Efficient water management in agriculture is crucial for sustainable food production, especially in regions facing water scarcity and climate variability. This research investigates the application of machine learning (ML) techniques to classify irrigation methods which includes: Overhead, Surface, and Precision Irrigation based on relevant agricultural data. The study evaluates the performance of four widely used ML models: Random Forest (RF), Support Vector Machine (SVM), XGBoost, and K-Nearest Neighbors (KNN), with the aim of identifying the most suitable model for accurate and consistent classification. Each model was trained and tested using labeled datasets and assessed through performance metrics including Precision, Recall, F1-Score, Accuracy, and Cohen’s Kappa. Confusion matrices and ROC-AUC curves were also utilized to visualize class-specific performance. The results indicate that XGBoost outperformed all other models, achieving the highest classification accuracy (86%) and a Kappa score of 0.79. It demonstrated superior performance across all irrigation classes, particularly excelling in the Precision Irrigation category. Random Forest followed closely, with an accuracy of 80% and Kappa of 0.70, notably achieving a perfect precision score for Overhead Irrigation. SVM delivered moderate performance with 72% accuracy and a Kappa of 0.57, while KNN lagged behind, scoring 64% accuracy and 0.46 Kappa. The comparative analysis highlights the effectiveness of ensemble-based methods, particularly XGBoost, for handling diverse and potentially non-linear agricultural datasets. The findings support the integration of advanced ML models in agricultural decision support systems, enabling more precise irrigation management and optimizing water resource utilization.

Irrigation Techniques Machine Learning Agricultural Data Analysis Classification Models