AI Agents Implementing AI Agents for Strategic Transformation in Agriculture

Abstract

This paper focuses on the application of Artificial Intelligence agents (AI agents) in smart farming, highlighting their potential to drive strategic transformation across agricultural prodction systems. It introduces a general framework for AI agent implementation, emphasizing a problem-specific design that integrates wearable IoT sensors, computer vision, and cloud-based analytics to enable data-driven decision-making. A detailed case study on estrus detection in dairy cows demonstrates this framework in practice. For this task, a traditional Random Forest (RF) model and a Reinforcement Learning (RL)-based Q-learning agent were employed and compared on simulated data. While the overall accuracy metrics of both models were very similar, the RF model demonstrated higher potential by prioritizing the correct classification of non-estrus cases. In contrast, the RL agent delivered a more balanced and practically successful approach in the simulated environment. The RL agent's success is attributed to a carefully designed reward structure that effectively balances detection sensitivity with the need to minimize operational costs from false alarms. Our findings from simulated data once again validate the transformative potential of AI agents in addressing critical agricultural challenges, from reproductive management to resource optimization. The study also emphasizes the importance of ethical design, farmer-centric interfaces, and scalable deployment strategies for real-world applications.