-6360

Citation Request:

Special thanks to Department of Electronics and Communication Engineering, Sant Longowal Institute of Engineering and Technology (SLIET), Longowal-148106, Sangrur, Punjab, India, for extending excellent technical support in the form of Lab Facilities in Machine Vision and Motion Control Lab to conclude the present investigations.

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