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