Drones for Monitoring Soil Moisture and Optimizing Irrigation Scheduling in Horticultural Farms
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.
Authors
- Anirban Dutta (ORCID: https://orcid.org/0000-0002-7298-9773)
- Lalit Upadhyay (ORCID: https://orcid.org/0000-0002-0573-3823)
- Shivam Kumar Pandey (ORCID: https://orcid.org/0009-0005-2462-7961)
- Bal Veer Singh
- Vimal Kumar (ORCID: https://orcid.org/0009-0004-6264-8619)
- Shatruhan Jaiswal
- Reemashree Das
- Juman Das
- Mohd Ashaq
- Lopamudra Jena
Institutions
- Rashtriya Raksha University (IN)
- Assam Agricultural University (IN)
- Central Agricultural University (IN)
Publication Details
- Journal
- Journal of Scientific Research and Reports
- Published
- 2024-11-28
- DOI
- https://doi.org/10.9734/jsrr/2024/v30i112639
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00