Abstract:

Internet of Things (IoT) technology allows the control and management of systems independent of humans. Internet of things based agriculture applications have become widespread as a solution to the problems of food consumption and water scarcity in agriculture as the World population has increased gradually. Soil moisture is an important factor for agriculture production and hydrological cycles and the prediction of soil moisture is required in developing agricultural practices. In this study, an IoT-based irrigation system prototype is presented which consist of Esp8266 Wifi module, humidity and temperature, soil moisture, rain and ultraviolet sensors connected to the Arduino Uno board. Then, using the prototype system, data are collected from the pilot area determined in half-hour periods for 55 days and saved over the cloud with ThingSpeak. The soil moisture value is estimated by applying different machine learning regression models such as multiple linear, polynomial, support vector, decision tree and random forest regression using the collected data. The results obtained are compared according to the coefficient of determination and mean square error criteria to examine the success of these algorithms. The random forest regression model has found to be superior to other machine learning algorithms for soil moisture estimation.

Keywords: