Evaluation of the tepal colour of Begonia x tuberhybrida Voss. for DUS testing using image analysis

Lootens, Peter; Waes, Johan Van; Carlier, L.

doi:10.1007/s10681-006-9315-0

Cited by 11 publications

(8 citation statements)

References 10 publications

(8 reference statements)

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“…Grain number is a 'bad' feature, the reason may be that most of the peanuts we chose are double kernel, and the difference is not significant in grain number. Furthermore, for area (1), extent (8), smoothness (14), consistency (16), area/convex area (11), perimeter (4), eccentricity (7), shape factor (9), their discrimination abilities are high, and they can also be used as potential traits for DUS testing in the future. Here, we have chosen these 12 features for DUS testing as candidate features.…”

Section: Feature Selection By Fishermentioning

confidence: 99%

“…By investigating its germplasm resources we found that this variety is a breed after hybridization between Baisha 1016 and 99D1, and belongs to a Spanish type small peanut which is different from other big peanut varieties. Beside this, the varieties numbered 7, 10, 11,12,8,14,13 are gathered into the second major category and the rest are divided into the third category. The varieties of the second category were mainly harvested from Qingdao, China and it may be due to external environmental factors that make this category exhibit same characteristics in appearance.…”

Section: Paternity Analysis By K-meansmentioning

confidence: 99%

“…Furthermore, image processing methods are used on wheat, rice 8,9 and other crops. 10,11 However, in terms of peanut pod inspection, few relevant research studies are reported.…”

Section: Introductionmentioning

confidence: 99%

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Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Deng

Han

2018

J Sci Food Agric

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BACKGROUND: DUS (Distinctness, Uniformity and Stability) testing of new varieties is an important method for peanut germplasm evaluation and identification of varieties. In order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner. Initially, six DUS testing traits were quantified using a mathematical method based on image processing technology, and then, size, shape, color and texture features (total 31) were also extracted. Next, the Fisher algorithm was used as a feature selection method to select 'good' features from the extracted features to expand the DUS testing traits set. Finally, support vector machine (SVM) and K-means algorithm were respectively used as recognition model and clustering method for variety identification and pedigree clustering.RESULTS: By the Fisher selection method, a number of significant candidate features for DUS testing were selected which can be used in the DUS testing further; using the top half of these features (about 18) ordered by Fisher discrimination ability, the recognition rate of SVM model was found to be more than 90%, which was better than unordered features. In addition, a pedigree clustering tree of 20 peanut varieties was built based on the K-means clustering method, which can be used in deeper studies of the genetic relationship of different varieties. CONCLUSION: This article can provide a novel reference method for future DUS testing, peanut varieties identification and study of peanut pedigree.

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Section: Feature Selection By Fishermentioning

confidence: 99%

Section: Paternity Analysis By K-meansmentioning

confidence: 99%

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Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Deng

Han

2018

J Sci Food Agric

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“…(Yoshioka et al, 2006)], and begonia [Begonia •tuberhybrida Voss. (Lootens et al, 2007)]. Digital color analysis is also performed in plant pathology to quantify lesions on diseased leaves (Kwack et al, 2005).…”

mentioning

confidence: 99%

Tomato Analyzer-color Test: A New Tool for Efficient Digital Phenotyping

Darrigues¹,

Hall²,

Knaap³

et al. 2008

J. Amer. Soc. Hort. Sci.

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Measuring plant characteristics via image analysis has the potential to increase the objectivity of phenotypic evaluations, provides data amenable to quantitative analysis, and is compatible with databases that aim to combine phenotypic and genotypic data. We describe a new tool, which is implemented in the Tomato Analyzer (TA) software application, called Color Test (TACT). This tool allows for accurate quantification of color and color uniformity, and allows scanning devices to be calibrated using color standards. To test the accuracy and precision of TACT, we measured internal fruit color of tomato (Solanum lycopersicum L.) with a colorimeter and from scanned images. We show high correlations (r > 0.96) and linearity of L*, a*, and b* values obtained with TACT and the colorimeter. We estimated genotypic variances associated with color parameters and show that the proportion of total phenotypic variance attributed to genotype for color and color uniformity measured with TACT was significantly higher than estimates obtained from the colorimeter. Genotypic variance nearly doubled for all color and color uniformity traits when collecting data with TACT. This digital phenotyping technique can also be applied to the characterization of color in other fruit and vegetable crops.

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“…But since the aperture of many colorimeters is limited in size and shape, it is difficult to accurately evaluate fruit that has an uneven surface colour, such as gradations, blotches, and stripes. However, recent improvements in computer and optical instrument performance may allow researchers to develop new objective methods of phenotyping plant organ colours based on image analysis (e.g., Choi et al 1995;Leemans et al 2002;Yoshioka et al 2006Yoshioka et al , 2007Lootens et al 2007;Darrigues et al 2008). In many cases, simple statistics, such as averages and standard deviations of the RGB (red, green, blue) colour channels or of the CIE-Lab (L*, a*, b*) colour space, are calculated as colour features of an object or region of interest (ROI).…”

Section: Introductionmentioning

confidence: 99%

Image-based phenotyping: use of colour signature in evaluation of melon fruit colour

Yoshioka

Fukino

2009

Euphytica

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Fruit colour, both external and internal, is important because it relates directly to the commercial value of the product. In breeding and in pre-and postharvest studies of fruit colour, an effective method for evaluating colour is needed to replace subjective evaluations by eye. We used a series of data processing and statistical analyses used in content-based image retrieval to evaluate melon flesh colour, and assessed the efficacy of this approach. This method relies on summarizing colour information from images into colour signatures, calculating the earth mover's distance (EMD) between colour signatures, and multi-dimensional scaling (MDS) based on an EMD matrix. Performing MDS on a set of fruit flesh images revealed important colour features, such as the yellowish-green strength in green-fleshed melons and the relative size of the green and red parts in red-fleshed melons, without the need for an explicit definition of these features. The proportion of variance due to differences among cultivars was higher by MDS than by traditional evaluation, indicating that this new method performed best at detecting colour differences among cultivars. The method provides effective, objective indicators of fruit colour, and shows considerable promise for use in research and breeding programs.

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Evaluation of the tepal colour of Begonia x tuberhybrida Voss. for DUS testing using image analysis

Cited by 11 publications

References 10 publications

Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Image features and DUS testing traits for peanut pod variety identification and pedigree analysis

Tomato Analyzer-color Test: A New Tool for Efficient Digital Phenotyping

Image-based phenotyping: use of colour signature in evaluation of melon fruit colour

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