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Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges

Author

Listed:
  • Parthasarathy Velusamy

    (Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, India)

  • Santhosh Rajendran

    (Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, India)

  • Rakesh Kumar Mahendran

    (Department of Electronics and Communication Engineering, Vel Tech Multitech Dr.Rangarajan Dr.Sakuthala Engineering College, Chennai 600062, India)

  • Salman Naseer

    (Department of Information Technology, University of the Punjab Gujranwala Campus, Gujranwala 52250, Pakistan)

  • Muhammad Shafiq

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

  • Jin-Ghoo Choi

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

Abstract

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

Suggested Citation

  • Parthasarathy Velusamy & Santhosh Rajendran & Rakesh Kumar Mahendran & Salman Naseer & Muhammad Shafiq & Jin-Ghoo Choi, 2021. "Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges," Energies, MDPI, vol. 15(1), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:217-:d:713809
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    Citations

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    Cited by:

    1. Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & Phathutshedzo Eugene Ratshiedana & Eric Economon & George Chirima & Sipho Sibanda, 2023. "Unmanned Aerial Vehicle (UAV) and Spectral Datasets in South Africa for Precision Agriculture," Data, MDPI, vol. 8(6), pages 1-14, May.
    2. Barbara Dobosz & Dariusz Gozdowski & Jerzy Koronczok & Jan Žukovskis & Elżbieta Wójcik-Gront, 2023. "Evaluation of Maize Crop Damage Using UAV-Based RGB and Multispectral Imagery," Agriculture, MDPI, vol. 13(8), pages 1-14, August.
    3. Mohammed Al-Naeem & M M Hafizur Rahman & Anuradha Banerjee & Abu Sufian, 2023. "Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    4. Mario Lillo-Saavedra & Alberto Espinoza-Salgado & Angel García-Pedrero & Camilo Souto & Eduardo Holzapfel & Consuelo Gonzalo-Martín & Marcelo Somos-Valenzuela & Diego Rivera, 2022. "Early Estimation of Tomato Yield by Decision Tree Ensembles," Agriculture, MDPI, vol. 12(10), pages 1-13, October.

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