Determination of fruit health status and yield with unmanned aerial vehicle

In this study, it is aimed to determine the number of reference fruits and health status (sturdy, rotten, mottled, non-spotted) by using real-time image or recorded video taken from the autonomous Unmanned Aerial Vehicle (UAV) camera in orchards. In the determinations made by using image processing techniques, sturdy-rotten and mottled-speckless distinction are made for oranges and apricots, respectively. These distinction and determination processes are carried out using highly trained classifiers. Three types of multi-trained classifiers performance have been compared and a highly trained classifier which has high performance has been preferred for object detection. The accuracy of the Haar, local binary pattern (LBP), and histogram of oriented gradients (HOG) classifiers are compared in Python using the open source computer vision library. It has been shown experimentally that Haar classifier achieves high performance in determining real-time reference fruit health status and yield.

Eser Adı
[dc.title]
Determination of fruit health status and yield with unmanned aerial vehicle
Yazar
[dc.contributor.author]
Kulu, Nükhet
Yazar
[dc.contributor.author]
Başkaya, Merve
Yazar
[dc.contributor.author]
Keleş, Aysel
Yazar
[dc.contributor.author]
Altan, Aytaç
Yazar
[dc.contributor.author]
Hacıoğlu, Rıfat
Yayın Yılı
[dc.date.issued]
2018
Yayıncı
[dc.publisher]
IEEE
Yayın Türü
[dc.type]
proceedings
Açıklama
[dc.description]
2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) -- OCT 19-21, 2018 -- Kizilcahamam, TURKEY
Açıklama
[dc.description]
WOS: 000467794200051
Özet
[dc.description.abstract]
In this study, it is aimed to determine the number of reference fruits and health status (sturdy, rotten, mottled, non-spotted) by using real-time image or recorded video taken from the autonomous Unmanned Aerial Vehicle (UAV) camera in orchards. In the determinations made by using image processing techniques, sturdy-rotten and mottled-speckless distinction are made for oranges and apricots, respectively. These distinction and determination processes are carried out using highly trained classifiers. Three types of multi-trained classifiers performance have been compared and a highly trained classifier which has high performance has been preferred for object detection. The accuracy of the Haar, local binary pattern (LBP), and histogram of oriented gradients (HOG) classifiers are compared in Python using the open source computer vision library. It has been shown experimentally that Haar classifier achieves high performance in determining real-time reference fruit health status and yield.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
unmanned aerial vehicle (UAV)
Konu Başlıkları
[dc.subject]
real time image processing
Konu Başlıkları
[dc.subject]
Haar algorithm
Konu Başlıkları
[dc.subject]
classification
Konu Başlıkları
[dc.subject]
harvest estimation
Künye
[dc.identifier.citation]
Kulu, N., Başkaya, M., Keleş, A., Altan, A. ve Hacioglu, R. (2018). Determination of fruit health status and yield with unmanned aerial vehicle. 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) içinde (ss. 1-4). 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), sunulmuş bildiri. doi:10.1109/ISMSIT.2018.8567042
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Sponsor YAYINCI
[dc.description.sponsorship]
IEEE Turkey Sect, Karabuk Univ, Kutahya Dumlupinar Univ
İlk Sayfa Sayısı
[dc.identifier.startpage]
290
Son Sayfa Sayısı
[dc.identifier.endpage]
293
Dergi Adı
[dc.relation.journal]
2018 2ND INTERNATIONAL SYMPOSIUM ON MULTIDISCIPLINARY STUDIES AND INNOVATIVE TECHNOLOGIES (ISMSIT)
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/2361
Tek Biçim Adres
[dc.identifier.uri]
doi:10.1109/ISMSIT.2018.8567042
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
39
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
18 Şubat 2024 18:50
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Tıklayınız
performance classifiers classifier compared highly trained distinction real-time status health reference experimentally object achieves determining preferred detection computer histogram source Python multi-trained oriented pattern binary vision library accuracy gradients (sturdy recorded non-spotted) mottled rotten
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