Federated Reinforcement Learning with Linear Programming for Improving UAV-Enabled Smart Agriculture
Received: 11 August 2025 | Revised: 6 September 2025 | Accepted: 20 September 2025 | Online: 8 December 2025
Corresponding author: A. V. Mayakkannan
Abstract
The incorporation of Unmanned Aerial Vehicles (UAVs) has huge potential to improve crop monitoring, precision farming, and data gathering in smart agriculture. However, optimizing UAV functions on geologically distributed farms poses important challenges related to computational efficiency, energy depletion, and route selection. This paper introduces a Federated Reinforcement Learning with Linear Programming (FRLP) to address these issues. The proposed system utilizes an FRL algorithm, which is more suitable since every UAV learns about local energy depletion related to every movement path. The FRLP mechanism utilizes Super nodes (SPs) to collect and communicate sensor data to the UAV, and then remove redundant information using Principal Component Analysis (PCA). In this work, a Reinforcement Learning (RL) with Linear Programming (LP) model is utilized to forecast the next state based on a reward function, calculated using SP node energy, queued packets, and link quality. The LP also checks the UAV distance limit, the maximum UAV travels per round, and connectivity. Finally, the UAV decides on an optimal stop point to visit and collect data from the SP nodes. FRL integrated with UAVs offers promising advances in agricultural practices, particularly in optimizing UAV route formation to enhance cultivation efficiency. The simulation results illustrate that the FRLP mechanism reaches a 98.5% success rate and minimizes additional energy utilization.
Keywords:
Unmanned aerial vehicles, linear programming, federated reinforcement learning, energy efficiency, agriculture fieldDownloads
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Copyright (c) 2025 A. V. Mayakkannan, Chethan Chandra S. Basavaraddi, R. Karthick, S. Durga Devi, S. Amudha, R. Sasikumar, Bharat Tidke, S. Murugan
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