With the wave of digitization sweeping across the globe, artificial intelligence (AI), as a strategic technology leading a new round of technological revolution, is constantly accelerating breakthroughs and reshaping the new agricultural landscape. From basic scientific research to applied scientific development, the integration of artificial intelligence and science has given rise to the fifth research paradigm AIforScience (AI4S, AI driven scientific research), which can break through the cognitive boundaries of traditional agricultural research and lead disruptive innovation in fields such as crop breeding and pest prediction. At the same time, the deep integration of AI and smart agriculture promotes the transformation of agricultural production towards data-driven and intelligent decision-making, accelerates the construction of a new ecology for promoting agriculture through technology, and thus drives the transformation of rural revitalization. AI, with its powerful capabilities in data processing, intelligent analysis, and pattern recognition, can break through the cognitive limitations of field scientists and is gradually becoming an indispensable tool in modern agricultural scientific research, injecting new momentum into the intelligent, efficient, and collaborative development of agricultural scientific research. In recent years, important breeding technologies such as gene editing breeding, genome selection breeding, and design breeding have developed rapidly, and intelligent breeding has become an emerging frontier in the seed industry. The breeding paradigm is shifting from "experimental selection" to "computational selection", entering the "4.0 era" of breeding that combines "conventional breeding+biotechnology+information technology+artificial intelligence". At present, there has been some progress in the application of AI4S in the agricultural field in China, such as the smart breeding platform jointly developed by the National Southern Breeding Research Institute of the Chinese Academy of Agricultural Sciences and Alibaba Damo Institute, which integrates breeding data management, analysis, and artificial intelligence algorithm prediction functions, improving breeding efficiency and accuracy; The intelligent breeding system based on big data and AI, jointly developed by the Crop Science Research Institute of the Chinese Academy of Agricultural Sciences and the Modern Agriculture Research Institute of Peking University, can collect and clean environmental, genotype, and phenotype data, and construct predictive models based on machine learning and AI technology for predicting phenotypes and guiding breeding decisions; The Agricultural Information Research Institute of the Chinese Academy of Agricultural Sciences and Tongfang Knowledge Network have jointly developed China's first agricultural universal language model - the Agricultural Knowledge Big Model. It mainly targets multiple application scenarios such as agricultural technology innovation, agricultural production services, agricultural knowledge popularization, and auxiliary agricultural decision-making, providing intelligent knowledge services covering the entire agricultural industry chain. It can efficiently serve agricultural management departments, agricultural research institutes, agricultural universities, agricultural enterprises, agricultural technicians, etc., enhance the ability of agricultural intelligent knowledge services, serve the development of agricultural technology innovation, and help create new quality productivity in agriculture. In addition to agricultural technology, AI has also demonstrated strong momentum and enormous potential in the application of smart agriculture and even rural revitalization. AI and agricultural production data are widely integrated and applied in the agricultural industry chain, supply chain, and value chain, empowering agricultural technology innovation in multiple scenarios, links, and fields. For example, through high-precision sensors, satellite remote sensing, and unmanned aerial vehicles, coupled with artificial intelligence algorithms, crop phenotype data collection and analysis, weed identification, and overall growth discrimination of livestock, poultry, and crops can be achieved; The army of agricultural robots, including intelligent planting, crop reconnaissance, intelligent irrigation, intelligent picking and control, and autonomous navigation and transportation, has gathered and exploded, constructing the "unmanned farm" production mode. While enabling smart agriculture, AI has gradually penetrated into the diversified development path of agriculture and rural areas, forming rural governance models such as Internet and rural governance grid, digital village and public services, and smart village. Effectively promote the extension of rural digital industries such as smart tourism, rural e-commerce, and rural animal husbandry; We have strengthened the comprehensive digital governance of smart village affairs, safe countryside, and living environment; It has enriched rural digital services, such as Internet medical care, distance education, production knowledge services, market conditions, agricultural financial services access model and other artificial intelligence technologies, to narrow the gap between urban and rural public resource allocation. It should be said that AI has effectively improved the quality and efficiency of agricultural production, and the monitoring and management of agricultural production processes are faster, more intelligent, and more precise; Promote the iteration and upgrading of modern agriculture, while breaking through the boundaries of traditional agriculture, further strengthening and extending the supply chain, and enhancing the resilience of the agricultural industry; Continuously empowering sustainable development in rural areas, the role of linking agriculture and promoting agricultural benefits is more prominent, providing more possibilities for increasing agricultural income. However, we also need to recognize that currently, AI still faces many challenges in the industrial implementation process of intelligent scientific research and smart agriculture, such as the lack of high-quality, long-term, all factor dynamic agricultural data and knowledge corpus, bottlenecks in the implementation of high-value application scenarios, contradictions between the interpretability and scientific credibility of agricultural AI models, lagging application of intelligent agricultural machinery and equipment technology, and incomplete rural agricultural infrastructure. From data barriers to cognitive gaps, from "technological black boxes" to isolated scenarios, the collaborative innovation of AI and scientific paradigms has not yet formed a complete closed loop, and the deep development of multi scenario integration of "AI+agriculture" also needs to be further strengthened. In the long run, China needs to strengthen the comprehensive layout and top-level design of "grasping planning, strengthening foundation, attacking technology, creating products, building ecology, and cultivating talents", proactively deploy and initiate support for core key technology research and key projects, build a digital scientific research collaborative innovation platform for intelligent scientific research, attach great importance to the construction of collaborative innovation system, deepen the construction of interdisciplinary system and the cultivation of interdisciplinary talent team, adhere to demand orientation, deepen the application scenarios of "AI+agriculture", create a high-level industry innovation base of "AI+agriculture", build a complete intelligent agricultural ecological system, and help develop new agricultural productivity. (New Society)