Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics
出版年份 2022 全文链接
标题
Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics
作者
关键词
-
出版物
RUSSIAN JOURNAL OF GENETICS
Volume 58, Issue 5, Pages 603-611
出版商
Pleiades Publishing Ltd
发表日期
2022-05-16
DOI
10.1134/s102279542205009x
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield
- (2019) Gniewko Niedbała Journal of Integrative Agriculture
- NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface
- (2019) Liu Zhang et al. SENSORS
- Modeling the seed yield of Ajowan ( Trachyspermum ammi L.) using artificial neural network and multiple linear regression models
- (2018) Mohsen Niazian et al. INDUSTRIAL CROPS AND PRODUCTS
- Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding
- (2018) Jared Crain et al. Plant Genome
- Multi-wheat-model ensemble responses to interannual climate variability
- (2016) Alex C. Ruane et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Genomic Prediction of Testcross Performance in Canola (Brassica napus)
- (2016) Habib U. Jan et al. PLoS One
- Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline
- (2016) D. C. Kadam et al. G3-Genes Genomes Genetics
- Genomic Prediction of Barley Hybrid Performance
- (2016) Norman Philipp et al. Plant Genome
- Seed yield prediction of sesame using artificial neural network
- (2015) Samad Emamgholizadeh et al. EUROPEAN JOURNAL OF AGRONOMY
- Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization
- (2015) T. J. SALO et al. JOURNAL OF AGRICULTURAL SCIENCE
- Application of Artificial Neural Networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L.)
- (2015) Mohammad Reza Naroui Rad et al. SCIENTIA HORTICULTURAE
- Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization
- (2015) T. J. SALO et al. JOURNAL OF AGRICULTURAL SCIENCE
- Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize
- (2014) Frank Technow et al. GENETICS
- Phenotyping and other breeding approaches for a New Green Revolution
- (2014) Jose Luis Araus et al. Journal of Integrative Plant Biology
- Predicting hybrid performance in rice using genomic best linear unbiased prediction
- (2014) S. Xu et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Predictive ability of machine learning methods for massive crop yield prediction
- (2014) Alberto Gonzalez-Sanchez et al. SPANISH JOURNAL OF AGRICULTURAL RESEARCH
- Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years
- (2014) Theresa Albrecht et al. THEORETICAL AND APPLIED GENETICS
- Yield Trends Are Insufficient to Double Global Crop Production by 2050
- (2013) Deepak K. Ray et al. PLoS One
- Best linear unbiased prediction of triticale hybrid performance
- (2012) Manje Gowda et al. EUPHYTICA
- Achieving yield gains in wheat
- (2012) MATTHEW REYNOLDS et al. PLANT CELL AND ENVIRONMENT
- Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India
- (2011) A. Singh et al. AGRICULTURAL WATER MANAGEMENT
- Global food demand and the sustainable intensification of agriculture
- (2011) D. Tilman et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now