Abstract:

[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 support. The RL model iteratively learned optimal strategies by interacting with the simulation environment, adjusting to diverse conditions 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 developed for RL training to address specific agricultural goals. The reward functions account for crop yield, resource efficiency, and labor optimization, aligning with the identified user priorities. Through iterative training and simulation, the system dynamically adapted its decision-making strategies to varying environmental and operational conditions. [Results and Discussions] The experimental evaluation highlighted the system's capability to effectively personalize crop management strategies. Using simulations, the method demonstrated significant improvements over traditional approaches. The LLM-based model accurately captured user-specific preferences through structured natural language interactions, achieving reliable preference modeling and integration into the decision-making process. The system's adaptability was evident in its ability to respond dynamically to changes in user priorities and environmental conditions. For example, in scenarios emphasizing resource conservation, water and fertilizer use were significantly reduced without compromising crop health. Conversely, when users prioritized yield, the system optimized irrigation and fertilization schedules to enhance productivity. These results showcased the method's flexibility and its potential to balance competing objectives in complex agricultural settings. Additionally, the integration of user preferences into RL-based strategy development enabled the generation of tailored management plans. These plans aligned with diverse user goals, including maximizing productivity, minimizing resource consumption, and achieving sustainable farming practices. The system's multi-objective optimization capabilities allowed it to navigate trade-offs effectively, providing actionable insights for decision-making. The experimental validation also demonstrated the robustness of the PPO algorithm in training the RL model. The system's strategies were refined iteratively, resulting in consistent performance improvements across various scenarios. By leveraging LLM to capture nuanced user preferences and combining them with RL for adaptive decision-making, the method bridges the gap between generic precision agriculture solutions and personalized farming needs. [Conclusions] This study established a novel framework for intelligent decision-making in agriculture, integrating LLM with reinforcement learning to address personalized crop management challenges. By accurately capturing user-specific preferences and dynamically adapting to environmental and operational variables, the method offers a transformative approach to optimizing agricultural productivity and sustainability. Future work will focus on expanding the system's applicability to a wider range of crops and environmental contexts, enhancing the interpretability of its decision-making processes, and facilitating integration with real-world agricultural systems. These advancements aim to further refine the precision and impact of intelligent agricultural decision-making systems, supporting sustainable and efficient farming practices globally.

Key words: crop management, large language model, multi-objective decision, personalized decision, proximal policy optimization