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The utilisation of irrigation, one of the most water-intensive agricultural techniques worldwide, has grown in popularity over time. Smart irrigation methods are comparatively innovative technologies that improve domestic landscape irrigation plans, reducing water usage and potential contamination. The primary determinant of agricultural output is soil moisture, and irrigation management depends on a precise estimate of this criterion. The decision-making tool for Irrigation Schedule Prediction available in Machine Learning (ML) plays a crucial role. The drawbacks of traditional machine learning techniques are that they don't predict future values, reduce relative error, and decrease the effective estimation of soil moisture. To overcome the above-said drawbacks, we proposed a machine learning-based SupportTree Algae Algorithm (STAA) to presage soil wetness for smart irrigation. Our proposed STAA method consists of a hybrid approach of Support Vector Machine (SVM) and Decision Tree (DT)classifier incorporated with Artificial Algae Optimization. Our proposed method simulated and achieves 99.5% accuracy, 98.4% precision, 97.5% specificity, and 99.2% Sensitivity when compared with existing methods, namely Random Forest (RF), Fuzzy Logic, and K-Nearest Neighbour (KNN). As a result, a novel STAA method ensured better performance concerning Precision, Sensitivity, Accuracy, Relative Error, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Squared Logarithmic Error (MSLE), and Specificity over existing methods.
Suresh et al. (Tue,) studied this question.