Study on image analysis application for identification Quinoa seeds (Chenopodium quinoa Willd) geographical provenance

Medina, Wenceslao T.; Skurtys, Olivier; Aguilera, J. M.

doi:10.1016/j.lwt.2009.07.010

Cited by 45 publications

(41 citation statements)

References 17 publications

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“…Principal component analysis was used by Medina et al (2010) in the investigation of a set of 38 variables composed of 8 geometrical, 20 morphological, four fractal, and six color variables obtained from 25 varieties of quinoa (Chenopodium quinoa Willd) seeds. The first principal component (PC1) explained 47.7% of the total variation; the second plus the first, 65.8%; and the sum of the first three components explained 75.5%, suggesting that a three-dimensional graphic representation would be more suitable.…”

Section: Discussionmentioning

confidence: 99%

“…The digital image analysis of seeds has been employed for the identification of cultivars, determination of seed colors and mechanical damage, and classification of different seed sizes (Venora et al, 2009;Medina et al, 2010;Kara et al, 2013;Pinto et al, 2015;Andrade et al, 2016). However, to our knowledge, there are no reports on the use of digital imaging to measure seed traits such as size, form, texture, and color as applied for the quantification of intraspecific genetic diversity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Image size was adjusted to 640 x 480 pixels in red, green and blue (RGB), following the protocol de-scribed by Medina et al, (2010). Seed parameters obtained were length (L), width (W), area, perimeter, and thickness; while mathematical equations below were used to calculate, Feret diameter (DF), elongation index (EI), roundness index (RI) and compaction index (CI) (Wilcox et al, 2002;Isaza et al, 2017):…”

Section: Morphometric Characterization By Image Analysismentioning

confidence: 99%

Physicochemical characteristics of seeds from wild and cultivated castor bean plants (Ricinus communis L.)

et al. 2018

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The castor (Ricinus communis L.) is an oilseed plant whose main features are its drought resistance, and its adaptation to eroded, polluted, and low fertility soils. Its oil has a great demand in the industrial sector and it has recently attracted considerable interest for its use in the production of biodiesel and jet fuel. In this study, morphological, physical and chemical characterizations were performed to ascertain the quality of wild (VQ-1) and under cultivation (VQ-7) oil castor seeds. The results showed that there are differences in the morphological and physicochemical characteristics regarding oil content ( ), and acidity index (0,9918 vs 0,5440 mg KOH/g) for VQ-1 and VQ-7, respectively. Growing conditions to which castor plants were subjected may influence both the final quality of seeds and chemical properties of the oil.Keywords: characterization of seed, oil quality, castor seed chemistry. RESUMENLa higuerilla (Ricinus communis L.) es una planta oleaginosa cuyas principales características son su resistencia a la sequía y su adaptación a suelos erosionados, contaminados y de baja fertilidad. Su aceite tiene una gran demanda en el sector industrial y recientemente ha despertado un gran interés para ser utilizado en la producción de biodiesel y bioturbosina. En esta investigación, se realizaron caracterizaciones morfológicas, físicas y químicas de la semilla, así como una evaluación de la calidad del aceite de semillas silvestres (VQ-1) y bajo cultivo (VQ-7). Los resultados demostraron diferencias en las características morfológicas y físico-químicas con respecto al contenido de aceite (44,95 vs 33,84 %), cenizas (3,20 vs 2,42 %) y el peso de 100 semillas (45,87 vs 54,23 g); se observó un comportamiento similar en la caracterización del aceite: viscosidad cinemática (269,67 vs 266,44 mm 2 /s), densidad (0,9389 vs 0,9465 g/cm 3 ) e índice de acidez (0,9918 vs 0,5440 mg KOH g), respectivamente para VQ-1 y VQ-7. Las condiciones de crecimiento a las cuales fueron sometidas las plantas de ricino pueden influir en la calidad final de las semillas y propiedades químicas del aceite.Palabras clave: calidad de aceite, caracterización de semilla, química del ricino.

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“…The system used the weighted average method to convert the color image into gray image; that was, in accordance with the visual characteristics suitable for human [7], theories and experiments indicated that a group of weights (Red was 0.299, Green was 0.587 and Blue was 0.144) was suitable [8]. The transformation equation for:…”

Section: Grayingmentioning

confidence: 99%

Research of Maize Seeds Classification Recognition Based on the Image Processing

Li¹,

Liu²,

Gao³

2016

IJSIP

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Study on image analysis application for identification Quinoa seeds (Chenopodium quinoa Willd) geographical provenance

Cited by 45 publications

References 17 publications

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

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

Physicochemical characteristics of seeds from wild and cultivated castor bean plants (Ricinus communis L.)

Research of Maize Seeds Classification Recognition Based on the Image Processing

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