Su Ürünleri Yetiştiriciliği İçin Balık Davranışlarının Bilgisayarlı Görüntü İşleme Yöntemleriyle İzlenmesi

Yıl 2022, Cilt: 7 Sayı: 4, 568 - 581, 31.12.2022

Güray Tonguç , Beytullah Ahmet Balcı , Muhammed Nurullah Arslan

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.

Anahtar Kelimeler

Balık davranışları, Görüntü işleme, İnvazif olmayan, Su ürünleri yetiştiriciliği, Tahribatsız teşhis

Monitoring of Fish Behaviors with Computerized Image Processing Methods for the Aquaculture

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.

Anahtar Kelimeler

Aquaculture, Fish behaviors, Image processing, Non-destructive detection, Non-invasive

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
• Baxendale, S., Holdsworth, C.J., Meza Santoscoy, P.L., Harrison, M.R.M., Fox, J., Parkin, C.A., Ingham, P.W. & Cunliffe, V.T. (2012). Identification of compounds with anti-convulsant properties in a zebrafish model of epileptic seizures. Disease Models & Mechanisms, 5(6), 773-784. DOI: 10.1242/dmm.010090
• Bradski, G. & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. https://books.google.com.tr/books/about/Learning_OpenCV.html?id=seAgiOfu2EIC&redir_esc=y
• Bruni, G., Rennekamp, A.J., Velenich, A., McCarroll, M., Gendelev, L., Fertsch, E., Taylor, J., Lakhani, P., Lensen, D., Evron, T., Lorello, P.J., Huang, X-P., Kolczewski, S., Carey, G., Caldarone, B.J., Prinssen, E., Roth, B.L., Keiser, M.J., Peterson, R.T. & Kokel, D. (2016). Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds. Nature Chemical Biology, 12, 559-566. DOI: 10.1038/nchembio.2097
• Chen, Q., Zhang, C., Zhao, J. & Ouyang, Q. (2013). Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety. TrAC Trends in Analytical Chemistry, 52, 261-274. DOI: 10.1016/j.trac.2013.09.007
• Chuang, M-C., Hwang, J-N. & Williams, K. (2016). A feature learning and object recognition framework for underwater fish images. IEEE Transactions on Image Processing, 25(4), 1862-1872. DOI: 10.1109/TIP.2016.2535342
• Cui, S., Zhou, Y., Wang, Y. & Zhai, L. (2020). Fish detection using deep learning. Applied Computational Intelligence and Soft Computing. DOI: 10.1155/2020/3738108
• Di Paolo, C., Seiler, T.B., Keiter, S., Hu, M., Muz, M., Brack, W. & Hollert, H. (2015). The value of zebrafish as an integrative model in effect-directed analysis-A review. Environmental Sciences Europe, 27, 1-11. DOI: 10.1186/s12302-015-0040-y
• Ditria, E.M., Jinks, E.L. & Connolly, R.M. (2021). Automating the analysis of fish grazing behaviour from videos using image classification and optical flow. Animal Behaviour, 177, 31-37. DOI: 10.1016/j.anbehav.2021.04.018
• Egan, R.J., Bergner, C.L., Hart, P.C., Cachat, J.M., Canavello, P.R., Elegante, M.F., Elkhayat, S.I., Bartels, B.K., Tien, A.K., Tien, D.H., Mohnot, S., Beeson, E., Glasgow, E., Amri, H., Zukowska, Z. & Kalueff, A.V. (2009). Understanding behavioral and physiological phenotypes of stress and anxiety in zebrafish. Behavioural Brain Research, 205(1), 38-44. DOI: 10.1016/j.bbr.2009.06.022
• Eldrogi, N., Altherany, I. & Alqaddafi, S. (2019). Automatic fish tracking by kalman filter. Journal of Pure & Applied Sciences, 18(4), 20-23. DOI: 10.51984/jopas.v18i4.377
• Feijó, G.de O., Sangalli, V.A., da Silva, I.N.L. & Pinho, M.S. (2018). An algorithm to track laboratory zebrafish shoals. Computers in Biology and Medicine, 96, 79-90. DOI: 10.1016/j.compbiomed.2018.01.011
• Gao, H., Zhu, F., & Cai, J. (2010). A review of non-destructive detection for fruit quality. International Conference on Computer and Computing Technologies in Agriculture CCTA 2009: Computer and Computing Technologies in Agriculture III, IFIP Advances in Information and Communication Technology (IFIPAICT), 317, 133-140. DOI: 10.1007/978-3-642-12220-0_21
• Gao, Y., Chan, R.H.M., Chow, T.W.S., Zhang, L., Bonilla, S., Pang, C-P., Zhang, M. & Leung, Y.F. (2014). A high-throughput zebrafish screening method for visual mutants by light-induced locomotor response. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(4), 693-701. DOI: 10.1109/TCBB.2014.2306829
• Genç, M. (2011). Dikili durumdaki ağaçların iç kısımlarında oluşan ve belirgin bir emare göstermeyen çürük ve boşlukların tahribatsız belirlenmesi. I. Ulusal Akdeniz Orman ve Çevre Sempozyumu, 26-28 Ekim 2011, Kahramanmaraş, Bildiriler Kitabı, 306-314. https://www.academia.edu/6001195/Dikili_Durumdaki_Ağaçların_İç_Kısımlarında_Oluşan_ve_Belirgin_Bir_Emare_Göstermeyen_Çürük_ve_Boşlukların_Tahribatsız_Belirlenmesi
• González-Rivero, M., Beijbom, O., Rodriguez-Ramirez, A., Bryant, D.E.P., Ganase, A., Gonzalez-Marrero, Y., Herrera-Reveles, A., Kennedy, E.V., Kim, C.J.S., Lopez-Marcano, S., Markey, K., Neal, B.P., Osborne, K., Reyes-Nivia, C., Sampayo, E.M., Stolberg, K., Taylor, A., Vercelloni, J., Wyatt, M., & Hoegh-Guldberg, O. (2020). Monitoring of coral reefs using artificial ıntelligence: A feasible and cost-effective approach. Remote Sensing, 12(3), 489. DOI: 10.3390/rs12030489
• Gray, S.M., Bieber, F.M.E., Mcdonnell, l.H., Chapman, l.J. & Mandrak, N.E. (2014). Experimental evidence for species-specific response to turbidity in imperilled fishes. Aquatic Conservation: Marine and Freshwater Ecosystems, 24(4), 546-560. DOI: 10.1002/aqc.2436
• Gray, S.M., Sabbah, S. & Hawryshyn, C.W. (2011). Experimentally increased turbidity causes behavioural shifts in Lake Malawi cichlids. Ecology of Freshwater Fish, 20(4), 529-536. DOI: 10.1111/j.1600-0633.2011.00501.x
• Kitasato, A., Miyazaki, T., Sugaya, Y. & Omachi, S. (2018). Automatic discrimination between Scomber japonicus and Scomber australasicus by geometric and texture features. Fishes, 3(3), 26. DOI: 10.3390/fishes3030026
• Knausgård, K.M., Wiklund, A., Sørdalen, T.K., Halvorsen, K.T., Kleiven, A.R., Jiao, L. & Goodwin, M. (2022). Temperate fish detection and classification: a deep learning based approach. Applied Intelligence, 52(6), 6988-7001. DOI: 10.1007/s10489-020-02154-9
• Koçer, H.E. & Çevik, K.K. (2021). Deep neural networks based wrist print region segmentation and classification. MANAS Journal of Engineering, 9(1), 30-36. DOI: 10.51354/mjen.853971
• Kokel, D., Cheung, C.Y.J., Mills, R., Coutinho-Budd, J., Huang, L., Setola, V., Sprague, J., Jin, S., Jin, Y.N., Huang, X-P., Bruni, G., Woolf, C.J., Roth, B.L., Hamblin, M.R., Zylka, M.J., Milan, D.J. & Peterson, R. T. (2013). Photochemical activation of TRPA1 channels in neurons and animals. Nature Chemical Biology, 9, 257-263. DOI: 10.1038/nchembio.1183
• Labao, A.B. & Naval Jr., P.C. (2019). Simultaneous localization and segmentation of fish objects using multi-task CNN and dense CRF. Asian Conference on Intelligent Information and Database Systems ACIIDS 2019: Intelligent Information and Database Systems, Lecture Notes in Computer Science, 11431, 600-612. DOI: 10.1007/978-3-030-14799-0_52
• Lawrence, C. (2007). The husbandry of zebrafish (Danio rerio): A review. Aquaculture, 269(1-4), 1-20. DOI: 10.1016/j.aquaculture.2007.04.077
• Levin, E.D., Bencan, Z. & Cerutti, D.T. (2007). Anxiolytic effects of nicotine in zebrafish. Physiology & Behavior, 90(1), 54-58. DOI: 10.1016/j.physbeh.2006.08.026
• Levy, D., Belfer, Y., Osherov, E., Bigal, E., Scheinin, A.P., Nativ, H., Tchernov, D. & Treibitz, T. (2018). Automated analysis of marine video with limited data. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1466-1474. DOI: 10.1109/CVPRW.2018.00187
• Linney, E., Upchurch, L. & Donerly, S. (2004). Zebrafish as a neurotoxicological model. Neurotoxicology and Teratology, 26(6), 709-718. DOI: 10.1016/j.ntt.2004.06.015
• Liu, Y., Ma, P., Cassidy, P.A., Carmer, R., Zhang, G., Venkatraman, P., Brown, S.A., Pang, C.P., Zhong, W., Zhang, M. & Leung, Y. F. (2017). Statistical analysis of zebrafish locomotor behaviour by generalized linear mixed models. Scientific Reports, 7, 2937. DOI: 10.1038/s41598-017-02822-w
• MacPhail, R.C., Brooks, J., Hunter, D.L., Padnos, B., Irons, T.D. & Padilla, S. (2009). Locomotion in larval zebrafish: Influence of time of day, lighting and ethanol. Neurotoxicology, 30(1), 52-58. DOI: 10.1016/j.neuro.2008.09.011
• Man, M., Abdullah, N., Rahim, M.S.M. & Amin, I.M. (2016). Fish length measurement: The results from different types of digital camera. Journal of Advanced Agricultural Technologies, 3(1), 67-71. DOI: 10.18178/joaat.3.1.67-71
• Maximino, C., Da Silva, A.W.B., Arauj́o, J., Lima, M.G., Miranda, V., Puty, B., Benzecry, R., Picanço-Diniz, D.L.W., Gouveia Jr., A.G., Oliviera, K.R.M. & Herculano, A.M. (2014). Fingerprinting of psychoactive drugs in zebrafish anxiety-like behaviors. PLOS ONE, 9(7), e103943. DOI: 10.1371/journal.pone.0103943
• Maximino, C., de Brito, T.M., da Silva Batista, A.W., Herculano, A.M., Morato, S. & Gouveia, A. (2010). Measuring anxiety in zebrafish: A critical review. Behavioural Brain Research, 214(2), 157-171. DOI: 10.1016/j.bbr.2010.05.031
• Monkman, G.G., Hyder, K., Kaiser, M.J. & Vidal, F.P. (2019). Using machine vision to estimate fish length from images using regional convolutional neural networks. Methods in Ecology and Evolution, 10(12), 2045-2056. DOI: 10.1111/2041-210X.13282
• Nath, A.K., Roberts, L.D., Liu, Y., Mahon, S.B., Kim, S., Ryu, J.H., Werdich, A., Januzzi, J.L., Boss, G.R., Rockwood, G.A., MacRae, C.A., Brenner, M., Gerszten, R.E. & Peterson, R.T. (2013). Chemical and metabolomic screens identify novel biomarkers and antidotes for cyanide exposure. The FASEB Journal, 27(5), 1928-1938. DOI: 10.1096/fj.12-225037
• Niu, B., Li, G., Peng, F., Wu, J., Zhang, L. & Li, Z. (2018). Survey of Fish Behavior Analysis by Computer Vision. Journal of Aquaculture Research & Development, 9(5). DOI: 10.4172/2155-9546.1000534
• Papadakis, V.M., Papadakis, I.E., Lamprianidou, F., Glaropoulos, A. & Kentouri, M. (2012). A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering, 46, 53-59. DOI: 10.1016/j.aquaeng.2011.11.002
• Pérez, D., Ferrero, F.J., Alvarez, I., Valledor, M. & Campo, J.C. (2018). Automatic measurement of fish size using stereo vision. 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 1-6. DOI: 10.1109/I2MTC.2018.8409687
• Petrellis, N. (2021). Measurement of fish morphological features through image processing and deep learning techniques. Applied Sciences, 11(10), 4416. DOI: 10.3390/app11104416
• Prober, D.A., Rihel, J., Onah, A.A., Sung, R.J. & Schier, A.F. (2006). Hypocretin/orexin overexpression induces an insomnia-like phenotype in zebrafish. The Journal of Neuroscience, 26(51), 13400-13410. DOI: 10.1523/JNEUROSCI.4332-06.2006
• Pylatiuk, C., Zhao, H., Gursky, E., Reischl, M., Peravali, R., Foulkes, N. & Loosli, F. (2019). DIY automated feeding and motion recording system for the analysis of fish behavior. Technology Briefs, 24(4), 394-398. DOI: 10.1177/2472630319841412
• Qian, Z-M., Cheng, X.E. & Chen, Y.Q. (2014). Automatically detect and track multiple fish swimming in shallow water with frequent occlusion. PLOS ONE, 9(9), e106506. DOI: 10.1371/journal.pone.0106506
• Rao, R.M. & Arora, M.K. (2004). Overview of image processing. Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, 51-85. DOI: 10.1007/978-3-662-05605-9_3
• Rico-Díaz, Á.J., Rabuñal, J.R., Gestal, M., Mures, O.A. & Puertas, J. (2020). An application of fish detection based on eye search with artificial vision and artificial neural networks. Water, 12(11), 3013. DOI: 10.3390/w12113013
• Rihel, J., Prober, D.A., Arvanites, A., Lam, K., Zimmerman, S., Jang, S., Haggarty, S.J., Kokel, D., Rubin, L.L., Peterson, R.T. & Schier, A.F. (2010). Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science, 327(5963), 348-351. DOI: 10.1126/science.1183090
• Safaei, N., Smadi, O., Masoud, A. & Safaei, B. (2022). An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification. International Journal of Pavement Research and Technology, 15, 159-172. DOI: 10.1007/s42947-021-00006-4
• Stewart, A., Gaikwad, S., Kyzar, E., Green, J., Roth, A. & Kalueff, A.V. (2012). Modeling anxiety using adult zebrafish: A conceptual review. Neuropharmacology, 62(1), 135-143. DOI: 10.1016/j.neuropharm.2011.07.037
• Stewart, A., Maximino, C., De Brito, T.M., Herculano, A.M., Gouveia Jr., A., Morato, S., Cachat, J.M., Gaikwad, S., Elegante, M.F., Hart, P.C. & Kalueff, A.V. (2011). Neurophenotyping of adult zebrafish using the light/dark box paradigm. Neuromethods, 51, 157-167. DOI: 10.1007/978-1-60761-953-6_13
• Torjesen, I. (2014). Number of animals used in science increased slightly in 2013, Home Office reports. BMJ, 349, g4586. DOI: 10.1136/bmj.g4586
• van der Sluijs, I., Gray, S.M., Amorim, M.C.P., Barber, I., Candolin, U., Hendry, A.P., Krahe, R., Maan, M.E., Utne-Palm, A.C., Wagner H.J. & Wong, B.B.M. (2011). Communication in troubled waters: Responses of fish communication systems to changing environments. Evolutionary Ecology, 25, 623-640. DOI: 10.1007/s10682-010-9450-x
• Wang, S.H., Cheng, X.E., Qian, Z-M., Liu, Y. & Chen, Y.Q. (2016). Automated planar tracking the waving bodies of multiple zebrafish swimming in shallow water. PLOS ONE, 11(4): e0154714. DOI: 10.1371/journal.pone.0154714
• Xia, Y., Xu, Y., Li, J., Zhang, C. & Fan, S. (2019). Recent advances in emerging techniques for nondestructive detection of seed viability: A review. Artificial Intelligence in Agriculture, 1, 35-47. DOI: 10.1016/j.aiia.2019.05.001
• Xu, W., Zhu, Z., Ge, F., Han, Z., & Li, J. (2020). Analysis of behavior trajectory based on deep learning in ammonia environment for fish. Sensors, 20(16), 4425. DOI: 10.3390/s20164425
• Zhang, H., Wu, J., Yu, H., Wang, W., Zhang, Y. & Zhou, Y. (2021). An underwater fish individual recognition method based on improved YoloV4 and FaceNet. 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), 196-200. DOI: 10.1109/IUCC-CIT-DSCI-SMARTCNS55181.2021.00042
• Zhang, L., Xiang, L., Liu, Y., Venkatraman, P., Chong, L., Cho, J., Bonilla, S., Jin, Z-B., Pang, C.P., Ko, K.M., Ma, P., Zhang, M. & Leung, Y.F. (2016). A naturally derived compound schisandrin B enhanced light sensation in the pde6c zebrafish model of retinal degeneration. PLOS ONE, 11(3), e0149663. DOI: 10.1371/journal.pone.0149663
• Zhao, X., Yan, S. & Gao, Q. (2019). An algorithm for tracking multiple fish based on biological water quality monitoring. IEEE Access, 7, 15018-15026. DOI: 10.1109/ACCESS.2019.2895072
• Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. Proceedings-International Conference on Pattern Recognition, 2, 28-31. DOI: 10.1109/ICPR.2004.1333992
• Zivkovic, Z. & Van Der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, 27(7), 773-780. DOI: 10.1016/j.patrec.2005.11.005