A Flower Status Tracker and Self Irrigation System (FloTIS)

Authors : Rumeysa KESKİN, Furkan GÜNEY, M Erdal ÖZBEK

Pages : 45-50

View : 20 | Download : 8

Publication Date : 2021-08-30

Article Type : Research Paper

Abstract :The Internet of Things insert ignore into journalissuearticles values(IoT); provides solutions to many daily life problems. Smartphones with user-friendly applications make use of artificial intelligence solutions offered by deep learning techniques. In this work, we provide a sustainable solution to automatically monitor and control the irrigation process for detected flowers by combining deep learning and IoT techniques. The proposed flower status tracker and self-irrigation system insert ignore into journalissuearticles values(FloTIS); is implemented using a cloud-based server and an Android-based application to control the status of the flower which is being monitored by the local sensor devices. The system detects changes in the moisture of the soil and provides necessary irrigation for the flower. In order to optimize the water consumption, different classification algorithms are tested. The performance comparisons of similar works for example flower case denoted higher accuracy scores. Then the best generated deep learning model is deployed into the smartphone application that detects the flower type in order to determine the amount of water required for the daily irrigation for each type of flower. In this way, the system monitors water content in the soil and performs smart utilization of water while acknowledging the user.
Keywords : Automatic irrigation system, deep learning, IoT