Development of an Optimized Predictive Machine Learning Approach for Smart Irrigation System using Cuckoo Search Algorithm

The increasing global demand for water conservation has necessitated the development of intelligent irrigation systems. However, traditional irrigation systems often lack efficiency, leading to significant water waste. This study proposes an optimized predictive machine learning approach for smart irrigation system using Cuckoo Search Algorithm (CSA) that can be used to optimized irrigation scheduling and reduce water usage while maintaining crop yield. The system involves data collection, pre-processing, model training and optimization, WaterNet dataset is used with parameters like temperature, pH, turbidity and coliforms. The CSA is employed to optimize the hyperparameters of three machine learning model: Random Forest (RF) 99.07%, Support Vector Machine (SVM) 98.36% and K-Nearest Neighbors (KNN) 89.6%. The Optimized Random Forest (ORF) achieves exceptional performance, significantly improving prediction accuracy. CSA optimization was performed using different population sizes specifically n = 5, n = 10 and n = 20, larger populations yielded better behavior but required more iterations for convergence. In terms of convergence curve, CSA has fast convergence with fewer iterations (16) while PSO has slower convergence and more iterations (25 to 49), the difference is the time they took to converge, additionally, CSA-RF outperforms multilayer perceptron (MLP), due to hyperparameter tuning. Comparison between actual values (Ytest) with predictions from two models; Random Forest (Ypred.RF) shows deviations and misclassifications from actual value while optimized model (Ypred.optimized) predictions matched the actual values. The comparative analysis of classification methods highlights the superiority of the proposed ORF, which was fine-tuned using the CSA. The performance metrics which are accuracy, precision, recall, and F1-score achieved an exceptional score, demonstrating the robustness of the proposed approach, the ORF not only achieved excellent performance levels but also incorporated hyperparameter tuning, ensuring improved stability and reduced risk of overfitting. This study contributes to the development of efficient and sustainable smart irrigation systems, supporting precision agriculture and water conservation efforts.