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Publication Type : Conference Paper
Publisher : IEEE
Source : 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)
Url : https://doi.org/10.1109/sceecs61402.2024.10482246
Campus : Faridabad
School : School of Artificial Intelligence
Year : 2024
Abstract : Traditional agriculture is under pressure to provide more food with fewer resources due to a growing global population and environmental issues. Maximizing crop productivity and quality requires efficient water management, precise nutrient delivery, and ideal environmental conditions. Traditional methods are imprecise, wasting water, over-fertilizing, and causing poor growth. This initiative tackles the real-world demand for a revolutionary agriculture that maximizes resource efficiency, minimizes environmental damage, and secures food sustainability. Advanced technologies like the ESP32 WROOM Dev Kit, TDS sensor, DHT11 humidity sensor, REES52 soil moisture sensor, piezo mist module, LDR module, 4-channel relay module, and water and fertilizer motors enable automated and precise irrigation, fertilization, cooling, and sunlight exposure. Using real- time soil moisture data from the REES52 sensor, a smart irrigation technique is developed.
Cite this Research Publication : Dakshita Thote, Vedanti Lanjewar, Vedant Sharma, Priyansh Agrawal, Vijay Kumar Soni, IoT and Machine Learning-Based Smart Soil Irrigation Farming Systems, 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), IEEE, 2024, https://doi.org/10.1109/sceecs61402.2024.10482246