农业科学发现大模型大幅提升科研人员研发效率

Core Insights - The upgraded agricultural scientific discovery model has achieved significant enhancements in capabilities, transitioning agricultural biological breeding from "experience trial and error" to a new paradigm of "intelligent creation" [1][2] Group 1: Model Enhancements - The model's knowledge base has expanded from over 1 million to over 2 million full-text documents, providing a higher quality and more comprehensive foundation for agricultural scientific research [1] - The data coverage has increased from over 30 species to over 50 core species, with a total data volume exceeding 10 billion entries, establishing a solid data foundation for deep exploration of superior gene resources [1] - The platform integrates over 100 specialized analytical tools and predictive models, optimizing the autonomous invocation and orchestration processes of multi-agent systems [1] Group 2: Breeding and Management Capabilities - The performance of the full-process digital breeding module has been enhanced, supporting multi-dimensional gene function prediction and precise site design for targeted trait improvement [1] - The intelligent breeding capabilities have been extended to the planting management phase, adding functionalities for variety screening, growth assessment, and pest identification based on actual field production needs [1] - The upgraded model incorporates natural language interaction capabilities, enabling "zero-code" natural language analysis for gene mining, trait prediction, and program design [2] Group 3: Efficiency Improvements - Initial estimates suggest that the efficiency of researchers in key R&D processes can be improved by approximately 10 times [2] - The model supports panoramic R&D program intelligent planning, allowing for the completion of over 1,000 steps of calculations and design simulations in about 24 hours, with efficiency improvements in related processes reaching over 90% [2]