Core Insights - The article discusses the integration of AI technology in agricultural breeding, specifically through the "Fengdeng" project, which aims to enhance crop breeding efficiency and precision using AI models [1][2]. Group 1: AI in Agricultural Breeding - The "Fengdeng" project, launched by a collaborative team including Shanghai Artificial Intelligence Laboratory and other research institutions, introduced the "Fengdeng·Seed Industry Large Model" in April 2024, followed by the "Fengdeng·Gene Scientist" AI tool in July 2024, designed to assist researchers in exploring and validating unknown gene functions [1]. - Traditional breeding methods are time-consuming and heavily reliant on expert experience, often taking years to validate hypotheses with limited success rates [1]. - The AI model is trained on vast datasets to identify relationships between genes and traits, enabling it to predict "gene-trait" associations and design breeding experiments [1][2]. Group 2: Advancements in Breeding Precision - The AI tool allows breeding researchers to combine superior alleles more accurately, addressing both traditional traits like yield and disease resistance, as well as new demands such as nutritional enhancement and flavor improvement [2]. - The "Fengdeng·Gene Scientist" simulates expert reasoning processes, automating the entire research workflow from hypothesis generation to result analysis, thereby enhancing research efficiency [2]. - The project has already identified new gene functions in rice and maize, with predictions aligning closely with field trial results, indicating a high level of accuracy in the AI's capabilities [2]. Group 3: Future Developments - The research team plans to continuously integrate more crop data, environmental data, and breeding knowledge into the system, evolving it into a comprehensive intelligent breeding platform that covers all species and processes [2].

Core Insights - The article emphasizes the importance of ensuring food security and sustainable agricultural development in the face of global population growth, climate change, and decreasing arable land resources [1] Group 1: Technological Innovations in Crop Improvement - A review paper published in Nature discusses the integration of multi-omics, genome editing, protein design, high-throughput phenotyping, and artificial intelligence (AI) in crop genetic improvement [2][3] - Modern omics technologies, such as genomics and metabolomics, provide unprecedented capabilities to analyze crop genetic information, revealing new loci for precise trait improvement [4] - High-throughput phenotyping (HTP) technologies utilize drones and sensors for rapid and accurate assessment of crop traits, effectively linking genotype to phenotype [4] Group 2: Genome Editing and Protein Design - Genome editing technologies, exemplified by CRISPR, enable efficient and precise modifications of crop genomes, significantly shortening breeding cycles and rapidly creating desirable traits [4] - AI-driven protein design technologies are emerging, allowing the creation of novel proteins with specific functions, which can lead to breakthroughs in disease resistance and environmental monitoring [4] Group 3: AI-Assisted Crop Design Framework - The review introduces an "AI-assisted crop design" model that integrates and analyzes multimodal big data from genomics, phenomics, environment, and management practices [19] - Breeders can set specific improvement goals, such as yield enhancement or stress resistance, while AI generates optimized breeding plans through deep learning and knowledge reasoning [19] Group 4: Challenges and Future Directions - The article discusses the challenges and future directions for the application of new technologies, highlighting the need for high-quality, standardized data for training AI models [21] - Regulatory policies for genome-edited crops are evolving towards more scientific and simplified frameworks, creating favorable conditions for the widespread application of new technologies [21]