TY  - JOUR
T1  - Su Ürünleri Yetiştiriciliği İçin Balık Davranışlarının Bilgisayarlı Görüntü İşleme  Yöntemleriyle İzlenmesi
TT  - Monitoring of Fish Behaviors with Computerized Image Processing Methods for the Aquaculture
AU  - Balcı, Beytullah Ahmet
AU  - Tonguç, Güray
AU  - Arslan, Muhammed Nurullah
PY  - 2022
DA  - December
Y2  - 2022
DO  - 10.35229/jaes.1197703
JF  - Journal of Anatolian Environmental and Animal Sciences
JO  - JAES
PB  - Bülent VEREP
WT  - DergiPark
SN  - 2548-0006
SP  - 568
EP  - 581
VL  - 7
IS  - 4
LA  - tr
AB  - Hayvan davranışlarının izlenip, yorumlanarak faydalı bilgiler haline getirilmesi son yıllarda önem kazanan konulardan birisi olmuştur. Makine öğrenmesi ve derin öğrenme algoritmaları gibi yazılımsal gelişmeler, görüntüleme cihazları ve elde edilen görüntülerin işlenmesine imkân tanıyan donanımsal gelişmeler, hayvan davranışlarının izlenmesine altyapı oluşturmaktadır. Özellikle insanlarla sesli veya fiziki etkileşim yeteneği bulunmayan balıkların yaşam alanlarında temassız ve tahribatsız izlenmesi, bu teknolojiler sayesinde mümkün olabilmektedir. Alternatif türlerin yoğun akuakültüre kazandırılmasında karşılaşılan problemlerin başında canlının biyotik ve abiyotik gereksinimlerinin bilinmemesi gelmektedir. Bu çalışmada görüntü işleme yöntemleri ile, balıkların günlük yaşamları, bakımları, beslemeleri, bazı deneysel işlemlerin yapılması, bireysel veya sürü hareketleri, bu hareketlerin izlenmesi için oluşturulmuş donanımsal ve yazılımsal düzenekler ile ilgili yapılan çalışmalar hakkında bilgiler verilmiştir.  Ayrıca, düzeneklerde kullanılan balıklar ve deney prosedürleri, elde edilen görüntülerin işlenme yöntemleri, kullanılan istatistiksel yöntemler ve sonuçlarda ele alınmıştır. Bu makalede, su ürünleri yetiştiriciliği sektörü için kullanılabilecek görüntü işleme alanındaki çalışmalar incelenip sunulmuştur.
KW  - Balık davranışları
KW  - Görüntü işleme
KW  - İnvazif olmayan
KW  - Su ürünleri yetiştiriciliği
KW  - Tahribatsız teşhis
N2  - Observing and interpreting animal behaviors and turning them into useful information has become an issue that has gained importance in recent years. Software developments such as machine learning and deep learning algorithms, imaging devices, and hardware developments allow the processing of obtained images from the infrastructure for monitoring animal behavior. Thanks to these technologies, non-contact and non-destructive detection of fish, which cannot interact with people verbally or physically, in their habitats is possible. One of the problems encountered in introducing alternative species into intensive aquaculture is the lack of knowledge of the biotic and abiotic requirements of the living thing. This study gives information about the image processing methods, the daily life of fish, their care, feeding, some experimental procedures, individual or swarm movements, and the hardware and software mechanisms created to monitor these movements. In addition, the fish used in the setups and the experimental procedures, the processing methods of the images obtained, the statistical techniques used, and the results are discussed. This manuscript reviews and presents studies in the field of image processing that can be used for the aquaculture sector.
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UR  - https://doi.org/10.35229/jaes.1197703
L1  - https://dergipark.org.tr/en/download/article-file/2743737
ER  -