An Integrated System for Detection and Prediction of Soil Health using IoT and Machine Learning Techniques

Authors

Kabelo Moroke, Tshwane University of Technology, South Africa

Tlou Ramabu, University of South Africa, South Africa

Khuliso Sigama, University of South Africa, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.JQZT6230

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Soil degradation due to climate change, nutrient depletion, and poor management endangers long-term agricultural productivity. Traditional soil testing is reliable; however, it is time-consuming and does not provide real-time feedback for precision farming. This study presents an integrated IoT-Machine Learning system for the early detection and prediction of soil health, utilizing a 7-in-1 multi-sensor array. The system monitors seven important parameters (pH, nitrogen, phosphorus, potassium, electrical conductivity, temperature, and humidity), assessed using supervised Machine Learning (ML) models (Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)). Training and evaluation were conducted using a hybrid dataset that combined Kaggle's Crop Recommendation data with field measurements. DT and RF achieved high accuracies (97.57% and 94.3%), with SHAP analysis confirming phosphorus, nitrogen, and potassium as the dominant determinants. NN established nonlinear connections (93.3%) but lacked interpretability, whereas SVM performed poorly (70.83%). The framework illustrates how explainable IoT-ML systems can provide actionable, transparent soil-health information, enabling data-driven precision agriculture decisions.

Keywords

Soil Health Prediction, Internet of Things, Machine Learning, SHAP Analysis, Precision Agriculture, Explainable AI

Cite this paper

K. Moroke, T. Ramabu and K. Sigama (2026) "An Integrated System for Detection and Prediction of Soil Health using IoT and Machine Learning Techniques", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.JQZT6230

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