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A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

Author

Listed:
  • Faris A. Almalki

    (Department of Computer Engineering, College of Computers, and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Ben Othman Soufiene

    (PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse 4023, Tunisia)

  • Saeed H. Alsamhi

    (Software Research Institute, Athlone Institute of Technology, N37 HD68 Athlone, Ireland
    Faculty of Engineering, IBB University, Ibb 70270, Yemen)

  • Hedi Sakli

    (MACS Research Laboratory, National Engineering School of Gabes, Gabes University, Gabes 6029, Tunisia
    EITA Consulting, 5 Rue du Chant des Oiseaux, 78360 Montesson, France)

Abstract

When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.

Suggested Citation

  • Faris A. Almalki & Ben Othman Soufiene & Saeed H. Alsamhi & Hedi Sakli, 2021. "A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs," Sustainability, MDPI, vol. 13(11), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5908-:d:561119
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    References listed on IDEAS

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    1. Nahina Islam & Md Mamunur Rashid & Faezeh Pasandideh & Biplob Ray & Steven Moore & Rajan Kadel, 2021. "A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    2. S. H. Alsamhi & F. A. Almalki & Ou Ma & M. S. Ansari & M. C. Angelides, 2019. "Correction to: Performance optimization of tethered balloon technology for public safety and emergency communications," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(1), pages 155-155, September.
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    Cited by:

    1. Mohammad Amiri-Zarandi & Mehdi Hazrati Fard & Samira Yousefinaghani & Mitra Kaviani & Rozita Dara, 2022. "A Platform Approach to Smart Farm Information Processing," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    2. Tian Tian & Li Li & Jing Wang, 2022. "The Effect and Mechanism of Agricultural Informatization on Economic Development: Based on a Spatial Heterogeneity Perspective," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    3. Faris A. Almalki & Maha Aljohani & Merfat Algethami & Ben Othman Soufiene, 2022. "Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model," Sustainability, MDPI, vol. 14(7), pages 1-24, March.
    4. Cristiano Fragassa & Giuliano Vitali & Luis Emmi & Marco Arru, 2023. "A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    5. Kosior, Katarzyna, 2023. "Projekty Badawczo-Rozwojowe Na Rzecz Rolnictwa Cyfrowego W Polsce," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2023(1).
    6. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.

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