Core Insights - The "Shennong" model, developed by China Agricultural University, is a specialized AI model for agriculture, addressing the unique complexities of the agricultural sector and aiming to transform traditional farming practices into data-driven approaches [2][4]. Development - The Shennong model was initiated in 2020 to create a tailored AI solution for agriculture, recognizing that generic models often fail to meet the specific needs of the agricultural sector [2]. - A dedicated team built a core database by scanning over 3,000 agricultural books, resulting in a specialized database of 20,000 volumes to support the model's development [2][3]. - The model underwent rigorous data quality control processes, ensuring high-quality agricultural data was used for training [3]. - The Shennong model has evolved through several versions, with the 3.0 version launched in October 2025, featuring a lightweight architecture and 36 specialized intelligent agents for various agricultural tasks [4]. Applications - The Shennong model has been piloted in various regions, providing real-time data analysis and decision-making support for farmers, significantly reducing operational costs [6][7]. - In breeding, the model has successfully developed new crop varieties, such as the "Yanshi No. 2" lettuce, which shows improved yield and disease resistance [8]. - The "Policy Assistant" intelligent agent helps farmers navigate complex agricultural policies, streamlining access to information and reducing the burden on local officials [9]. Promotion - The adoption of the Shennong model is facilitated by user-friendly applications, such as the "Shennong Weitian" WeChat mini-program, which simplifies the process for farmers to diagnose crop issues [10]. - The "Technology Small Courtyard" model involves team members living and working alongside farmers to provide hands-on training and support, enhancing trust and acceptance of the technology [10]. - The upgraded 3.0 version of the model aims to lower the barriers for new farmers, enabling them to leverage AI tools for efficient farming practices [11].