UAV-enabled approaches for irrigation scheduling and water body characterization

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

Agricultural Water Management, 2024, vol. 304, issue C

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.

Keywords: Canopy temperature; Crop monitoring; Machine learning; Precision agriculture; Sustainable; Water resources (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:304:y:2024:i:c:s037837742400427x

DOI: 10.1016/j.agwat.2024.109091

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