Title: Predictive analysis of smart agriculture using IoT-based UAV and propagation models of machine learning

Authors: M. Kumarasamy; Balachandra Pattanaik; Jaiprakash Narain Dwivedi; B.R. Ramji; Muruganantham Ponnusamy; V. Nagaraj

Addresses: Department of Computer Science, Wollega University, Nekemte, Ethiopia ' Department of Electrical and Computer Engineering, Wollega University, Nekemte, Ethiopia ' ECE Department of Lingaya's Vidyapeeth, Haryana, India ' Department of Industrial Engineering and Management, BMS College of Engineering, Bangalore, India ' Indian Institute of Information Technology Kalyani, Phase 3, Block A, Kalyani, West Bengal 741235, India ' Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, Tamil Nadu, India

Abstract: Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.

Keywords: predictive analysis; unmanned aerial vehicle; UAV; smart agriculture; machine; learning; internet of things; IoT.

DOI: 10.1504/IJESMS.2023.127399

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.1, pp.16 - 23

Received: 19 Jul 2021
Accepted: 02 Sep 2021
Published online: 03 Dec 2022 *