Comparison of illuminations to identify wheat classes using monochrome images

Manickavasagan, Annamalai; Sathya, G.; Jayas, D. S.

doi:10.1016/j.compag.2008.03.002

Cited by 30 publications

(11 citation statements)

References 15 publications

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“…, 2007; Choudhary et al. , 2008; Manickavasagan et al. , 2008a,b) believe that the linear discriminant analysis classifier can give the best result to identification of kernels.…”

Section: Resultsmentioning

confidence: 99%

Unsound kernel identification using linear colour charge‐coupled device

Chen

Luo

Fang

2012

Int J of Food Sci Tech

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Summary Wheat is one of the most consumed grains in the world. The identification of wheat based on surface characteristics is important for the market. This study is aimed at identifying unsound kernels (Triticum durum Desf), including 710 black germ kernels, 627 broken kernels and 1169 sound kernels from several seed distributors in China. The system is mainly composed of a liner charge‐coupled device for image capture and a software package for extracting various morphological, colour and texture features. The models built by partial least squares discriminate analysis, support vector machine discrimination analysis (SVMDA) and principal component analysis‐artificial neural networks for identifying the unsound kernels have been explored. After comparisons of these three methods, it has been found that SVMDA got the best accuracy: 95.1%, 96.0% and 98.3% (black germ kernels, broken kernels and sound kernels). Obviously, the experimental results have shown that SVMDA is the most feasible and effective choice for the identification.

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“…, 2007; Choudhary et al. , 2008; Manickavasagan et al. , 2008a,b) believe that the linear discriminant analysis classifier can give the best result to identification of kernels.…”

Section: Resultsmentioning

confidence: 99%

Unsound kernel identification using linear colour charge‐coupled device

Chen

Luo

Fang

2012

Int J of Food Sci Tech

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“…In this particular application, the key to the approach is the separation of the illumination and reflectance components achieved in the form shown in Eq. (5).the homomorphic filter function ( , ) H u v can then operate on these components separately, as indicated in Eq. (6).…”

Section: B Image Enhancementmentioning

confidence: 99%

“…Many researchers used machine vision for wheat classification; Digital imaging has been used as a non-destructive quality assessment tool for objective classification of cereal grains [1][2][3][4]. Neethirajan's used real time soft X-rays or transmitted light assessed kernel (Neethirajan et al, 2006).Manickavasagan compared the efficiencies of a monochrome camera-based vision system with three different illuminations to identify wheat classes [5]. Tahir evaluated the effect of moisture content on cereal grains by digital image analysis [6].…”

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confidence: 98%

Wheat Image Correction for Feature Segmentation Based on Color Linear CCD

Chen

Fang

2010

2010 International Conference on Digital Manufacturing &Amp; Automation

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This paper presented a machine vision system with a color linear CCD to identify several China wheat classes. However, these images were deformed due to color linear CCD itself, image noise and uneven brightness also existed. The image needs to be corrected so as to segment the wheat features better. Algorithms of linearity equation were developed to correct geometric distortions; homomorphic filtering and space domain enhancement methods for image restoration also played a significant role. From the evaluation, we could find that the image treatment was likely to effective.

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“…The selection of the illumination source must be done carefully which may drastically change the outcome (Luo et al, 1997;Brown and Timm, 1992;Manickavasagan et al, 2008). Due to this reason, most of the image related studies inevitably use either a laboratory (Murakami et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Due to this reason, most of the image related studies inevitably use either a laboratory (Murakami et al, 2005). Some of the light sources used are circular fluorescent lights (Paliwal et al, 2003;Majumdar and Jayas,1999), fluorescent tube lights (Manickavasagan et al, 2008), incandescent lights, and infrared lights (halogen lamps) in special situations (Hehn and Sokhansanj, 1990;Liu et al, 2005). Digital imaging is very much dependent on the light source and the resulting interpretation of colours in RGB coordinates or as gray levels.…”

Section: Introductionmentioning

confidence: 99%

In-situ colour correction for digital images acquired under non-standard lighting conditions

Manickavasagan

Alahakoon

2018

Jour. Agri. & Mar. Scie.

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Computer vision techniques using colour images are becoming popular in food and agriculture sector. Need of a standard illumination source is an important criterion in this approach to determine various attributes based on RGB values of the objects. In general, under laboratory conditions with standard lighting, an imaging system performs with high consistency in digitizing colour. However, in field conditions where the availability of a standard light source cannot be guaranteed, the colour interpretations may not yield accurate results. The objective of this study was to develop a simple algorithm to compensate for the variations in RGB values due to varying light conditions. It is intended to be useful in situations where taking digital images of objects without standard light sources is essential for a particular purpose. A set of quadratic transformation algorithms were developed to transform the RGB values of the images acquired under five different lighting conditions. The mean variance in RGB values of the image of a colour palette (with 6 different colours) taken under five lighting conditions were in the range of 277 – 548. After implementing the developed algorithm, this was reduced to 34 – 142. Similarly, this variance was reduced from 180 – 294 to 63 – 128 in the test conducted with a plant material. This algorithm can be easily adopted in all computer vision applications where variations in colour interpretations due to nonstandard lighting sources are common.

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Comparison of illuminations to identify wheat classes using monochrome images

Cited by 30 publications

References 15 publications

Unsound kernel identification using linear colour charge‐coupled device

Unsound kernel identification using linear colour charge‐coupled device

Wheat Image Correction for Feature Segmentation Based on Color Linear CCD

In-situ colour correction for digital images acquired under non-standard lighting conditions

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