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IoT-Based Smart Soil Quality Monitoring and Classification for Sustainable Agriculture
Subject area: Science,Engineering and Technology · Area of research: IoT, Machine Learning, Smart Agriculture
DOI: https://doi.org/10.64388/IREV9I11-1717906
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.
References
[1] Shubha B. et al., “IoT-Based Soil Monitoring using Raspberry Pi,” 2025.
[2] Dr. R. Kalpana, “Machine Learning Approaches for Smart Irrigation,” 2024.
[3] M. Rajkumar, “CNN-Based Soil Classification Techniques,” 2025.
[4] S. J. Miller, “TensorFlow Lite for Edge Agriculture Systems,” 2025.
[5] L. Zhao et al., “Agentic AI and RAG in Smart Farming,” 2026.
[6] ESP32 Official Documentation.
[7] Arduino IDE Documentation.
[8] Scikit-learn Machine Learning Library Documentation.
[9] Research articles on Precision Agriculture and IoT-based Soil Monitoring.
How to cite this paper
@article{1717906,
author = {Rishab NS, Prof. Rakshitha B. S.},
title = {IoT-Based Smart Soil Quality Monitoring and Classification for Sustainable Agriculture},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2443-2455},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717906.pdf},
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.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717906}
}