Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

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.

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Detection of Foreign Materials in Wheat Kernels using Boundary Descriptors

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f4354.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1001-1009

Keyword(s):

Machine Vision ◽

Vision System ◽

Recognition Task ◽

Machine Vision System ◽

On Line ◽

Neural Classifier ◽

Foreign Materials ◽

Impurity Elements ◽

Wheat Kernels ◽

Regional Boundary

The present paper reports the development of a machine vision system for quality inspection of wheat using kernel shape attribute. Shape attribute of agricultural products including wheat kernels is extremely difficult to quantify in digital computation. A new method is proposed in the present work to quantify shape attribute of wheat kernels using regional boundary descriptors. Recognition task in the proposed machine vision system is carried out by neural classifier trained with Levenberg-Marquardt (LM) based supervised learning. Proposed neural classifier was executed using feed-forward backpropagation based three layer artificial neural network. Experimental results indicate more than 98.1% overall average classification accuracy for the involved wheat and impurity elements in the present work. The results of present study are quite promising and the proposed machine vision system has potential future for on-line inspection of agriculture produce in real time environment.

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Detection of Foreign Materials in Wheat Kernels Using Regional Colour Descriptors

Journal of Engineering Research ◽

10.36909/jer.10371 ◽

2021 ◽

Author(s):

Neeraj Julka ◽

◽

Singh A. P ◽

Keyword(s):

Machine Vision ◽

Learning Algorithm ◽

Vision System ◽

Recognition Rate ◽

Quality Inspection ◽

Wheat Kernel ◽

New Type ◽

Neural Classifier ◽

Foreign Materials ◽

Wheat Kernels

Present paper reports the development of an automated machine vision system for detection of foreign materials in wheat kernels using regional color descriptors. The said system was executed in the form of an integrated flowing pipeline after having proper choice of different possible alternatives at different stages of image processing. A new type of surface colour descriptor is also proposed in this work to define wheat kernel uniquely. The fifteen-element colour descriptor is executed after having thorough comparison of six different colour spaces, each having 72 separate quantifiable components. The fifteen elements of the proposed colour-descriptor, extracted from each segmented region of the sample image, are concatenated in the form of an input to the neural classifier. The neural classifier is trained with Levenberg-Marquardt (LM) learning algorithm to achieve extremely fast convergence. The recognition rate of the executed classifier is found to be more than 99.2% for detection of impurity in unconnected wheat kernels. The results of present investigations are quite promising. The proposed pipeline has potential future in the field of machine vision based quality inspection of wheat and other cereal grains.

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Multifunctional stereoscopic machine vision system with multiple 3D outputs

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.2.sda-142 ◽

2020 ◽

Keyword(s):

Machine Vision ◽

International Symposium ◽

Fast Track ◽

Vision System ◽

Machine Vision System ◽

Stereoscopic Displays ◽

Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

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A real-time machine-vision system for monitoring the textile raising process

Computers in Industry ◽

10.1016/j.compind.2005.05.010 ◽

2005 ◽

Vol 56 (8-9) ◽

pp. 831-842 ◽

Cited By ~ 22

Author(s):

Monica Carfagni ◽

Rocco Furferi ◽

Lapo Governi

Keyword(s):

Machine Vision ◽

Real Time ◽

Vision System ◽

Machine Vision System ◽

Time Machine

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Application of Machine Vision System in the Precision Inspection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.546-547.1382 ◽

2012 ◽

Vol 546-547 ◽

pp. 1382-1386

Author(s):

Yin Xia Liu ◽

Ping Zhou

Keyword(s):

Machine Vision ◽

Vision System ◽

Machine Vision System ◽

Technical Problems ◽

Application Program ◽

Precision Inspection ◽

Machine Parts

In order to promote the application and development of machine vision, The paper introduces the components of a machine vision system、common lighting technique and machine vision process. And the key technical problems are also briefly discussed in the application. A reference idea for application program of testing the quality of the machine parts is offered.

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