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UAV-enabled approaches for irrigation scheduling and water body characterization

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  • Yadav, Manish
  • Vashisht, B.B.
  • Vullaganti, Niharika
  • Kumar, Prem
  • Jalota, S.K.
  • Kumar, Arun
  • Kaushik, Prashant

Abstract

In recent years, precision agriculture has seen a substantial increase in the use of unmanned aerial vehicles (UAVs). They have shown great potential in spraying, nutrient application, irrigation scheduling, field mapping, yield estimation, and crop monitoring. UAV-enabled approaches have transformed several industries, and they have enormous potential for irrigation water management and characterization of water reservoirs. This paper explores the use of UAVs for variable rate irrigation (VRI) which provides tailored irrigation based on crop water demand, weather conditions, and soil moisture levels using the indices viz canopy temperature, crop water stress index (CWSI), crop evapotranspiration, etc. UAV-VRI provides customized irrigation which increases crop yield and reduces total water uses by improving the water use efficiency. It further enables sustainable water resources management, particularly in water-scarce areas. UAVs offer versatile applications including mapping water quality, vegetation, and bathymetry of aquatic bodies such as lakes and reservoirs. The review highlights the advantages of UAVs over conventional techniques, including a cost-effective, high spatial and temporal resolution, frequent revisit time for irrigation scheduling and monitoring of water bodies which provide useful information for water resource managers and environmental researchers. However, It also discusses the challenges associated with UAVs such as legal issues, data processing, and the need for trained personnel. The massive amounts of data gathered by UAVs may be processed and analyzed using machine learning algorithms, enabling more effective and precise water management. The ongoing advancements in UAVs and machine learning ensure its potential for sustainable water resources management.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:304:y:2024:i:c:s037837742400427x
    DOI: 10.1016/j.agwat.2024.109091
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    1. Manish Yadav & B. B. Vashisht & S. K. Jalota & T. Jyolsna & Samar Pal Singh & Arun Kumar & Amit Kumar & Gurjeet Singh, 2024. "Improving Water Efficiencies in Rural Agriculture for Sustainability of Water Resources: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3505-3526, August.
    2. García-Tejero, I.F. & Rubio, A.E. & Viñuela, I. & Hernández, A & Gutiérrez-Gordillo, S & Rodríguez-Pleguezuelo, C.R. & Durán-Zuazo, V.H., 2018. "Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 208(C), pages 176-186.
    3. Bhatti, Sandeep & Heeren, Derek M. & Barker, J. Burdette & Neale, Christopher M.U. & Woldt, Wayne E. & Maguire, Mitchell S. & Rudnick, Daran R., 2020. "Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery," Agricultural Water Management, Elsevier, vol. 230(C).
    4. Hedley, C.B. & Yule, I.J., 2009. "A method for spatial prediction of daily soil water status for precise irrigation scheduling," Agricultural Water Management, Elsevier, vol. 96(12), pages 1737-1745, December.
    5. Chen, Assaf & Orlov-Levin, Valerie & Meron, Moshe, 2019. "Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management," Agricultural Water Management, Elsevier, vol. 216(C), pages 196-205.
    6. 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.
    7. Yang, Gaiqiang & Liu, Lei & Guo, Ping & Li, Mo, 2017. "A flexible decision support system for irrigation scheduling in an irrigation district in China," Agricultural Water Management, Elsevier, vol. 179(C), pages 378-389.
    8. Nahry, A.H. El & Ali, R.R. & Baroudy, A.A. El, 2011. "An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques," Agricultural Water Management, Elsevier, vol. 98(4), pages 517-531, February.
    9. Tainá T. Guimarães & Maurício R. Veronez & Emilie C. Koste & Luiz Gonzaga & Fabiane Bordin & Leonardo C. Inocencio & Ana Paula C. Larocca & Marcelo Z. De Oliveira & Dalva C. Vitti & Frederico F. Mauad, 2017. "An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    10. 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.
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