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/v30i112639Abstract
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.
Cited by 0
No indexed citations yet.
Related research
- Relationships between Climate Parameters and Forest Vegetation At and Near Digya National Park, Ghana — shares topic coverage
- Evolution of Beninese Coastline from 1963 to 2005: Causes and Consequences — shares topic coverage
- Use of GIS and Remote Sensing as Risk Reduction Techniques in Disasters with Special Reference of India — shares topic coverage
- Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India — shares topic coverage
- Rainfall-Runoff Analysis using Runoff Coefficient and SCS-CN Methods under GIS Approach — shares topic coverage
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.