A Flower Status Tracker and Self Irrigation System (FloTIS)

The Internet of Things (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 (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.

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Detaylı Görünüm
Eser Adı
(dc.title)
A Flower Status Tracker and Self Irrigation System (FloTIS)
Eser Sahibi
(dc.contributor.author)
Rumeysa KESKİN
Eser Sahibi
(dc.contributor.author)
Furkan GÜNEY
Eser Sahibi
(dc.contributor.author)
M. Erdal ÖZBEK
Yayın Tarihi
(dc.date.issued)
2021
Diğer Yazarlar
(dc.contributor.authors)
Rumeysa KESKİN
Yayıncı
(dc.publisher)
İzmir Katip Çelebi Üniversitesi
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
The Internet of Things (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 (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.
Kayıt Giriş Tarihi
(dc.date.accessioned)
06.06.2022
Açık Erişim Tarihi
(dc.date.available)
2022-06-06
Yayın Dili
(dc.language.iso)
eng
Konu Başlıkları
(dc.subject)
Automatic irrigation system
Konu Başlıkları
(dc.subject)
Deep learning
Konu Başlıkları
(dc.subject)
IoT
Atıf için Künye
(dc.identifier.citation)
R. Keskin , F. Güney ve M. E. Özbek , "A Flower Status Tracker and Self Irrigation System (FloTIS)", Journal of Artificial Intelligence and Data Science, c. 1, sayı. 1, ss. 45-50, Ağu. 2021
ISSN
(dc.identifier.issn)
2791-8335
Yayının ilk sayfa sayısı
(dc.identifier.startpage)
45
Yayının son sayfa sayısı
(dc.identifier.endpage)
50
Dergi Adı
(dc.relation.journal)
Journal of Artificial Intelligence and Data Science
Dergi Sayısı
(dc.identifier.issue)
1
Dergi Cilt
(dc.identifier.volume)
1
Haklar
(dc.rights)
Open access
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/11469/1928
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