Seed image analysis provides evidence of taxonomic differentiation within the <i>Medicago</i> L. sect. <i>Dendrotelis</i> (Fabaceae)

Bianco, Marisol Lo; Ferrer‐Gallego, P. Pablo; Grillo, Oscar; Laguna, Emilio; Venora, Gianfranco; Bacchetta, Gianluigi

doi:10.1080/14772000.2015.1046968

Cited by 15 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to be able to deal with large same kinds and numbers in an accurate manner are crucial to increase the discrimination power of an identification system because they depend not only on the intra-specific representativeness of the analyzed samples but also on the quality and quantity of the parameters measured and used to discriminate among groups, as well as on the dimension and variability degree of the groups according to recent papers [18,19]. Even the relatively recent study on soybean seed character had only small number of samples due to the difficulty as mentioned above [20,21].…”

Section: Discussionmentioning

confidence: 99%

High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

Baek

Lee

Kim

et al. 2020

Sensors

View full text Add to dashboard Cite

Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.

show abstract

Section: Discussionmentioning

confidence: 99%

High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

Baek

Lee

Kim

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Samples are increasingly similar at values closer to zero. Higher values indicate that a particular case includes extreme values for one or more independent variables, and can be considered significantly different to other cases of the same group (Lo Bianco et al, 2015b). Finally, with the aim of exploring the relationships between the galactomannan content and the seed phenotypic parameters, also including the seed weight, a Pearson correlation was performed.…”

Section: Discussionmentioning

confidence: 99%

Productive, qualitative and seed image analysis traits of guar (Cyamopsis tetragonoloba L. Taub.)

Gresta¹,

Santonoceto²,

Ceravolo³

et al. 2016

Aust J Crop Sci

Self Cite

View full text Add to dashboard Cite

Due to the high content of galactomannans extractable from the endosperm, guar or cluster bean (Cyamopsis tetragonoloba (L.) Taub), is always more frequently cultivated as a grain crop. Guar galactomannans are a source of hydrocolloid able to form a thick solution even in cold water in low concentrations. For this reason, in the last few years the demand for guar gum has greatly increased both for the food and the oil industry. Guar seed accessions identification is quite difficult, since American and Indian varieties and unlabelled genotypes show similar phenotypic seed traits, but very different agronomic value. Eight guar accessions with different origins have been cultivated in a Mediterranean environment and grain yield and galactomannans enzymatic determination have been carried out. The data of 137 morpho-colorimetric quantitative variables were measured, on each seed, by image analysis techniques and analysed applying a stepwise Linear Discriminant Analysis. This procedure allowed us to implement an identification grading system able to discriminate among varieties, with an overall performance of 92.2%. With this work it has been proved that an objective, repeatable and non-destructive method, based on seed phenotypic characterization can be effectively applied also to identify and discriminate C. tetragonoloba accessions. Moreover, for the first time, many strong and positive correlations were found between the galactomannans content and the size descriptive variables measured on seeds, allowing an easy procedure for the determination and comparison of seed galactomannans content. This procedure could become a useful tool for researchers and stakeholders.

show abstract

“…Image analysis has been implemented for the identification of several plants (Gresta et al, 2016; Lo Bianco et al, 2015; Smykalova, Grillo, Bjelkova, Hybl, & Venora, 2011; Ucchesu et al, 2015) confirming its performance as an analytical approach for seed authenticity. Many researchers have presented digital image processing using three important features: color features, morphological features and textural features.…”

Section: Introductionmentioning

confidence: 96%

Identification of sorghum genotypes using a machine vision system

Nazari

Shaker

Karimi

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

The aim of this study was to develop an innovative machine vision system based on seed texture and geometric parameters to evaluate their efficacy in the identification of sorghum genotypes. The images of sorghum grain were acquired by a digital camera. Texture parameters were calculated for images converted to individual color channels. The geometric features including length, width, perimeter, area as well as 10 shape factors were determined. The seeds of individual genotypes were classified using the discriminative classifiers. In the case of discriminant analysis performed using texture parameters, the total accuracy reached 95.6% for the model built based on textures selected from all color channels. The complete separation of some genotypes was confirmed by scatterplots. The results of discrimination based on textures selected from color space Lab revealed a total accuracy of up to 94.8%. Discriminative models based on textures selected from color space RGB resulted in a total accuracy of up to 93.5%. Models built based on geometric features provided the accuracy of up to 99% for comparison selected genotypes. Practical Applications The classification models developed in this study can efficiently be used for the identification of sorghum seeds. The results of the studies can be used in practice for the assessment of the authentication of seeds of sorghum cultivars in a rapid, nondestructive and cost‐effective way and it can be an alternative to chemical or molecular methods, which are more expensive, long‐lasting and more complicated. Therefore, the application of image analysis is a great novelty of the research for the identification of cereal grain genotype.

show abstract

Seed image analysis provides evidence of taxonomic differentiation within the Medicago L. sect. Dendrotelis (Fabaceae)

Cited by 15 publications

References 44 publications

High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

Productive, qualitative and seed image analysis traits of guar (Cyamopsis tetragonoloba L. Taub.)

Identification of sorghum genotypes using a machine vision system

Contact Info

Product

Resources

About