Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach

Ebrahimi, Ebrahim; Mollazade, Kaveh; Babaei, Sirwan

doi:10.1016/j.measurement.2014.05.003

Cited by 50 publications

(28 citation statements)

References 12 publications

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“…Moreover, β > 1 was suggested in the study conducted by Niknam et al [81]. Atashpaz-Gargari and Lucas [49] introduced θ = π/4 in the initial version of ICA and this value was successfully implemented by Marto et al [55] and Ebrahimi et al [82] in different fields. In the case of ζ, values of 0.1 by Atashpaz-Gargari and Lucas [49], 0.05 by Taghavifar et al [22], and 0.02 by Hajihassani et al [5] have been suggested.…”

Section: Ica Parametermentioning

confidence: 99%

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Armaghani

Hasanipanah

Mohamad

2015

Engineering with Computers

126

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including coefficient of determination (R 2 ), root mean square error and value account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models were selected among all constructed models. It was found that the ICA-ANN approach can provide higher performance capacity in predicting AOp compared to other predictive methods.

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Section: Ica Parametermentioning

confidence: 99%

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Armaghani

Hasanipanah

Mohamad

2015

Engineering with Computers

126

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“…Ebrahim Ebrahimi et al [60] introduce a machine vision based approach as a primary step for fabricating an automatic wheat purity determination and grading device. A new algorithm that combines Imperialist Competitive Algorithm (ICA) and Artificial Neural Networks (ANNs) has been used for two purposes: to find the best characteristic parameters set and to create robust classification models.…”

Section: Weed and Impuritesmentioning

confidence: 99%

Applications of Image Processing for Grading Agriculture products

Raj¹,

Swaminarayan²

2015

IJRITCC

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Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products.

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“…It is important for the food industry to provide quality goods which includes wheat grains. After its harvest, wheat seeds go through many procedures from its separation from chaff to its packaging and they are stored in warehouses to be sold at specified intervals [1]. The inspection and the classification of good quality wheat grains can be done by manually through a series of instrumental or chemical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Through the last years, many researchers have evaluated machine vision and image processing techniques if they really meet the expectations for the inspection and classification of the quality of wheat. There have been many studies about the determination of the properties of single wheat seed, separation of one type wheat from another or identification of damaged wheat seeds, but there have not been many researches about separating the wheat seeds from non-wheat seeds [1]. In a study conducted by Pourreza et al, nine different wheat classes growing in Iran have been classified according to their textural properties extracted from Gray Level, GLCM (Gray Level Co-occurrence Matrix), GLRM (Gray Level Run-length Matrix), LBP (Local Binary Pattern), LSP (Local Similarity Pattern) and LSN (Local Similarity Numbers) matrices and classified using LDA (Linear Discriminate Analysis [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, there is also a small number of studies about purification of wheat from its chaff and other impurities. One of these studies is made by Ebrahimi et al to propose a machine vision automatic grading system which separates the wheat from the impurities within [1]. The objective of another study conducted by Paliwal et al is to develop an algorithm which classifies 5 different types of Canadian wheats and also differentiates the wheats from nonwheat materials [10].…”

Section: Introductionmentioning

confidence: 99%

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Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties

Kaya¹,

Sarıtaş²,

Özkan³

2016

ijisae

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Abstract:Wheat is the main food source of the humankind. After its harvest, it goes through many procedures from its separation from chaff to its packaging. With the development in technology, many of these procedures are realized with automatic systems which saves the manufacturer the cost of labour, time and provides the customer with more quality food. One of the main concerns of quality food production is to provide a customer with the product in its purest form which means the product must be separated from all foreign matters. In this study, type-1252 durum wheat seeds have been separated from junk using the morphological properties of wheat seeds through the uncompressed video image taken with the camera Prosilica GT2000c. The main references for the quality measurement of wheat seeds are the shape and the dimensions of a wheat seed. Aiming for high quality wheat grain storage with no junk, this article has adopted various image processing techniques from image preprocessing to feature extraction. The image processing has been realized in a computer environment and the results show that the image processing is successful and the detection of wheat seeds from junk was accurate.

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Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach

Cited by 50 publications

References 12 publications

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

Applications of Image Processing for Grading Agriculture products

Separation of Wheat Seeds from Junk in a Dynamic System Using Morphological Properties

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