Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system

Venora, Gianfranco; Grillo, Oscar; Shahin, Muhammad A.; Symons, Stephen J.

doi:10.1016/j.foodres.2006.09.001

Cited by 65 publications

(43 citation statements)

References 10 publications

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“…The comparison of the results of Venora et al (2007) and Bhattacharya et al (2005) with our results (Table 1) indicates that Turkish varie- Range: minimum -maximum value; SD -standard deviation; CV-coefficient of variance ties are the same in size as Sicilian landraces and Canadian cultivar. The main characteristics of the lentil samples used in the study are given in Table 2.…”

Section: Resultssupporting

confidence: 58%

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Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey

Firatligil-Durmus¹,

Šárka²,

Bubník³

2008

Czech J. Food Sci.

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Firatligil-Durmuş E., Šárka E., Bubník Z. (2008): Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey. Czech J. Food Sci., 26: 109-116.Geometrical features of lentil seeds (lens culinaris Medik) were analysed using the image analysis LUCIA system Ver. 3.52. The values of the weight of 1000 kernels, kernel density, specific volume, specific surface area, and surface area of 1000 kernels of red and green lentils were determined as 66.61 and 138.56 g, 1504.5 and 1376.4 kg/m 3 , 0.6647 and 0.7265 cm 3 /g, 0.594 and 0.579 m 2 /kg, 395.4 and 801.9 cm 2 , respectively. The lentil volume was simulated by an oblate spheroid and two sphere segments and the volumes obtained with both models were compared with that obtained by pycnometric method. Percentage differences of the two sphere segment approximation for red and green lentils were 4.4% and 4.2%, respectively. The height (thickness) of lentils was constant and practically the same with both varieties (2.6 mm) and therefore it was possible to simplify the geometrical models. Thus, 2D image analysis is suitable for a quick evaluation of the specific volume and surface area of grains on the basis of the projected area (equivalent diameter) without the measurement of the height. Image processing provides a simple, rapid, and non-invasive methodology to estimate lentil geometric features and engineering parameters.

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Section: Resultssupporting

confidence: 58%

“…Kadlec et al (2006b) dealt with shape characterisation of pea seeds using image analysis to determine the specific surface area of 0.60 m 2 /kg for micro-wave drying. Venora et al (2007) identified Sicilian landraces and Canadian cultivars of lentil. They are characterised by equivalent diameters of 4.47-6.97 mm and surface areas of 11.13-35.87 mm 2 .…”

mentioning

confidence: 99%

Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey

Firatligil-Durmus¹,

Šárka²,

Bubník³

2008

Czech J. Food Sci.

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“…As a result, traits that have never been measured before or that have only been measured in specific situations are starting to be measured more frequently (Fiorani and Schurr, 2013) and with greater ease, e.g., traits related to seed size, color, form, and texture (Granitto et al, 2005). Venora et al (2007) also reported that image analysis is a fast method that requires less than one minute for digitalization and measurement. It is also a very easily reproducible technique, in addition to being reliable and non-destructive.…”

Section: Introductionmentioning

confidence: 99%

Digital phenotyping for quantification of genetic diversity in inbred guava (Psidium guajava) families

Krause¹,

Viana²,

Cavalcante³

et al. 2017

Genet. Mol. Res.

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ABSTRACT. Digital image analysis of seeds has been used for the identification of cultivars, determination of seed color and mechanical damage, and classification of different seed sizes. The aim of the present study was to evaluate the efficiency of digital image analysis of seeds for the quantification of genetic diversity among genotypes of inbred guava (Psidium guajava L.) families. The SAS Mini equipment, which consists of a capture module and a software program for analysis, was employed for the capture and analysis of the seed images. Different genetic diversity quantification strategies were tested using the WardModified Location Model method. The set of variables related to geometry of the seeds was the largest contributor to divergence among the guava genotypes. The use of seed descriptors obtained by digital image analysis via the SAS system was efficient at quantifying the

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“…The experiment was also conducted within 1 year, and there is no information whether it can be used in subsequent years of cultivation. Similar studies using geometric parameters, together with texture and color of grain or seeds of papilionaceous plants, were conducted by [1,2,10,11,14,15].…”

Section: Introductionmentioning

confidence: 86%

Discrimination of wheat grain varieties using image analysis: morphological features

Zapotoczny

2011

Eur Food Res Technol

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This paper presents the results of a study on the discrimination of 11 wheat grain varieties in three successive years of cultivation and at the grain humidity of 12, 14 and 16%. Each grain was described with the use of 54 geometric variables which, after reduction of variables, left 20 for use in the main analysis. Variables calculated from linear dimensions had the greatest share in the group of discriminating variables, with shape-related indexes being of lesser importance. Seven methods of variables selection based on genetic algorithms (the Class Ranker and Class RankersSearch methods) were used in the study. The final discriminant analysis was performed with the use of stepwise progressive analysis and Meta MultiClass Classifier. The proposed statistical model classified varieties with 90-100% accuracy, depending on the experimental group. Grain images were acquired with a flat scanner, and grains were arranged with a specially designed matrix which enabled arranging 552 grains in rows and columns within several minutes; this makes the method usable in the cereal industry.

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Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system

Cited by 65 publications

References 10 publications

Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey

Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey

Digital phenotyping for quantification of genetic diversity in inbred guava (Psidium guajava) families

Discrimination of wheat grain varieties using image analysis: morphological features

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