  It is not hard to see that the need for clean water is growing by considering
the decrease of the water sources day by day in the world. Potable fresh water
is also used for irrigation, so it should be planned to decrease freshwater
wastage. With the development of technology and the availability of cheaper and
more effective solutions, the efficiency of irrigation increased and the water
loss can be reduced. In particular, Internet of things (IoT) devices has begun
to be used in all areas. We can easily and precisely collect temperature,
humidity and mineral values from the irrigation field with the IoT devices and
sensors. Most of the operations and decisions about irrigation are carried out
by people. For people, it is hard to have all the real-time data such as
temperature, moisture and mineral levels in the decision-making process and
make decisions by considering them. People usually make decisions with their
experience. In this study, a wide range of information from the irrigation
field was obtained by using IoT devices and sensors. Data collected from IoT
devices and sensors sent via communication channels and stored on MongoDB. With
the help of Weka software, the data was normalized and the normalized data was
used as a learning set. As a result of the examinations, a decision tree (J48)
algorithm with the highest accuracy was chosen and an artificial intelligence
model was created. Decisions are used to manage operations such as starting,
maintaining and stopping the irrigation. The accuracy of the decisions was
evaluated and the irrigation system was tested with the results. There are
options to manage, view the system remotely and manually and also see the
system s decisions with the created mobile application.
