Open AccessMachine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley
Author(s) -
Anas H. Blasi,
Mohammad Ali Abbadi,
Rufaydah Alhuweimel
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.3944
Subject(s) - irrigation , water content , agricultural engineering , agriculture , environmental science , irrigation management , process (computing) , water resources , computer science , hydrology (agriculture) , water resource management , engineering , geography , ecology , geotechnical engineering , archaeology , biology , operating system
The agriculture sector is the most water-consuming sector. Due to the critical situation of available water resources in Jordan, attention should be paid to the issues of water demand and appropriate irrigation in order to spread the right management ways of modern irrigation to the farmers. The objectives of this paper are to improve the irrigation process and provide irrigation water to the highest possible extent through the use of artificial intelligence to construct a smart irrigation system that controls the irrigation mechanism using the necessary tools for sensing soil moisture and temperature, giving alerts of any change in the parameters entered as the baseline values for comparison, and installing system sensors buried at a depth of 3-5 inches below the roots to measure the moisture content in the soil. The sensors measure the humidity and temperature in the soil every ten minutes. They prevent the automatic irrigation process if the humidity is high, and permit it if the humidity is low. The smart automatic irrigation system model was built using the Decision Tree (DT) algorithm, which is a machine learning algorithm that trains the system on a part of the collected data to build the model that will be used to examine and predict the remaining data. The system had a prediction accuracy of 97.86%, which means that it may be successfully used in providing irrigation water for the agricultural sector.