Agentic AI with Large Language Models for Precision Farming: Advancing Sustainable Resource Optimization in Smart Agritech

Abstract

Precision Agriculture tries to ensure maximum use of resources to get maximum production of crops with minimum use of water, fertilizers, and agrochemicals under highly dynamic climatic and soil conditions. Most existing precision farming approaches, however, depend on static rule-like logic or are single-task machine learning models, which possess limited contextual reasoning capabilities, multi-objective coordination, and adaptive decision making capabilities. In this paper, present a new Agentic Artificial Intelligence approach where a Large Language Model (LLM) acts as a high-level cognitive controller for autonomous and sustainable farm management. The proposed solution processes multimodal data such as soil and climate sensor data, weather predictions and crop growth stages information, along with historical farm operations and agronomic knowledge to recommend coordinated irrigation, fertilization and crop protection decisions. Instead of traditional advisory or threshold-based methods, the framework uses closed-loop agentic control where the candidate decisions produced by the LLM are tested for agronomic, environmental, and resource feasibility then carried out via IoT-based actuators. Adaptive refinement of policies and context-aware optimization of policies is made over time, enabled by continuous field monitoring. It focuses on explainable autonomy through a combination of natural language reasoning and machine-readable action commands, achieved by reducer farmer decision burden with transparency and safety. The results suggest the potential to substantially enhance water- and nutrient-use efficiency with less loss in crop yield using the proposed method. This study builds a foundation for the design of multi-scale, interpretable and autonomous precision agriculture systems with Agentic AI, hence it makes a stride toward realizing sustainable smart agritech.