检索结果-南通市图书馆

TrG2P:A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

Plant Communications 2024年第7期5卷 16-27页

作者： Jinlong Li Dongfeng Zhang Feng Yang Qiusi Zhang Shouhui Pan Xiangyu Zhao Qi Zhang Yanyun Han Jinliang Yang Kaiyi Wang Chunjiang Zhao Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Engineering Research Center for Information Technology in Agriculture Beijing 100097China Department of Agronomy and Horticulture University of Nebraska-LincolnLincolnNE 68583USA Center for Plant Science Innovation University of Nebraska-LincolnLincolnNE 68583USA These authors contributed equally to this article

Yield prediction is the primary goal of genomic selection(GS)-assisted crop *** yield is a complex quantitative trait,making predictions from genotypic data is *** learning can produce an effective model for a target task by leveraging knowledge from a different,but related,source domain and is considered a great potential method for improving yield prediction by integrating multi-trait ***,it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation *** therefore developed TrG2P,a transfer-learning-based ***2P first employs convolutional neural networks(CNN)to train models using non-yield-trait phenotypic and genotypic data,thus obtaining pre-trained ***,the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task,and the fully connected layers are retrained,thus obtaining fine-tuned ***,the convolutional layer and the first fully connected layer of the fine-tuned models are fused,and the last fully connected layer is trained to enhance prediction *** applied TrG2P to five sets of genotypic and phenotypic data from maize(Zea mays),rice(Oryza sativa),and wheat(Triticum aestivum)and compared its model precision to that of seven other popular GS tools:ridge regression best linear unbiased prediction(rrBLUP),random forest,support vector regression,light gradient boosting machine(LightGBM),CNN,DeepGS,and deep neural network for genomic prediction(DNNGP).TrG2P improved the accuracy of yield prediction by 39.9%,6.8%,and 1.8%in rice,maize,and wheat,respectively,compared with predictions generated by the best-performing comparison *** work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait *** attribute its enhanced prediction accuracy to the valuable information ava

关键词： crop genotype to phenotype transfer learning yield prediction multi-trait

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Clonal Variation in Growth and Wood Quality and the multi-trait Index Selection of Chinese Fir

引用

Chinese Forestry Science and Technology 2012年第3期11卷 49-49页

作者： Wang Runhui Hu Dehuo Zheng Huiquan Wei Ruping Huang Yuemao Cai Weijiu Guangdong Academy of Forestry Dakengshan State-owned Forest Farm in Zhaoqing of Guangdong Province

Two clonal trial stands of Chinese Fir （Cunninghamia lanceolata） were used in this study, one was 19-year-old stand which included 38 clones, and the other was 17-year-old stand including 102 *** statistical analyses showed that there were very significant genetic variations in height, DBH,volume and ratio of heartwood(Rhw),wood basic density（ρb ） of the clones in the two stands. The repeatability of clones was in median to high level,and the genetic CV was different over the all five *** were very significant phenotypic and genetic correlations among height,DBH and volume,and negative correlations among growth, Rhw andρb.The selection method experiment indicated that index selection could improve volume, Rhw andρb,showing synthetically superior selection effects compared to any individual trait selection methods.

关键词： Chinese Fir(Cunninghamia lanceolata) clone wood quality multi-trait index selection

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Characterization of early maturing elite genotypes based on MTSI and MGIDI indexes:an illustration in upland cotton(Gossypium hirsutum L.)

引用

Journal of Cotton Research 2024年第3期7卷 253-265页

作者： D S RAJ Supritha PATIL Rajesh S. PATIL Bhuvaneshwara R. NAYAK Spurthi N. PAWAR Kasu N. Department of Genetics and Plant Breeding College of AgricultureUniversity of Agricultural SciencesDharwad 580005India Agricultural Research Station Dharwad 580005India Department of Biotechnology College of AgricultureUniversity of Agricultural SciencesDharwad 580005India Department of Crop Physiology College of AgricultureUniversity of Agricultural SciencesDharwad 580005India

Background Globally,the cultivation of cotton is constrained by its tendency for extended periods of *** maturity plays a potential role in rainfed-based multiple cropping system especially in the current era of climate *** the current study,a set of 20 diverse Gossypium hirsutum genotypes were evaluated in two crop seasons with three planting densities and assessed for 11 morphological traits related to early *** study aimed to identify genotype(s)that mature rapidly and accomplish well under diverse environmental conditions based on the two robust multivariate techniques called multi-trait stability index(MTSI)and multi-trait genotype-ideotype distance index(MGIDI).Results MTSI analysis revealed that out of the 20 genotypes,three genotypes,viz.,NNDC-30,A-2,and S-32 accomplished well in terms of early maturity traits in two ***,three genotypes were selected using MGIDI method for each planting densities with a selection intensity of 15%.The strengths and weaknesses of the genotypes selected based on MGIDI method highlighted that the breeders could focus on developing early-maturing genotypes with specific traits such as days to first flower and boll *** selected genotypes exhibited positive genetic gains for traits related to earliness and a successful harvest during the first and second ***,there were negative gains for traits related to flowering and boll *** The study identified three genotypes exhibiting early maturity and accomplished well under different planting *** multivariate methods(MTSI and MGIDI)serve as novel approaches for selecting desired genotypes in plant breeding programs,especially across various growing *** methods offer exclusive benefits and can easily construe and minimize multicollinearity issues.

关键词： Cotton MTSI MGIDI Genotype environment interaction Early maturity multi-trait multi-environment

来源：

维普期刊数据库

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：