Rice Breeding

Cereals ◽  
2009 ◽  
pp. 99-126 ◽  
Author(s):  
Elcio P. Guimarães
Keyword(s):  
2018 ◽  
Vol 1 (74) ◽  
pp. 25-32
Author(s):  
Ruslan Dzhamirze ◽  
◽  
Nadezhda Ostapenko ◽  
Keyword(s):  

2021 ◽  
Author(s):  
Yang Xu ◽  
Kexin Ma ◽  
Yue Zhao ◽  
Xin Wang ◽  
Kai Zhou ◽  
...  

Rice ◽  
2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Lin Zhang ◽  
Bin Ma ◽  
Zhong Bian ◽  
Xiaoyuan Li ◽  
Changquan Zhang ◽  
...  

Abstract Background Grain size is an extremely important aspect of rice breeding, affecting both grain yield and quality traits. It is controlled by multiple genes and tracking these genes in breeding schemes should expedite selection of lines with superior grain yield and quality, thus it is essential to develop robust, efficient markers. Result In this study, 14 genes related to grain size (GW2, GS2, qLGY3, GS3, GL3.1, TGW3, GS5, GW5, GS6, TGW6, GW6a, GLW7, GL7 and GW8) were selected for functional marker development. Twenty-one PCR-gel-based markers were developed to genotype the candidate functional nucleotide polymorphisms (FNPs) of these genes, and all markers can effectively recognize the corresponding allele types. To test the allele effects of different FNPs, a global collection of rice cultivars including 257 accessions from the Rice Diversity Panel 1 was used for allele mining, and four grain-size-related traits were investigated at two planting locations. Three FNPs for GW2, GS2 and GL3.1 were genotyped as rare alleles only found in cultivars with notably large grains, and the allele contributions of the remaining FNPs were clarified in both the indica and japonica subspecies. Significant trait contributions were found for most of the FNPs, especially GS3, GW5 and GL7. Of note, GW5 could function as a key regulator to coordinate the performance of other grain size genes. The allele effects of several FNPs were also tested by QTL analysis using an F2 population, and GW5 was further identified as the major locus with the largest contribution to grain width and length to width ratio. Conclusions The functional markers are robust for genotyping different cultivars and may facilitate the rational design of grain size to achieve a balance between grain yield and quality in future rice breeding efforts.


2012 ◽  
Vol 112 (2) ◽  
pp. 331-345 ◽  
Author(s):  
T. J. Rose ◽  
S. M. Impa ◽  
M. T. Rose ◽  
J. Pariasca-Tanaka ◽  
A. Mori ◽  
...  

2014 ◽  
Vol 127 (4) ◽  
pp. 995-1004 ◽  
Author(s):  
Hiroshi Shinada ◽  
Toshio Yamamoto ◽  
Eiji Yamamoto ◽  
Kiyosumi Hori ◽  
Junichi Yonemaru ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 281
Author(s):  
Ruoling Deng ◽  
Ming Tao ◽  
Xunan Huang ◽  
Kemoh Bangura ◽  
Qian Jiang ◽  
...  

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.


Author(s):  
U Barokah ◽  
U Susanto ◽  
M Swamy ◽  
D W Djoar ◽  
Parjanto

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