Wheat class identification using monochrome images

Manickavasagan, Annamalai; Sathya, G.; Jayas, Digvir S.; White, N. D. G.

doi:10.1016/j.jcs.2007.06.008

Cited by 48 publications

(21 citation statements)

References 8 publications

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“…The classification accuracies of wheat classes independent of moisture levels using the LDA and QDA models are given in Table 1. Classification accuracies were 89.8 and 85.4% for identifying wheat classes independent of moisture levels using the LDA and QDA, respectively, by analyzing grayscale images of bulk samples [9]. Secondly, moisture levels of wheat classes were represented approximately evenly throughout the analysis range.…”

Section: Identification Of Wheat Classes Independent Of Moisture Levementioning

confidence: 99%

“…The highest classification accuracy of 98.8% for classifying single wheat kernels was based on colour using a divergence feature selection method [30]. Overall classification accuracies were 95-96.3 and 92-94.4% for identifying wheat classes at different moisture levels using the LDA and QDA, respectively, by analyzing monochrome images of bulk samples [9]. The QDA had classification accuracies of 95 and 91% with bootstrap and leaveone-out validation methods, respectively, for pairwise classification within red and white wheat classes using thermal images of bulk samples [10].…”

Section: Identification Of Wheat Classes At Different Moisture Levelsmentioning

confidence: 99%

“…Wheat classes and varieties were identified using image analysis with hardness measurements [8]. Western Canadian wheat classes were identified at different moisture levels using monochrome images of bulk samples [9]. Thermal imaging, where visible images are generated from invisible radiation patterns of samples, was also used to classify wheat [10].…”

Section: Introductionmentioning

confidence: 99%

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Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

Sivakumar

Jayas

Paliwal

et al. 2010

Sens. & Instrumen. Food Qual.

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Wheat classes at different moisture levels need to be identified to accurately segregate, properly dry, and safely store before processing. This paper introduces a new method using a near infrared (NIR) hyperspectral imaging system (960-1,700 nm) to identify five western Canadian wheat classes (Canada Western Red Spring (CWRS), Canada Western Extra Strong (CWES), Canada Western Red Winter (CWRW), Canada Western Soft White Spring (CWSWS), and Canada Western Hard White Spring (CWHWS)) and moisture levels, independent of each other. The objectives of this research also included identification of each wheat class at specific moisture levels of 12, 14, 16, 18, and 20%. Bulk samples of wheat were scanned in the 960-1,700 nm wavelength region at 10 nm intervals using an Indium Gallium Arsenide (InGaAs) NIR camera. Spectral feature data sets were developed by calculating relative reflectance intensities of the scanned images. Principal components analysis was used to generate scores images and loadings plots. The NIR wavelengths in the region of 1,260-1,360 nm were important based on the loadings plot of first principal component. In statistical classification, the linear and quadratic discriminant analyses were used to classify wheat classes giving accuracies of 61-97 and 82-99%, respectively, independent of moisture contents. It was also found that the linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) could classify moisture contents with classification accuracies of 89-91 and 91-99%, respectively, independent of wheat classes. Once wheat classes were identified, classification accuracies of 90-100 and 72-99% were observed using LDA and QDA, respectively, when identifying specific moisture levels. Spectral features at key wavelengths of 1,060, 1,090, 1,340, and 1,450 nm were ranked at top in classifying wheat classes with different moisture contents. This work shows that hyperspectral imaging techniques can be used for rapidly identifying the wheat classes even at varying moisture levels.

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Section: Identification Of Wheat Classes Independent Of Moisture Levementioning

confidence: 99%

Section: Identification Of Wheat Classes At Different Moisture Levelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

Sivakumar

Jayas

Paliwal

et al. 2010

Sens. & Instrumen. Food Qual.

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show abstract

“…In another study, Farahani tried to determine the best potential morphologic features to classify 5 different types of durum wheat [7]. Manickavasagan et al tried to measure the ability of a machine vision system with a monochrome camera to classify the different types of western Canadian wheat types by using bulk sample analysis [8]. Williams et al have evaluated two different digital image analysis (DIA) approaches to quantifying wheat seed shape for exploring trait correlations and QTL (Quantitative Trait Loci) mapping [9].…”

Section: Introductionmentioning

confidence: 99%

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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“…Except for few studies (Boniecki et al, 2012b;Kujawa et al, 2013) the literature does not provide any publications about the application of these methods to investigate the composting processes. However, the methods of computer image analysis proved to be an adequate and credible tool supporting the classification and assessment of the state of different biomaterials (Delwiche et al, 2013;Jayas, 1999, 2000a,b,c,d;Manickavasagan et al, 2008;Rodríguez-Pulido et al, 2012;Szczypiń ski and Zapotoczny, 2012). They are often used in combination with neural modelling.…”

Section: Introductionmentioning

confidence: 99%

Neural image analysis for maturity classification of sewage sludge composted with maize straw

Kujawa

Nowakowski

Tomczak³

et al. 2014

Computers and Electronics in Agriculture

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Wheat class identification using monochrome images

Cited by 48 publications

References 8 publications

Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples

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

Neural image analysis for maturity classification of sewage sludge composted with maize straw

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