Soil Quality Assessment Using Machine Learning & IoT

Computer Engineering

Soil quality plays a crucial role in determining crop productivity and long-term agricultural sustainability. Conventional laboratory-based analysis, although accurate, is slow, costly, and unsuitable for continuous field monitoring. This study presents an integrated Internet of Things (IoT) and Machine Learning (ML) framework for real-time soil quality assessment. Low-cost IoT sensor nodes measure soil moisture, pH, temperature, electrical conductivity, and nutrient indicators, and transmit the data to a cloud platform for processing. Multiple ML models—including Support Vector Machine, Random Forest, and LightGBM—were trained to classify soil quality into predefined health categories. Experimental evaluation demonstrates that LightGBM achieves the highest accuracy, outperforming classical classifiers, while Random Forest provides robust performance under noisy conditions. The proposed system enables farmers to make timely decisions regarding irrigation and nutrient management, thereby supporting precision agriculture and improving yield outcomes.

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