Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors
Neeraj Julka1, A.P Singh2
1Neeraj Julka, Department of Electronics and Communication Engineering, Sliet Longowal, India
2A.P Singh Department of Electronics and Communication Engineering, Sliet Longowal, India.

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9321-9328 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9499118419/2019©BEIESP | DOI: 10.35940/ijrte.D9499.118419

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Abstract: The present paper reports the development of an efficient machine vision system for automatic detection of foreign materials in wheat kernels using regional texture descriptors. In this system, the detection task is performed in two phases. These phases include features extraction phase followed by classification phase. New surface texture descriptors of wheat kernels are developed using Non-Shannon entropies in this work. These entropies are defined using intensity histograms of wheat and non-wheat regions in the given image. Such an attempt has not been made earlier. Experimental results on a database of about 2635 wheat and non-wheat components from 63 images confirm the effectiveness of the proposed method. The classification task is performed by the neural classifier in the proposed machine vision system. An accuracy of more than 98.5% is achieved using proposed system. However, the results of present investigations are quite promising.
Keywords: Wheat, Non-Wheat, Kernel, Texture, Machine Vision, Quality and Recognition.
Scope of the Article: Pattern Recognition.