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Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield

Author

Listed:
  • Jehangir Arshad

    (Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • Musharraf Aziz

    (Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Asma A. Al-Huqail

    (Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Muhammad Hussnain uz Zaman

    (Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Muhammad Husnain

    (Department of Electrical and Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

A majority of the population of developing countries is associated with agriculture directly or indirectly. The liaison of engineering technology and Sustainable Development Goals (SDGs) can build a bridge for farmers to enhance their skills regarding advancements through future generation agriculture trends. The next-generation trends include better soil preparation, intelligent irrigation systems, advanced methods of crop nutrient inspection, smart fertilizers applications, and multi-cropping practices. This work proposes a smart Decision Support System (DSS) that acquires the input parameters based on real-time monitoring to optimize the yield that realizes sustainability by improving per hectare production and lessening water seepage wastage in agribusiness. The proposed model comprises three basic units including an intelligent sensor module, smart irrigation system and controlled fertilizer module. The system has integrated sensors, cloud employing decision support layers, and networking based DSS to recommend cautions for optimum sustainable yield. The intelligent sensors module contains a temperature and humidity sensor, NPK sensor, soil moisture sensor, soil conductivity sensor, and pH sensor to transmit the statistics to the cloud over the internet via Long Range (LoRa) using Serial Peripheral Interface (SPI) communication protocol. Moreover, an android application has been developed for real-time data monitoring according to GPS location and node information (accessed remotely). Furthermore, the DSS contemplates the accessible information from sensors, past patterns, monitoring climate trends and creating cautions required for sustainable fertilizer consumption. The presented results and comparison validate the novelty of the design as it embraces smart irrigation with smart control and smart decision-making based on accurate real-time field data. It is better than existing systems as it transmits the data over the LoRa that is an open-source communication with long-range transmission ability up to several kilometres. The sensor nodes helped in advancing the yield of crops, which resulted in achieving inclusive and sustainable economic goals.

Suggested Citation

  • Jehangir Arshad & Musharraf Aziz & Asma A. Al-Huqail & Muhammad Hussnain uz Zaman & Muhammad Husnain & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield," Sustainability, MDPI, vol. 14(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:827-:d:722969
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    References listed on IDEAS

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    1. Uwe Deichmann & Aparajita Goyal & Deepak Mishra, 2016. "Will digital technologies transform agriculture in developing countries?," Agricultural Economics, International Association of Agricultural Economists, vol. 47(S1), pages 21-33, November.
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    1. Yuan Liu & Xun He & Wanzhang Wang & Chenhui Zhu & Ruibo Jian & Jinfan Chen, 2022. "Agri-Environment Atmospheric Real-Time Monitoring Technology Based on Drone and Light Scattering," Agriculture, MDPI, vol. 12(11), pages 1-20, November.
    2. Honggang Wang & Peidong Pei & Ruoyu Pan & Kai Wu & Yu Zhang & Jinchao Xiao & Jingfeng Yang, 2022. "A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway," Mathematics, MDPI, vol. 10(21), pages 1-21, October.
    3. Jehangir Arshad & Ateeq Ur Rehman & Mohamed Tahar Ben Othman & Muhammad Ahmad & Hassaan Bin Tariq & Muhammad Abdullah Khalid & Muhammad Abdul Rehman Moosa & Muhammad Shafiq & Habib Hamam, 2022. "Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    4. Michail-Alexandros Kourtis & Michael Batistatos & Georgios Xylouris & Andreas Oikonomakis & Dimitris Santorinaios & Charilaos Zarakovitis & Ioannis Chochliouros, 2023. "Energy Efficiency in Agriculture through Tokenization of 5G and Edge Applications," Energies, MDPI, vol. 16(13), pages 1-16, July.
    5. Kayson M. Shurtz & Emily Dicataldo & Robert B. Sowby & Gustavious P. Williams, 2022. "Insights into Efficient Irrigation of Urban Landscapes: Analysis Using Remote Sensing, Parcel Data, Water Use, and Tiered Rates," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    6. Valentina Constanta Tudor & Toma Adrian Dinu & Marius Vladu & Dragoș Smedescu & Ionela Mituko Vlad & Eduard Alexandru Dumitru & Cristina Maria Sterie & Carmen Luiza Costuleanu, 2022. "Labour Implications on Agricultural Production in Romania," Sustainability, MDPI, vol. 14(14), pages 1-22, July.

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