Current Volume 9
Abstract
Precision agriculture has become an essential approach for improving crop productivity and sustainable farming practices through intelligent monitoring systems. Real-time monitoring of soil parameters such as moisture, temperature, pH, and nutrient content enables farmers to make informed irrigation and fertilisation decisions. This paper proposes an IoT-based smart soil quality monitoring and classification framework using ESP32 edge devices, soil sensors, and Machine Learning techniques for sustainable agriculture. The system integrates soil moisture and environmental sensors with predictive analytics for real-time soil monitoring. Data preprocessing techniques are applied to remove noisy values and improve prediction accuracy. Random Forest, Decision Tree, and Logistic Regression algorithms are implemented for soil condition classification. The proposed system improves irrigation efficiency, reduces water wastage, and supports sustainable agriculture practices.
Keywords
Precision Agriculture, IoT, ESP32, Soil Moisture Monitoring, Machine Learning, Random Forest, Sustainable Agriculture.
Citations
IRE Journals:
Rishab NS, Prof. Rakshitha B. S. "IoT-Based Smart Soil Quality Monitoring and Classification for Sustainable Agriculture" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2443-2455 https://doi.org/10.64388/IREV9I11-1717906
IEEE:
Rishab NS, Prof. Rakshitha B. S.
"IoT-Based Smart Soil Quality Monitoring and Classification for Sustainable Agriculture" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717906
About IRE Journals
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