Skip to content
Research Article Open access CC BY 4.0

Drones for Monitoring Soil Moisture and Optimizing Irrigation Scheduling in Horticultural Farms

Mohd Ashaq, Lalit Upadhyay, Lopamudra Jena, Vimal Kumar, Shatruhan Jaiswal, Juman Das, Reemashree Das, Anirban Dutta, Shivam Kumar Pandey, Bal Veer Singh

Journal of Scientific Research and Reports · pp. 1118–1135 · Published 28 Nov 2024

10.9734/jsrr/2024/v30i112639

Abstract

The efficient management of irrigation is crucial for the sustainability and productivity of horticultural farms. Traditional methods of monitoring soil moisture and scheduling irrigation can be labor-intensive and imprecise. The advent of unmanned aerial vehicles (UAVs), commonly known as drones, has opened up new possibilities for precision agriculture. Drones equipped with remote sensing technologies can provide high-resolution spatial and temporal data on soil moisture variability across a farm. This data can be used to optimize irrigation scheduling, leading to water savings, improved crop yields, and reduced environmental impact. This article reviews the current state of drone technology for soil moisture monitoring and irrigation management in horticulture. It discusses the principles of drone-based remote sensing, the types of sensors used, and the data processing and interpretation techniques involved. Case studies of successful applications of drones for irrigation optimization in various horticultural crops are presented. The article also addresses the challenges and limitations of drone-based irrigation management, including regulatory issues, data accuracy and resolution, and the need for specialized expertise. Future directions for research and development in this field are explored. With ongoing advancements in drone technology and data analytics, drones are poised to become an indispensable tool for precision irrigation management in horticulture.

Drones UAVs remote sensing soil moisture irrigation scheduling precision horticulture

Cited by 0

No indexed citations yet.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

0

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.