An artificial intelligence and Internet of things based automated irrigation system

• URL: http://arxiv.org/abs/2104.04076v1
• Date: Thu, 1 Apr 2021 21:05:26 GMT
• Title: An artificial intelligence and Internet of things based automated irrigation system
• Authors: \"Omer Aydin, Cem Ali Kandemir, Umut Kira\c{c}, Feri\c{s}tah Dalkili\c{c}
• Abstract summary: Internet of things (IoT) devices has begun to be used in all areas. Most of the operations and decisions about irrigation are carried out by people. Data collected from IoT devices and sensors sent via communication channels and stored on MongoDB.
• Score: 8.283810659689589
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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