Abstract :

Soil Moisture Detection Plays A Critical Role In Modern Agriculture, Environmental Monitoring, And Water Resource Management, As It Directly Influences Crop Yield, Irrigation Efficiency, And Soil Health. With The Increasing Demand For Precision Agriculture, Traditional Soil Moisture Measurement Techniques Such As Gravimetric Analysis, Tensiometers, And Resistive Sensors Face Limitations Related To Scalability, Cost, Maintenance, And Real-time Adaptability. Machine Learning–based Approaches Have Emerged As A Promising Alternative By Leveraging Historical Sensor Data, Meteorological Parameters, And Soil Characteristics To Accurately Estimate Soil Moisture Levels. In This Work, An Intelligent Soil Moisture Detection System Using The XGBoost Algorithm Is Proposed To Improve Prediction Accuracy And Robustness. XGBoost, An Advanced Gradient Boosting Technique, Is Well Suited For Handling Nonlinear Relationships, Missing Values, And High-dimensional Datasets Commonly Found In Agricultural Data. The Proposed Approach Integrates Data Preprocessing, Feature Selection, And Model Optimization To Deliver Reliable Soil Moisture Predictions Under Varying Environmental Conditions. Furthermore, The System Is Compared Conceptually With Conventional Approaches And Positioned As A Scalable Solution That Can Support Smart Irrigation Decisions. This Study Aims To Demonstrate How Machine Learning, Particularly XGBoost, Can Enhance Soil Moisture Detection, Reduce Water Wastage, And Contribute To Sustainable Agricultural Practices By Enabling Data-driven Decisionmaking.

Published:

17-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1227-1232

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite