Voice Activated Drip irrigation: Merging technology and agriculture for smart watering
The integration of voice-activated technology with drip irrigation systems, complemented by plantmonitoring cameras, heralds a transformative era in agricultural practices. This innovative solution not only revolutionizes irrigation management but also introduces a sophisticated method for monitoring plant health and water usage, aiming to strike a balance between optimizing water resources and enhancing crop yield sustainably. By leveraging voice commands, farmers gain hands-free control over irrigation processes, allowing for intuitive adjustments to watering schedules and access to real-time updates on plant health. Moreover, the strategic placement of cameras enables the capture of vital plant data, which is then analysed using computer vision algorithms to assess various health indicators and soil moisture levels. This rich dataset empowers farmers with actionable insights, facilitating informed decision-making to optimize irrigation practices and ultimately improve crop yield and resource efficiency. The incorporation of smart algorithms further enhances precision by continuously adapting watering schedules based on factors such as plant and soil types, weather conditions, and seasonal variations. The voice recognition is implemented using a mobile application that transmits commands to the Arduino through Bluetooth. Crucially, the seamless integration of this system with existing farm infrastructure minimizes logistical hurdles, while comprehensive support services ensure successful implementation and adoption. In essence, this project represents a ground breaking fusion of technology and agriculture, paving the way for a more sustainable and productive future in farming
Institutions
- Jawaharlal Nehru Technological University, Hyderabad (IN)
- NRG Oncology (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-10-05
- DOI
- https://doi.org/10.5281/zenodo.14586303
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00