Irrigation Demand Prediction Based on Environmental Sensor Data Using Machine Learning in Smart Agriculture Systems
Keywords:
Smart agriculture, irrigation prediction, machine learning, environmental sensors, Random Forest.Abstract
Irrigation management in agricultural systems often relies on fixed schedules or single threshold values, limiting the ability to fully adjust water supply to changing environmental conditions. The use of environmental sensors and machine learning in Smart Agriculture offers the opportunity to develop more adaptive prediction systems based on multiple simultaneous parameters. Objective: This study aims to develop and evaluate machine learning models to predict irrigation needs based on soil moisture, air temperature, and air humidity. Methods: An experimental quantitative approach was employed. Sensor data underwent preprocessing and irrigation-need labeling, followed by dataset splitting (80% training, 20% testing) and modeling using Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Model performance was evaluated using accuracy, precision, recall, and F1-score. Results: Based on the simulation data used, Random Forest achieved the highest performance, with an accuracy of 94.5%, precision of 94.2%, recall of 95.1%, and F1-score of 94.6%. SVM yielded an accuracy of 91.0% and an F1-score of 91.4%, while KNN achieved an accuracy of 87.5% and an F1-score of 87.8%. Conclusion: The results indicate that combining multiple environmental parameters can support more adaptive irrigation need predictions, with Random Forest demonstrating the most consistent performance across testing scenarios. However, as this study utilized simulation data, future research should incorporate field sensor data, perform empirical label calibration, and evaluate the models under diverse land and crop conditions. Keywords: Smart Agriculture, irrigation prediction, machine learning, environmental sensors, Random Forest.
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