Industry Background: Transition to a "Data-Driven" Era in Breeding - Traditional breeding relies heavily on time-consuming field trials and personal experience, typically requiring 7-12 years for completion, which is costly and has a limited success rate. The global breeding industry is undergoing a fundamental shift from "experience-driven" to "data + model-driven" due to the significant decrease in genome sequencing costs, widespread application of high-throughput phenotyping technologies, and continuous enhancement of AI computing power [2]. Core Applications of AI in the Breeding Industry - **Genomic Selection**: This technology analyzes vast amounts of genetic markers to accurately predict key traits such as crop yield, quality, and disease resistance. AI can reduce the breeding cycle from 7-10 years to 4-6 years and improve trait prediction accuracy by 10-25%, while significantly lowering field validation costs. Leading companies like Bayer and KWS have implemented this at scale for major crops [6]. - **Phenotypic Analysis**: AI enhances the efficiency and accuracy of collecting phenotypic data through automated analysis of plant growth, disease severity, and canopy structure using drones and hyperspectral cameras. This technology increases selection efficiency by 5-10 times and provides objective quantitative data for field trials [7]. - **Hybrid Combination Prediction**: AI integrates multi-dimensional data to improve the accuracy of predicting successful hybrid combinations, addressing the issue where over 95% of traditional combinations fail to meet expectations. This allows for the early elimination of ineffective combinations, focusing resources on high-potential materials [8]. - **Environmental Modeling**: AI constructs G×E (genotype × environment) models to predict how different genotypes perform in various environments, facilitating a shift from reliance on multi-site trials to model-driven predictions, thus overcoming geographical limitations in breeding [10]. Global Application Status: From Experimental Exploration to Large-Scale Implementation - The global breeding industry has formed three distinct tiers in AI application: - Leading seed companies like Bayer and Syngenta have established comprehensive data infrastructures and AI breeding systems, creating high technical barriers [12]. - Research institutions such as CGIAR and USDA are driving innovation in AI breeding projects for staple crops, providing theoretical and technical support [13]. - Small and medium enterprises are gaining capabilities through SaaS platforms, allowing them to access advanced breeding tools at lower costs [13]. Commercialization Models and Industry Transformation Directions - Three main commercialization models have emerged in AI breeding: - **SaaS Software Subscription Model**: Targets small and medium breeding companies, offering tiered pricing based on image processing volume or trial area, lowering the application threshold for AI technology [15]. - **Data Analysis Service Model**: Provides specialized services for specific breeding projects, converting professional knowledge into value [15]. - **AI-Driven New Variety Commercialization**: This mainstream model for seed companies enhances breeding efficiency, leading to faster market introduction and more stable traits, ultimately increasing seed sales revenue [15]. Future Prospects: AI Will Reshape the Global Breeding Competitive Landscape - The breeding industry is expected to undergo three distinct development stages driven by AI: - **Short-term (1-3 years)**: Rapid commercialization of single-point AI tools, with phenotypic analysis AI leading the way and genomic selection solidifying its mainstream status [17]. - **Mid-term (3-7 years)**: AI will evolve from single-point tools to a full-process breeding system, with digital field trials becoming common and hybrid combination prediction transitioning into routine production [17]. - **Long-term (7-10 years)**: The deep integration of AI with gene editing technologies will create a closed-loop system, significantly reducing breeding costs by 40-60% and shortening cycles by 30-50% [17]. Domestic Development Status: Breakthroughs in China's AI Breeding Field - The year 2025 is projected to be a landmark year for China's AI breeding, with several innovative achievements: - The Shanghai AI Laboratory, in collaboration with other institutions, launched the first autonomous scientific discovery system in biological breeding, capable of simulating molecular biologist functions [19]. - Zhejiang University and Huawei developed an "AI Breeder" that enhances cotton hybrid breeding efficiency by 20 times and reduces the breeding cycle from 6-8 years to 3-4 years [19]. - The establishment of a unified data platform by the Qiaozhou Bay National Laboratory and Huawei significantly improves breeding efficiency through standardized data collection [19]. Conclusion: AI Initiates the "Second Revolution" in the Breeding Industry - AI is leading the most profound transformation in the breeding industry since the advent of hybrid breeding technology, marking a shift from reliance on experience to data-driven predictions. This revolution fundamentally changes the breeding paradigm from "art" to "science," establishing quantifiable and predictable mathematical models [21]. The integration of AI with gene editing technologies will usher in an era of "precise design" in breeding, redefining competitive dynamics in the global seed industry [21].

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 discusses the integration of AI technology in agricultural breeding, specifically through the "Fengdeng" project, which aims to enhance crop breeding efficiency using advanced AI models [1][2]. Group 1: AI in Crop 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 to assist researchers in exploring and validating unknown gene functions [1][2]. - The AI model is trained on vast datasets to accurately identify the relationship between genes and traits, enabling precise predictions and experimental designs in breeding [2]. Group 2: Breeding Efficiency and Challenges - Traditional breeding methods are time-consuming and heavily reliant on expert experience, often taking years to validate hypotheses with limited success rates [1]. - The increasing frequency of extreme climate events has made reliance on manual experience even less effective, highlighting the need for data-driven approaches in breeding [1]. Group 3: Research Outcomes and Future Directions - The "Fengdeng" project has identified new gene functions related to plant height and photosynthetic efficiency in rice, and accurately predicted candidate genes associated with traits in corn, aligning well with field trial results [3]. - The research team plans to expand the system to incorporate more crop data, environmental data, and breeding knowledge, evolving towards a comprehensive intelligent breeding platform [3].

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 using advanced AI models [1][2]. Group 1: AI in Agricultural Breeding - The "Fengdeng" project, a collaboration among several research institutions, launched 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][2]. - Traditional breeding methods are time-consuming and heavily reliant on expert experience, often taking years to validate hypotheses with limited success rates [1][2]. Group 2: Capabilities of the AI Model - The AI model has been trained on vast datasets to accurately identify relationships between genes and traits, predict "gene-trait" associations, and design breeding experiments [2]. - The "Fengdeng·Gene Scientist" can simulate expert reasoning processes, completing the entire research workflow from hypothesis generation to result analysis, thereby enhancing the efficiency of breeding research [2]. Group 3: Research Outcomes - The project has identified new gene functions in rice that affect plant height and photosynthetic efficiency, and in corn, it has accurately predicted candidate genes related to plant height and ear position, aligning closely with field trial results [3]. - The research team plans to expand the system to incorporate more crop data, environmental data, and breeding knowledge, evolving towards a comprehensive intelligent breeding platform [3].

Core Insights - The emergence of AI scientists represents a significant advancement in scientific research, enabling faster hypothesis generation and experimental design, as demonstrated by the recent validation of a new bacterial gene transmission mechanism by Google's AI in just 48 hours [1][2] Group 1: AI Scientist Development - AI scientists are not physical robots but intelligent agents powered by large language models, capable of generating scientific hypotheses and research plans autonomously [1] - The global competition among research institutions to develop AI scientist systems is intensifying, with two main categories: AI as research assistants and fully autonomous scientific discovery systems [2][3] Group 2: Research Assistant Systems - The first category focuses on creating AI systems that assist human scientists, providing interdisciplinary knowledge and research ideas, exemplified by Stanford University's "Virtual Laboratory" which successfully designed 92 antiviral nanobodies [2] Group 3: Autonomous Discovery Systems - The second category aims to develop fully autonomous systems capable of scientific discovery, with examples including Japan's "Fish AI" which produced a computer science paper and the "Future Home" AI system that discovered a drug for dry macular degeneration [3] Group 4: China's AI Scientist Initiatives - China is accelerating the development of AI scientist systems, with initiatives like the "Virtual Scientist" system and the "Feng Deng Gene Scientist" system, which has identified previously unreported gene functions in staple crops [4] Group 5: Future Prospects - The future may see more physical AI scientists assisting in complex research environments, such as "AI crop geneticists" and "AI soil scientists," transforming previously fictional scenarios into reality [5]