• Journal of Plant Biotechnology
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• v.46 no.3
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• pp.165-171
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• 2019

Marker-assisted backcrossing (MABC) is useful for selecting an offspring with a highly recovered genetic background for a recurrent parent at early generation to various crops. Moreover, marker-assisted backcrossing (MABC) along with marker-assisted selection (MAS) contributes immensely to overcome the main limitation of the conventional breeding and it accelerates recurrent parent genome (RPG) recovery. In this study, we were employed to incorporate rin gene(s) from the donor parent T13-1084, into the genetic background of HK13-1151, a popular high-yielding tomato elite inbred line that is a pink color fruit, in order to develop a rin HK13-1084 improved line. The recurrent parent genome recovery was analyzed in early generations of backcrossing using SNP markers obtained from genotyping-by-sequencing analysis. From the $BC_1F_1$ and $BC_2F_1$ plants, 3,086 and 4868 polymorphic SNP markers were obtained via GBS analysis, respectively. These markers were present in all twelve chromosomes. The background analysis revealed that the extent of RPG recovery ranged from 56.7% to 84.5% and from 87.8% to 97.8% in $BC_1F_1$ and $BC_2F_1$ generations, respectively. In this study, No 5-1 with 97.8% RPG recovery rate among $BC_2F_1$ plants was similar to HK13-1151 strain in the fruit shape. Therefore, the selected plants were fixed in $BC_2F_2$ generation through selfing. MAS allowed identification of the plants that are more similar to the recurrent parent for the loci evaluated in the backcross generations. MABC can greatly reduce breeding time as compared to the conventional backcross breeding. For instance, MABC approach greatly shortened breeding time in tomato.

• Horticultural Science & Technology
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• v.30 no.5
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• pp.557-567
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• 2012

This study was conducted to achieve basal information for the development of tomato cultivars with disease resistances through marker-assisted backcross (MAB). Ten inbred lines with TYLCV, late blight, bacterial wilt, or powdery mildew resistance and four adapted inbred lines with superior horticultural traits were collected, which can be useful as the donor parents and recurrent parents in MAB, respectively. Inbred lines collected were evaluated by molecular markers and bioassay for confirming their disease resistances. To develop DNA markers for selecting recurrent parent genome (background selection) in MAB, a total of 108 simple sequence repeat (SSR) primer sets (nine per chromosome at average) were selected from the tomato reference genetic maps posted on SOL Genomics Network. Genetic similarity and relationships among the inbred lines were assessed using a total of 303 polymorphic SSR markers. Similarity coefficient ranged from 0.33 to 0.80; the highest similarity coefficient (0.80) was found between bacterial wilt-resistant donor lines '10BA333' and '10BA424', and the lowest (0.33) between a late blight resistant-wild species L3708 (S. pimpinelliforium L.) and '10BA424'. UPGMA analysis grouped the inbred lines into three clusters based on the similarity coefficient 0.58. Most of the donor lines of the same resistance were closely related, indicating the possibility that these lines were developed using a common resistance source. Parent combinations (donor parent ${\times}$ recurrent parent) showing appropriate levels of genetic distance and SSR marker polymorphism for MAB were selected based on the dendrogram. These combinations included 'TYR1' ${\times}$ 'RPL1' for TYLCV, '10BA333' or '10BA424' ${\times}$ 'RPL2' for bacterial wilt, and 'KNU12' ${\times}$ 'AV107-4' or 'RPL2' for powdery mildew. For late blight, the wild species resistant line 'L3708' was distantly related to all recurrent parental lines, and a suitable parent combination for MAB was 'L3708' ${\times}$ 'AV107-4', which showed a similarity coefficient of 0.41 and 45 polymorphic SSR markers.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.