AGRI GUARD - Monitoring, Automating, and Sustaining your Crops
Over the years, technological innovations have transformed multiple sectors, with agriculture emerging as a crucial field that demands modernization. Despite significant advancements in farming techniques, many small-scale farmers still rely on traditional methods that lack precision, leading to inefficient resource utilization and unpredictable crop health. Addressing this challenge, Agri Guard presents an intelligent, sensor-based agricultural monitoring system that enhances soil health analysis and disease detection while optimizing water usage. This project integrates a soil moisture sensor to automate irrigation, ensuring that crops receive adequate water without wastage. A DHT22 sensor monitors temperature and humidity levels, enabling farmers to assess environmental conditions that affect plant growth. Additionally, a CO₂ sensor detects changes in microbial activity, which can indicate the early onset of plant diseases, providing preemptive alerts. A GSM module acts as a bridge between the farm and the farmer, sending real-time data and alerts directly to a mobile phone, ensuring prompt action can be taken when necessary. By leveraging Arduino-based automation, Agri Guard eliminates the need for constant manual intervention, reducing labor costs while improving overall farm efficiency. The system’s ability to provide real-time monitoring and automated responses makes it a valuable asset for farmers seeking to optimize productivity and safeguard crop health with minimal effort. This solution aligns with the modern vision of smart agriculture, where technology-driven farming practices ensure sustainability, efficiency, and improved yields.
Authors
- B Anupama (ORCID: https://orcid.org/0009-0004-0324-0306)
Institutions
- Institute of Management Technology (IN)
Publication Details
- Journal
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2025-04-29
- DOI
- https://doi.org/10.22214/ijraset.2025.67949
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00