Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems

Abstract

Agriculture is the main source of food. With thepassing of time, there are dangers in order to preserve on thefreshwater in agriculture sector. Thus, one of solutions to savethe freshwater is enhancing the wastewater. Machine learning(ML) algorithms are used in several applications, such as smartirrigation, to reduce freshwater loss via building highperformance ML algorithms. This paper proposes fouralgorithms: support vector machine (SVM), decision tree (DT),SVM with Adaboost, and DT with Adaboost to classify waterlevels of sprinklers for smart irrigation. Here, five levels ofwater are classified– Max, High, Medium, Low, and Stop. Theproposed algorithms are tested to obtain which algorithmachieves better performance and higher accuracy. Five stepssequentially are implemented on the used dataset via Pandasand Scikit-learn frameworks. The steps are preprocessing data,feature selection, feature scaling, training, and classification; toanalyze the performance of the algorithms. The results showedthat the DT algorithm with Adaboost is the best algorithmcompared to the rest of the algorithms. The DT algorithmachieves an accuracy score of 0.912 with a shorter testing timeof 2.2 seconds and mean square error (MSE) of 0.08.

Keywords

• multiple algorithms
• smart irrigation
• machine learning
• freshwater
• svm