Polymer Composite-Enabled UAV Platform for Edge AI-Based Precision Agriculture: A System-Level Evaluation

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Year : 2026 | Volume : 14 | 04 | Page :
    By

    Patan Imran Khan,

  • Y Narasimha Reddy,

  • P Veeresh,

  • Akhtar Khan,

  1. Assistant Professor, Department of Electronics and Communication Engineering, St. Johns College of Engineering and Technology, Yemmiganur-518360, Kurnool(D), Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, St. Johns College of Engineering and Technology(A), Yemmiganur-518360, Kurnool(D), Andhra Pradesh, India
  3. Assistant Professor, Department of Computer Science and Engineering, St. Johns College of Engineering and Technology(A), Yemmiganur-518360, Kurnool(D), Andhra Pradesh, India
  4. Assistant Professor, Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kurnool, Andhra Pradesh, India

Abstract

This study investigates the system-level role of commercially available polymer composite materials in enabling lightweight and energy-efficient unmanned aerial vehicle (UAV) platforms integrated with edge artificial intelligence for real-time agricultural monitoring. Rather than developing or experimentally characterizing new composite materials, the work evaluates fiber-reinforced polymer (FRP) composites and epoxy-based laminates as enabling structural components whose established properties support UAV performance in precision agriculture. Their high strength-to-weight ratio, corrosion resistance, and vibration damping characteristics contribute to improved flight endurance, sensing stability, and energy efficiency. Polymer composites used in UAV frames, FR4 epoxy-glass printed circuit boards, and protective housings provide lightweight construction and mechanical reliability essential for sustained aerial operations. The proposed system, AgroVision-Edge, is deployed on a 1.4 kg quadrotor equipped with a Raspberry Pi 4B (8 GB), Sony IMX477 imaging sensor, and u-blox NEO-M9N GNSS module. Its classification backbone, AgroVision-Net, extends MobileNetV3-Large with a lightweight channel-attention mechanism and is trained on a geo-referenced dataset of 23,200 aerial images covering five crop disease categories. Post-training INT8 quantisation reduces the model size to 1.87 MB while enabling efficient execution on embedded ARM platforms. Experimental results demonstrate a top-1 classification accuracy of 98.1% on a plot-disjoint test set of 4,640 images, with a mean end-to-end inference latency of 43.3 ms and a 95th-percentile latency of 108 ms under concurrent workloads. A geo-referenced Coverage Score of 97.7% over a 4.2-hectare field confirms high spatial detection fidelity. The lightweight polymer-based UAV structure contributes to reduced power consumption of 8.4 W and sustained flight endurance, enabling continuous real-time inference without cloud dependence. The proposed framework demonstrates the effective integration of polymer-based UAV systems and edge AI for scalable, energy-efficient precision agriculture in resource-constrained environments.

Keywords: Edge AI; UAV crop disease detection; AgroVision-Net; geo-tagged imaging; MobileNetV3; channel attention; precision agriculture; INT8 quantisation; Raspberry Pi; five-class classification.

How to cite this article:
Patan Imran Khan, Y Narasimha Reddy, P Veeresh, Akhtar Khan. Polymer Composite-Enabled UAV Platform for Edge AI-Based Precision Agriculture: A System-Level Evaluation. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL:
Patan Imran Khan, Y Narasimha Reddy, P Veeresh, Akhtar Khan. Polymer Composite-Enabled UAV Platform for Edge AI-Based Precision Agriculture: A System-Level Evaluation. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=246497


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Ahead of Print Subscription Original Research
Volume 14
04
Received 25/05/2026
Accepted 05/06/2026
Published 11/06/2026
Publication Time 17 Days
2

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