基于大语言模型的个性化作物水肥管理智能决策方法  

Intelligent Decision-Making Method for Personalized Vegetable Crop Water and Fertilizer Management Based on Large Language Models

作　　者：吴华瑞[1] 李静晨 杨雨森 WU Huarui;LI Jingchen;YANG Yusen(Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100079,China)

机构地区：[1]北京市农林科学院信息技术研究中心,北京100079

出　　处：《智慧农业(中英文)》2025年第1期11-19,共9页Smart Agriculture

基　　金：国家重点研发计划(2021ZD0113604);财政部和农业农村部国家现代农业产业技术体系建设专项(CARS-23-D07);中央引导地方科技发展资金项目(2023ZY1-CGZY-01)。

摘　　要：[目的/意义]为解决当前作物管理中个性化需求难以捕捉、决策过程缺乏灵活性难题,本研究提出了一种基于大语言模型的个性化作物生产智能决策方法[方法]通过自然语言对话收集用户在蔬菜作物管理过程中的个性化需求,涵盖产量、人力资源消耗和水肥消耗等方面。随后,将作物管理过程建模为多目标优化问题,同时考虑用户个性化偏好和作物产量,并采用强化学习算法来学习作物管理策略。水肥管理策略的训练通过与环境的交互持续更新,学习在不同条件下采取何种行动以实现最优决策,从而实现个性化的作物管理。[结果和讨论]在gym-DSSAT(Gym-Decision Support System for Agrotechnology Transfer)仿真平台上进行的实验,结果表明,所提出的个性化作物生产智能决策方法能够有效地根据用户的个性化偏好调整作物管理策略。[结论]通过精准捕捉用户的个性化需求,该方法在保证作物产量的同时,优化了人力资源与水肥资源的消耗。[Objective]The current crop management faces the challenges of difficulty in capturing personalized needs and the lack of flexibility in the decision-making process.To address the limitations of conventional precision agriculture systems,optimize key aspects of agricultural production,including crop yield,labor efficiency,and water and fertilizer use,while ensure sustainability and adaptability to diverse farming conditions,in this research,an intelligent decision-making method was presents for personalized vegetable crop water and fertilizer management using large language model(LLM)by integrating user-specific preferences into decision-making processes through natural language interactions.[Methods]The method employed artificial intelligence techniques,combining natural language processing(NLP)and reinforcement learning(RL).Initially,LLM engaged users through structured dialogues to identify their unique preferences related to crop production goals,such as maximizing yield,reducing resource consumption,or balancing multiple objectives.These preferences were then modeled as quantifiable parameters and incorporated into a multi-objective optimization framework.To realize this framework,proximal policy optimization(PPO)was applied within a reinforcement learning environment to develop dynamic water and fertilizer management strategies.Training was conducted in the gym-DSSAT simulation platform,a system designed for agricultural decision sup‐port.The RL model iteratively learned optimal strategies by interacting with the simulation environment,adjusting to diverse condi‐tions and balancing conflicting objectives effectively.To refine the estimation of user preferences,the study introduced a two-phase process comprising prompt engineering to guide user responses and adversarial fine-tuning for enhanced accuracy.These refinements ensured that user inputs were reliably transformed into structured decision-making criteria.Customized reward functions were devel‐oped for RL training to address specific agricultural

关 键 词：作物管理 大语言模型 多目标决策 个性化决策 PPO算法 

分 类 号：S24[农业科学—农业电气化与自动化]