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Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles

Author

Listed:
  • Luxon Nhamo

    (Water Research Commission of South Africa, 4 Daventry St, Lynnwood Manor, Pretoria 0081, South Africa
    Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa
    International Water Management Institute (IWMI-SA), 141 Cresswell St, Weavind Park, Silverton, Pretoria 0184, South Africa)

  • James Magidi

    (Geomatics Department, Tshwane University of Technology, Staatsartillerie Road, Pretoria 0001, South Africa)

  • Adolph Nyamugama

    (Agriculture Research Council Institute for Soil, Climate and Water (ARC-ISCW), Pretoria 0001, South Africa)

  • Alistair D. Clulow

    (Discipline of Agro-meteorology, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa)

  • Mbulisi Sibanda

    (Department of Geography, Environmental Studies and Tourism, University of the Western Cape (UWC), Robert Sobukwe Road, Bellville, Cape Town 7535, South Africa
    Discipline of Geography and Environmental Science, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa)

  • Vimbayi G. P. Chimonyo

    (Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa)

  • Tafadzwanashe Mabhaudhi

    (Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa)

Abstract

Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing.

Suggested Citation

  • Luxon Nhamo & James Magidi & Adolph Nyamugama & Alistair D. Clulow & Mbulisi Sibanda & Vimbayi G. P. Chimonyo & Tafadzwanashe Mabhaudhi, 2020. "Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:7:p:256-:d:379191
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    References listed on IDEAS

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    Cited by:

    1. Shaeden Gokool & Maqsooda Mahomed & Richard Kunz & Alistair Clulow & Mbulisi Sibanda & Vivek Naiken & Kershani Chetty & Tafadzwanashe Mabhaudhi, 2023. "Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    2. Yang, Ning & Zhang, Zhitao & Yang, Xiaofei & Dong, Ning & Xu, Qi & Chen, Junying & Sun, Shikun & Cui, Ningbo & Ning, Jifeng, 2025. "Evaluation of crop water status using UAV-based images data with a model updating strategy," Agricultural Water Management, Elsevier, vol. 312(C).
    3. Javier A. Quintana & Carlos Bordons & Sergio Esteban & Julian Delgado, 2025. "Hybrid Powerplant Design and Energy Management for UAVs: Enhancing Autonomy and Reducing Operational Costs," Energies, MDPI, vol. 18(12), pages 1-25, June.
    4. Nhamo, L. & Mpandeli, S. & Liphadzi, S. & Hlophe-Ginindza, S. & Kapari, M. & Molwantwa, J. & Mabhaudhi, Tafadzwanashe, . "Advances in water research: enhancing sustainable water use in irrigated agriculture in South Africa," Book Chapters,, International Water Management Institute.
    5. repec:iwt:jounls:h050853 is not listed on IDEAS
    6. repec:iwt:jounls:h050903 is not listed on IDEAS
    7. Yadav, Manish & Vashisht, B.B. & Vullaganti, Niharika & Kumar, Prem & Jalota, S.K. & Kumar, Arun & Kaushik, Prashant, 2024. "UAV-enabled approaches for irrigation scheduling and water body characterization," Agricultural Water Management, Elsevier, vol. 304(C).
    8. Lingfei Weng & Wentao Dou & Yejing Chen, 2023. "Study on the Coupling Effect of Agricultural Production, Road Construction, and Ecology: The Case for Cambodia," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    9. Yacoob, Ameera & Gokool, Shaeden & Clulow, Alistair & Mahomed, Maqsooda & Naiken, Vivek & Mabhaudhi, Tafadzwanashe, 2026. "A machine learning approach for quantifying crop water stress in smallholder farms using unmanned aerial vehicle multispectral imagery," Agricultural Water Management, Elsevier, vol. 324(C).
    10. Almasbek Maulit & Aliya Nugumanova & Kurmash Apayev & Yerzhan Baiburin & Maxim Sutula, 2023. "A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan," Data, MDPI, vol. 8(5), pages 1-13, May.
    11. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.

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