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An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field

Author

Listed:
  • Tabassum Kanwal

    (University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi 43600, Pakistan)

  • Saif Ur Rehman

    (University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi 43600, Pakistan)

  • Tariq Ali

    (University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi 43600, Pakistan)

  • Khalid Mahmood

    (Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Santos Gracia Villar

    (Research Group on Food, Nutritional Biochemistry, and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Department of Projects, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Department of Projects, Universidade Internacional do Cuanza, Cuito EN250, Angola)

  • Luis Alonso Dzul Lopez

    (Research Group on Food, Nutritional Biochemistry, and Health, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
    Department of Projects, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
    Department of Projects, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA)

  • Imran Ashraf

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

Abstract

Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved.

Suggested Citation

  • Tabassum Kanwal & Saif Ur Rehman & Tariq Ali & Khalid Mahmood & Santos Gracia Villar & Luis Alonso Dzul Lopez & Imran Ashraf, 2023. "An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1600-:d:1216277
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    References listed on IDEAS

    as
    1. Mirza, Adeel Feroz & Mansoor, Majad & Zhan, Keyu & Ling, Qiang, 2021. "High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance," Energy, Elsevier, vol. 228(C).
    2. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    3. Refaat, Ahmed & Osman, Mohamed Hassan & Korovkin, Nikolay V., 2020. "Current collector optimizer topology to extract maximum power from non-uniform aged PV array," Energy, Elsevier, vol. 195(C).
    4. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    5. Ali M. Eltamaly, 2021. "An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-26, February.
    6. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    7. Prasanth Ram, J. & Rajasekar, N., 2017. "A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)," Energy, Elsevier, vol. 118(C), pages 512-525.
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