Abstract

This study presents a hybrid framework that integrates Reinforcement Learning (RL) and Imitation Learning (IL) to optimize irrigation and nitrogen application in precision agriculture. RL agents learn adaptive management policies through interactions with simulated crop–environment systems, whereas IL accelerates training by leveraging expert demonstrations. The proposed framework was benchmarked using crop growth models under varying climatic and soil conditions. The results indicate a 3–6% yield increase, 8–15% improvement in water use efficiency, and 12–22% nitrogen reduction compared to baseline methods, with a 30–40% faster convergence rate. These findings demonstrate the potential of RL + IL approaches to enhance agricultural sustainability, scalability, and resilience.

Authors

Sudharshan Banakar, R. Pawan Kumar, K. Chandrashekar, N. Srikanta
Rao Bahadur Y. Mahabaleswarappa Engineering College, India

Keywords

Precision Agriculture, Reinforcement and Imitation Learning, Sustainable Crop Management, Deep RL, Hybrid Learning Frameworks