Meena Kumari,
Akshansh Mishra,
Aryan Vashisth,
- Assistant Professor, Department of Computer Science Engineering, ABES Institute of Technology, Ghaziabad, India
- Student, Department of Computer Science Engineering (AI), ABES Institute of Technology, Ghaziabad, India
- Student, Department of Computer Science Engineering (AI), ABES Institute of Technology,, Ghaziabad, India
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This study provides an integrated approach to enhance irrigation systems by fusing machine learning (ML) with Internet of Things (IoT) technologies. Agriculture is a major contributor to India’s economy, which is referred to as the backbone of the country. However, its production is highly dependent on several environmental and agronomic factors, with water being a critical resource. Irrigation consumes about 84% of total available water in India; however, a significant portion is wasted due to poor management. We created a mechanism to automatically regulate water release valves based on the amount of soil moisture present in the crop’s root zone to overcome this difficulty. This system uses a predictive ML model that estimates more accurate irrigation durations. Furthermore, we integrated real-time soil sensors to collect critical data including nutrient level, pH, temperature, and rainfall, transmitting this information wirelessly to a mobile/web app. The app provides accurate real-time data and regulates valves to maintain optimal soil moisture levels. This innovative fusion of ML and IoT technologies empowers farmers with data-driven decision-making capabilities, ultimately leading to improved crop management and enhanced agricultural sustainability.
Keywords: Machine learning, internet of things, web app, automatic irrigation, soil moisture
[This article belongs to Journal of Electronic Design Technology (joedt)]
Meena Kumari, Akshansh Mishra, Aryan Vashisth. Enhancing Irrigation Efficiency through Machine Learning and IoT Integration. Journal of Electronic Design Technology. 2024; 15(03):18-24.
Meena Kumari, Akshansh Mishra, Aryan Vashisth. Enhancing Irrigation Efficiency through Machine Learning and IoT Integration. Journal of Electronic Design Technology. 2024; 15(03):18-24. Available from: https://journals.stmjournals.com/joedt/article=2024/view=0
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References
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Journal of Electronic Design Technology
| Volume | 15 |
| Issue | 03 |
| Received | 18/09/2024 |
| Accepted | 05/10/2024 |
| Published | 19/11/2024 |