The International Rice Research Institute (IRRI) has launched the Global AI-Hybrid Rice Platform (GAI-HRP), an AI-powered digital tool designed to speed up hybrid rice breeding and parental selection. This innovative platform aims to enhance food security and promote sustainable agriculture by accelerating the development of high-yielding and climate-resilient rice hybrids.
Developed by the IRRI Hybrid Rice Unit in collaboration with the Hybrid Rice Development Consortium (HRDC), GAI-HRP harnesses the power of artificial intelligence and machine learning to analyze vast datasets accumulated over decades of research. This advanced technology allows for the rapid and precise identification of optimal hybrid rice combinations based on specific breeding parameters, making the process significantly more efficient and cost-effective.
Hybrid rice is created by crossing two distinct parental lines, often resulting in plants that exhibit ‘hybrid vigor,’ leading to yield increases of up to 30% or more. Many of these hybrids also demonstrate enhanced traits such as improved tolerance to water stress, shorter maturity periods, and reduced dependency on fertilizers.
However, the traditional process of identifying the best parental combinations for targeted traits in diverse environments is often slow and labor-intensive.
GAI-HRP streamlines this process by utilizing SNP genotypic data from male and female parental lines to predict the most productive F1 hybrid rice combinations. The platform also factors in historical hybrid datasets and market-specific requirements, enabling breeders, researchers, and seed companies to make data-driven decisions with greater accuracy and efficiency.
Beyond improving yield potential, GAI-HRP contributes to sustainable agriculture by identifying rice hybrids with a lower carbon footprint. By focusing on early-maturing, high-yielding varieties that require fewer resources, the platform helps reduce greenhouse gas emissions and supports eco-friendly farming practices. Further advancements in the system aim to refine gene panel predictions for targeted hybrid rice traits, optimizing breeding precision and efficiency.
Dr. Seyed Mahdi Hosseiniyan Khatibi, IRRI Postdoctoral Fellow and lead developer of GAI-HRP, highlighted the platform’s potential to accelerate and enhance accuracy in hybrid selection.
Meanwhile, Dr. Jauhar Ali, Research Unit Leader for IRRI Hybrid Rice Technology, emphasized that the AI-driven approach would significantly reduce field trials and associated costs, paving the way for increased global hybrid rice productivity.
