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Su Ürünleri Yetiştiriciliği İçin Balık Davranışlarının Bilgisayarlı Görüntü İşleme Yöntemleriyle İzlenmesi

Yıl 2022, Cilt: 7 Sayı: 4, 568 - 581, 31.12.2022
https://doi.org/10.35229/jaes.1197703

Öz

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.

Kaynakça

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  • Al-Jubouri, Q., Al-Nuaimy, W., Al-Taee, M. & Young, I. (2017). An automated vision system for measurement of zebrafish length using low-cost orthogonal web cameras. Aquacultural Engineering, 78(B), 155-162. DOI: 10.1016/j.aquaeng.2017.07.003
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  • Anonim. (2020b). https://public.roboflow.com/object-detection/aquarium
  • Anonim. (2020c). https://public.roboflow.com/object-detection/brackish-underwater
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Monitoring of Fish Behaviors with Computerized Image Processing Methods for the Aquaculture

Yıl 2022, Cilt: 7 Sayı: 4, 568 - 581, 31.12.2022
https://doi.org/10.35229/jaes.1197703

Öz

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.

Kaynakça

  • Akhtar, M.T., Ali, S., Rashidi, H., Van Der Kooy, F., Verpoorte, R. & Richardson, M.K. (2013). Developmental effects of cannabinoids on zebrafish larvae. Zebrafish, 10(3), 283-293. DOI: 10.1089/zeb.2012.0785
  • Al-Jubouri, Q., Al-Nuaimy, W., Al-Taee, M. & Young, I. (2017). An automated vision system for measurement of zebrafish length using low-cost orthogonal web cameras. Aquacultural Engineering, 78(B), 155-162. DOI: 10.1016/j.aquaeng.2017.07.003
  • AlZu’bi, H., Al-Nuaimy, W., Buckley, J., Sneddon, L., & Young, I. (2015). Real-time 3D fish tracking and behaviour analysis. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2015. DOI: 10.1109/AEECT.2015.7360567
  • Anonim. (2010). Directive 2010/63/Eu of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes (Text with EEA relevance). Official Journal of the European Union, 10-20.
  • Anonim. (2014). https://swfscdata.nmfs.noaa.gov/labeled-fishes-in-the-wild
  • Anonim. (2020a). https://www.kaggle.com/datasets/crowww/a-large-scale-fish-dataset
  • Anonim. (2020b). https://public.roboflow.com/object-detection/aquarium
  • Anonim. (2020c). https://public.roboflow.com/object-detection/brackish-underwater
  • Anonim. (2020d). https://public.roboflow.com/object-detection/fish
  • Anonim. (2020e). https://public.roboflow.com/object-detection/shellfish-openimages
  • Anonim. (2022). Statistics of scientific procedures on living animals-GOV.UK. Retrieved October 21, 2022. https://www.gov.uk/government/collections/statistics-of-scientific-procedures-on-living-animals
  • Anwer, A., Ali, S.S.A., Khan, A. & Mériaudeau, F. (2017). Underwater 3D scanning using Kinect v2 time of flight camera. Thirteenth International Conference on Quality Control by Artificial Vision, 10338, 103380C. DOI: 10.1117/12.2266834
  • Banerjee, S., Alvey, L., Brown, P., Yue, S., Li, L. & Scheirer, W.J. (2021). An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor. Scientific Reports 11, 1002. DOI: 10.1038/s41598-020-79772-3
  • Barreiros, M.de O., Dantas, D.de O., Silva, L.C. de O., Ribeiro, S. & Barros, A.K. (2021). Zebrafish tracking using YOLOv2 and Kalman filter. Scientific Reports 11, 3219. DOI: 10.1038/s41598-021-81997-9
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Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Güray Tonguç 0000-0002-5476-7114

Beytullah Ahmet Balcı 0000-0002-6762-3259

Muhammed Nurullah Arslan 0000-0002-9322-6804

Erken Görünüm Tarihi 16 Aralık 2022
Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 1 Kasım 2022
Kabul Tarihi 19 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 7 Sayı: 4

Kaynak Göster

APA Tonguç, G., Balcı, B. A., & Arslan, M. N. (2022). Su Ürünleri Yetiştiriciliği İçin Balık Davranışlarının Bilgisayarlı Görüntü İşleme Yöntemleriyle İzlenmesi. Journal of Anatolian Environmental and Animal Sciences, 7(4), 568-581. https://doi.org/10.35229/jaes.1197703


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