scholarly journals Detection of Foreign Materials in Wheat Kernels Using Regional Colour Descriptors

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.

scholarly journals Detection of Foreign Materials in Wheat Kernels using Boundary Descriptors

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.

scholarly journals Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

2019 ◽  
Vol 8 (4) ◽  
pp. 9321-9328
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Detection Task ◽  
Machine Vision System ◽  
Efficient Machine ◽  
Neural Classifier ◽  
Two Phases ◽  
Foreign Materials ◽  
The Given ◽  
Wheat Kernels

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.

scholarly journals Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2690 ◽  
Author(s):  
Jannat Yasmin ◽  
Santosh Lohumi ◽  
Mohammed Raju Ahmed ◽  
Lalit Mohan Kandpal ◽  
Mohammad Akbar Faqeerzada ◽  
...  
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Light Emitting Diode ◽  
Germination Rate ◽  
Classification Method ◽  
Tomato Seeds ◽  
Significant Difference ◽  
Foreign Materials ◽  
The One ◽  
One Class Classification

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

Classification of Single Cereal Grain Kernel Using Shape Parameters Based on Machine Vision

2012 ◽  
Vol 605-607 ◽  
pp. 2179-2182 ◽  
Author(s):  
Lan Lan Wu ◽  
Jie Wu ◽  
You Xian Wen ◽  
Li Rong Xiong ◽  
Yu Zheng
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Recognition Rate ◽  
Bayesian Classifier ◽  
Training Data ◽  
Japonica Rice ◽  
Shape Feature ◽  
Data Set ◽  
Cereal Grain ◽  
Length Width

The study was conducted to identify three types of non-touching grain kernels using a colour machine vision system. Images of individual cereal grain kernels were acquired using an camera. Shape feature was extracted from binary and edge images of cereal grain kernels obtained by iamge processing for classification. A total of 13 shape feature parameters, including region area, perimeter, length, width, the maximum radius, the smallest radius etc, were extracted from each kernel to use as input to the Bayesian classifier. Experimental results showed that the Bayesian classifier gave better classification with a calssificaiton accuracy of 99.67% for indica type rice, followed by 98.67% and 78.33% for japonica rice and glutinous rice using training set, respectively. The classification system was developed with Bayesian classifier that achieved an overall recognition rate of 92.22% with training data set and furthermore, a classification accuracy of 90% for the testing data set.

Machine vision system for quality inspection of beans

2020 ◽  
Vol 111 (11-12) ◽  
pp. 3421-3435
Author(s):  
Peterson Adriano Belan ◽  
Robson Aparecido Gomes de Macedo ◽  
Wonder Alexandre Luz Alves ◽  
José Carlos Curvelo Santana ◽  
Sidnei Alves Araújo
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Quality Inspection ◽  
Machine Vision System
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