2025.06. 24 丰农控股集团总农艺师王兴林表示，近五年时间，团队在AI方面的实践包括了图像识别、病虫害管 理、生产力评估、农场综合管理、精准施肥用药等方面。我国农业文明虽然悠久，但工业化冲击对农 业发展带来的提升空间，目前来看仍然很大。也因此，丰农的定位仍以农业服务为核心，通过AI工 具属性来服务农业发展。 AI农业智能化提升慢的原因可以归结为三大方面：数据基础薄弱、高成本与低ROI预期、场景复杂性 与信任壁垒。由于土壤、气候、作物品种多样等原因，农业数据分散，农业相关数据集不足以训练可 靠AI模型，且缺乏实时数据收集能；智能农机、无人机等农业AI设备成本高昂，农业行业投资回报 周期长；从业者更信任经验，将AI视为辅助工具。 本文字数：1342，阅读时长大约3分钟 作者 | 第一财经 吕倩 6月24日，中金公司研究部发布的研报显示，AI渗透需长期演进，尤其从技术萌芽到企业大规模部署 阶段，会经历资源分配、用户习惯转变、技术与场景打磨、产品迭代等节点，可能存在短期阵痛。今 日收盘，AI应用(8841683)指数收涨2.55%。一方面，大模型落地行业应用进展迅速，但同时，部分 领域AI渗透率仍增长缓慢。A ...

Core Insights - The report from CICC indicates that AI penetration in agriculture requires long-term evolution, facing challenges such as resource allocation, user habit changes, and technology refinement [1] - AI applications in agriculture are currently in the experimental demonstration phase, with slow penetration rates in certain areas despite rapid advancements in large model applications [1] - The complexity of agricultural environments and the need for precise recommendations pose significant challenges for AI models, which must adapt to various conditions [7][8] Industry Overview - The agricultural sector has seen the emergence of multiple large model products, with competition centered around data accuracy and relevance to customer needs [4] - The investment landscape in smart agriculture has been active from 2015 to 2022, yet only 20-30 companies receive funding each year, indicating a selective investment environment [8] - The Ministry of Agriculture and Rural Affairs has set a goal for agricultural production informationization to reach over 32% by the end of 2028, highlighting the importance of national policies in fostering AI agricultural development [8] Challenges in AI Agriculture - Key challenges hindering AI adoption in agriculture include weak data foundations, high costs, low ROI expectations, and trust barriers among practitioners [1] - The diversity of agricultural data due to varying soil, climate, and crop types complicates the training of reliable AI models [1] - The complexity of agricultural environments limits the generalizability of AI solutions, making it difficult to apply models developed in one context to another [7][8]