Delay Efficient Federated Learning based Plant Disease Detection and Monitoring (DFLPDDM) in Agricultural Fields: A UAV-IoT Environment

Anuradha Banerjee

doi:10.36948/ijfmr.2026.v08i03.79161

Delay Efficient Federated Learning based Plant Disease Detection and Monitoring (DFLPDDM) in Agricultural Fields: A UAV-IoT Environment

Author(s)	Dr. Anuradha Banerjee
Country	India
Abstract	Plant disease detection, monitoring and smart spraying using Unmanned Aerial Vehicle-Internet of Things (UAV-IoT) environment, has become extremely important in precision agriculture, this has to be performed in delay efficient manner so that prompt action can be taken to save as many plants as possible. Energy efficiency is an added advantage that incorporates sustainability in thesolution. However, to the best of authors knowledge, articles in relevant literature have neglected the concept of security which is indispensable if different regions of a big agricultural land belong to different owners and they want to keep their land status information confidential. In this article, we propose a delay efficient federated learning-based plant disease detection and monitoring scheme (DFLPDDM) that utilizes the concept of transmitting gradients among untrusted UAV’s and transmitting data among trusted UAVs to ensure security and confidentiality. Also, mechanisms are proposed to incorporate delay and energy efficiency to improve overall performance effectiveness of the system. Simulation results shows that DFLPDDM produce much better performance compared to many other state-of-the-art agricultural field monitoring algorithms.
Keywords	Agricultural fields, energy, federated learning, follower, leader, plant disease detection and monitoring, precision agriculture.
Field	Computer Applications
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-26
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.79161

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Delay Efficient Federated Learning based Plant Disease Detection and Monitoring (DFLPDDM) in Agricultural Fields: A UAV-IoT Environment

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