The Agrigenomics project is a research initiative focused on the development of a predictive model for understanding gene sequences and predicting the phenotypic characteristics, specifically the height, of rice cultivars. By analyzing the genetic makeup of rice plants, this project aims to enhance our understanding of the relationship between genes and traits, contributing to advancements in agriculture and crop breeding.
The primary goal of the Agrigenomics project is to develop a model capable of accurately predicting the height of rice cultivars based on their gene sequences. This predictive capability will enable researchers and agricultural practitioners to identify desirable traits more efficiently, leading to improved crop yields and resilience.
- Gene sequence analysis: The project involves the analysis of gene sequences of rice cultivars to identify key genetic markers associated with plant height.
- Predictive modeling: Utilizing machine learning and statistical techniques, the project aims to develop a predictive model that can correlate gene sequences with phenotypic characteristics.
- Data analysis tools: The project includes the development of tools and algorithms for processing and analyzing large-scale genomic data sets to extract meaningful insights.
The findings and methodologies of the Agrigenomics project have been documented in the research paper titled "Genomic Prediction yield of Oryza Sativa Using Machine Learning and Deep Learning" published in the International Journal of Research in Information Technology and Computing. The paper is available here and provides detailed insights into the project's objectives, methodologies, and results, contributing to the broader scientific community's understanding of agrigenomics and crop breeding.