Machine learning algorithms to classify water levels for smart irrigation systems

Abstract EN

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 high performance 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.