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Volume 16, Issue 1
  • ISSN: 2772-574X
  • E-ISSN: 2772-5758

Abstract

Accurate prediction of breeding values is challenging due to the genotype-phenotype relationship is crucial and necessary for producing crops with elite genotypes. This paper is about investigating and predicting the phenotypic trait Height and Yeild in a genotype.

Background

Most of the existing studies focus on genetic methods or Machine learning models, in this, we implemented a hybrid combination of genetic methods and machine learning models that accurately predicted phenotypic trait yield, height and subpopulation.

Methodology

Our proposed methodology for genomic prediction of yield in (rice) involves a two-level classification approach. First, we classify biological sequences and cluster them using the UPGMA algorithm on a phylogenetic tree. Then, we use advanced machine learning techniques like Random Forest, and K-Nearest Neighbours to predict GEBVs with 85-95% accuracy on rice subpopulations.

Results

We achieved an accuracy of 93% when compared with other stated literature in this paper.

Conclusion

This approach overcomes limitations and effectively enhances crop breeding by capturing the genotype-phenotype relationship.

© 2025 Bentham Science Publishers
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2024-02-15
2025-03-01

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/content/journals/rafna/10.2174/012772574X281849240130120235
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GPOSYSH: Genomic Prediction of Oryza sativa Yield and Subpopulation Using Hybrid Methods
Recent Pat Food Nutr Agric 16, 57 (2025); https://doi.org/10.2174/012772574X281849240130120235
/content/journals/rafna/10.2174/012772574X281849240130120235
/content/journals/rafna/10.2174/012772574X281849240130120235
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  • Article Type:     Research Article
Keyword(s): genomic prediction; genotype; machine learning; Oryza sativa; phenotype; UPGMA

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