田垄间，科技赋能的场景处处可见。无人机识别出一片长势偏弱的区域，植保飞防无人机随即赶来开展 变量追肥作业，既不浪费资源，又能助力作物平衡增产。这背后，是智慧农业"一张图""一片云""一张 网"平台的核心支撑，让耕种管收各环节形成闭环。 科技扎根田间，源于持续的科创投入与成果转化。科技园已打造开放式创新平台，获批多项重点实验室 与创新联合体，通过产学研合作攻关AI农业、无人农场等前沿技术，构建起"中试—转化—推广"的完整 链条。 目前，苏垦农发正按梯度架构推进智慧农业落地，低空巡田、智能农机等场景持续拓展，土地产出率与 资源利用率显著提升。谈及未来，王灿明信心满满："我们将聚焦农业新质生产力，深化AI与智能装备 融合，打造示范标杆，用科技筑牢粮食安全防线，为农业强省建设添砖加瓦。"冬日的万亩农田，正在 科技赋能下，孕育着新的丰收希望。 "以前人工巡田，费时费力还难免有疏漏。现在有了这些AI'好帮手'，效率直接提升3到5倍！"苏垦农发 智慧农业科技园负责人王灿明指着空中的无人机介绍，如今农田管理已从"凭经验"转向"靠数据"，这80 余台配备在各分公司的巡田无人机，成了守护粮食生产可靠的"空中哨兵"。 在智慧指挥中 ...

加强农业人工智能全产业链布局。AI对农业的赋能,不仅要在育种、种植、养殖、渔业等领域横向铺 开，也需在生产加工、仓储物流、数字营销等环节纵向拓展。借助AI技术，承担喷药、施肥、播种、 运输、监测灾情等工作，并对农产品进行分级分选，将订单与库存快速匹配，自动分配不同销售渠道， 并精准投放广告。着力推进产学研联动与人工智能技术联合攻关，有效突破新型材料、高端农机等硬件 装备瓶颈，显著提升智能感知技术、自动化控制技术的研发应用。以全链条软硬件系统融合，实现农业 高端智能装备自主可控，以真实应用场景为锚点，多方协作、长期投入，推动AI全面整合农业产供销 全链路。 近日举办的2025年首届智慧农业创新大赛上，对靶施药除草机器人、温室搬运机器人、巡田无人机等智 慧农业发展成果，让观众享受了一场农业科技的"盛宴"，也让人对未来农业的发展空间充满期待。国务 院不久前印发的《关于深入实施"人工智能+"行动的意见》明确提出，加强人工智能在农业生产管理、 风险防范等领域应用，帮助农民提升生产经营能力和水平。人工智能的引入，有望把农业智能化推到一 个新的高度。 打破"数据孤岛"。AI农业大模型的深化发展离不开高质量的农业数据，其质量 ...

Core Insights - The event showcased innovations in smart agriculture, featuring robots and drones designed to enhance efficiency in farming tasks such as weeding, transporting, and monitoring crops [1][6] - The competition attracted 55 teams from 26 companies and 17 research institutions, highlighting the growing interest and investment in agricultural technology [1] Group 1: Weeding Robots - The targeted spraying weeding robot developed by Anhui Hefei Duojia Agricultural Technology Co., Ltd. can operate on 1 acre of land in under 1 minute, reducing pesticide usage by 30% to 80% compared to traditional methods [3] - The robot features an online mixing technology and dual spraying system, improving pesticide utilization and minimizing environmental pollution [3] Group 2: Transport Robots - The smart transport unmanned vehicle from Zhejiang Hangzhou Shennongshi Robot Co., Ltd. integrates various self-developed technologies, enabling it to navigate and avoid obstacles autonomously [5] - The new generation greenhouse transport robot GHBOT-02, developed by Beijing Agricultural College, utilizes low-cost 2D laser radar technology, reducing hardware costs by over 90% compared to traditional 3D laser radar [5] Group 3: Monitoring Drones - Drones demonstrated capabilities in quickly measuring field areas and identifying crops, providing data analysis and recommendations for farmers [6] - The drones can complete monitoring tasks for 50-60 acres in about 10 minutes, significantly improving efficiency compared to manual labor [6] Group 4: Future of Smart Agriculture - The event served as a platform to showcase China's achievements in smart agriculture and promote the digital transformation of the agricultural sector [6] - Experts predict that the future of smart agriculture will focus on automation, precision, and intelligence, contributing to the modernization of agriculture [6]

Core Insights - The application of artificial intelligence (AI) in agriculture is rapidly advancing, with various innovations such as four-legged robots for smart farming and automated pollination robots being introduced [2][3] - The Chinese government has issued policies to accelerate the digital transformation of agriculture, emphasizing the integration of AI in breeding systems and agricultural management [2][4] Group 1: Current Developments in Smart Agriculture - Companies are utilizing AI technologies, such as drones and sensors, to enhance crop monitoring and pest control, leading to a reduction in pesticide costs by 10% to 20% [3] - The implementation of the "National Smart Agriculture Action Plan (2024-2028)" aims to promote smart agriculture through policy support, technology innovation, and service enhancement [4][6] Group 2: Benefits of AI in Agriculture - AI applications in agriculture are automating repetitive tasks like pesticide spraying and harvesting, which traditionally relied on human labor [4] - The use of IoT data allows farmers to make precise decisions regarding fertilization, irrigation, and crop management, thereby reducing costs and increasing efficiency [4][6] Group 3: Challenges Facing AI Adoption - There are significant challenges in data acquisition and sharing, with issues such as data fragmentation and lack of standardization hindering model training and application [7] - High costs of technology implementation and insufficient infrastructure in rural areas limit the widespread adoption of AI solutions [7] Group 4: Future Trends and Recommendations - By 2028, it is expected that the integration of information technology in agriculture will significantly enhance productivity and efficiency, with a target of achieving over 32% informationization in agricultural production [8] - The development of customized AI solutions tailored to the needs of smallholders is recommended to facilitate technology adoption and improve agricultural outcomes [9]