A Comparison of Image Segmentation and Image Non-Segmentation to Classify Average Weight of Red Tilapia Using Machine Learning Techniques
Abstract
In this study, the segmented images of the upper body of fish near the water surface in which the fins, tail, and background were removed (5 fish per image) were compared with non-segmented images (original images) using machine learning techniques for weight estimation of red tilapia. The images used consisted of 3 groups of fish with an average weight of 300–500, 501-700, and 701–900 g/fish, with 48 images in each group. Decision Tree, K-nearest Neighbor, Naïve Bayes, Support Vector Machine, and Deep Learning models were applied to predict the average weight of the fish in images. The results showed that using the original images had better average results of accuracy, precision, and recall than using the segmented images, and there was a statistical difference (P < 0.05). The average accuracy, precision, and recall for the original images were 80.97±3.35, 81.23±3.41, and 82.17±5.26 percent, respectively. While the results of the segmented images were 59.85±3.45, 61.03±4.49, and 59.58±3.45 percent, respectively. In the case of the model used, the deep learning model was found to provide the highest accuracy average because it is a highly effective model to deal with complex data sets. The results of this study showed that the original images could be used for red tilapia weight estimation with high accuracy and faster processing than the segmented images. Keywords: weight estimation; machine learning; image classification; red tilapiaReferences
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Azaza, M.S., Dhraïef, M.N., & Kraïem, M.M. (2008). Effects of water temperature on growth and sex ratio of juvenile Nile tilapia Oreochromis niloticus (Linnaeus) reared in geothermal waters in southern Tunisia. Journal of Thermal Biology, 33(2), 98-105.
Balaban, M.O., Sengor, G.F.U., Soriano, M.G., & Ruiz, E.G. (2010). Using image analysis to predict the weight of Alaskan salmon of different species. Journal of Food Science Food Sci, 75(3), 157-162.
Chugh, R.S., Bhatia, V., Khanna, K., & Bhatia, V. (2020). A Comparative Analysis of Classifiers for Image Classification. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 248-253.
Cifuentes-Alcobendas, G., & Domínguez-Rodrigo, M. (2019). Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks. Scientific Reports, 9(1), 18933.
Fernandes, A.F.A., Turra, E.M., Alvarenga, É.R.d., Passafaro, T.L., Lopes, F.B., Alves, G.F.O., Singh, V., & Rosa, G.J.M. (2020). Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Computers and Electronics in Agriculture, 170.
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Jongjaraunsuk, R., & Taparhudee, W. (2021). Weight Estimation of Asian Sea Bass (Lates calcarifer) Comparing Whole Body with and without Fins using Computer Vision Technique. Walailak Journal of Science and Technology (WJST), 18(10).
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Medjahed, S. A. (2015). A Comparative Study of Feature Extraction Methods in Images Classification. International Journal of Image, Graphics and Signal Processing, 7(3), 16-23.
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Pickering, A.D. & Christie, P. (1981). Changes in the concentrations of plasma cortisol and thyroxine during sexual maturation of the hatchery-reared brown trout, Salmo trutta L. General and Comparative Endocrinologyen Comp Endocrinol. 44(4), 487-96.
Riche, M., Haley, D.I., Oetker, M., Garbrecht, S., & Garling, D.L. (2004). Effect of feeding frequency on gastric evacuation and the return of appetite in tilapia Oreochromis niloticus (L.). Aquaculture, 234(1-4), 657-673.
Ridha, M.T. (2006). Comparative study of growth performance of three strains of Nile tilapia, Oreochromis niloticus, L. at two stocking densities. Aquaculture Research, 37(2), 172-179.
Saberioon, M., & Císař, P. (2018). Automated within tank fish mass estimation using infrared reflection system. Computers and Electronics in Agriculture, 150, 484-492.
Silva, T.S.d.C., Santos, L.D.d., Silva, L.C.R.d., Michelato, M., Furuya, V.R.B., & Furuya, W.M. (2015). Length-weight relationship and prediction equations of body composition for growing-finishing cage-farmed Nile tilapia. Revista Brasileira de Zootecnia, 44(4), 133-137.
Tran-Duy, A., Van Dam, A.A., & Schrama, J.W. (2012). Feed intake, growth and metabolism of Nile tilapia (Oreochromis niloticus) in relation to dissolved oxygen concentration. Aquaculture Research, 43(5), 730-744.
Tseng, C-H., Hsieh, C-L., & Kuo, Y-F. (2020). Automatic measurement of the body length of harvested fish using convolutional neural networks. Biosystems Engineering, 189, 36-47.
Viazzi, S., Hoestenberghe, S.V., Goddeeris, B.M., & Berckmans, D. (2015). Automatic mass estimation of Jade perch Scortum barcoo by computer vision. Aquacultural Engineering, 64, 42-48.
Wang, T., Lefevre, S., Huong, D.T.T., Cong, N.V. & Bayley, M. (2009). Chapter 8 The Effects of Hypoxia On Growth and Digestion. In J.G. Richards, A.P. Farrell, & C.J. Brauner. (Eds.), Fish Physiology. (pp. 362-396) Burlington: Academic Press.
Watanabe, W. O., Losordo, T. M., Fitzsimmons, K., & Hanley, F. (2010). Tilapia Production Systems in the Americas: Technological Advances, Trends, and Challenges. Reviews in Fisheries Science, 10(3-4), 465-498.
Xu, H.-h., Wang, X.-q., Wang, D., Duan, B.-g., & Rui, T. (2021). Object detection in crowded scenes via joint prediction. Defence Technology.
Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2020). Deep learning for smart fish farming: applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66-90.
Álvarez-Ellacuría, A., Palmer, M., Catalán, I.A., & Lisani, J-L. (2020). Image-based, unsupervised estimation of fish size from commercial landings using deep learning. ICES Journal of Marine Science, 77(4), 1330-1339.
American Public Health Association, American Water Works Association and Water Pollution Control Federation (APHA). (2005). Standard Methods of the Examination of Water and Wastewater. Maryland, U.S.A.: United Book Press.
Azaza, M.S., Dhraïef, M.N., & Kraïem, M.M. (2008). Effects of water temperature on growth and sex ratio of juvenile Nile tilapia Oreochromis niloticus (Linnaeus) reared in geothermal waters in southern Tunisia. Journal of Thermal Biology, 33(2), 98-105.
Balaban, M.O., Sengor, G.F.U., Soriano, M.G., & Ruiz, E.G. (2010). Using image analysis to predict the weight of Alaskan salmon of different species. Journal of Food Science Food Sci, 75(3), 157-162.
Chugh, R.S., Bhatia, V., Khanna, K., & Bhatia, V. (2020). A Comparative Analysis of Classifiers for Image Classification. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 248-253.
Cifuentes-Alcobendas, G., & Domínguez-Rodrigo, M. (2019). Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks. Scientific Reports, 9(1), 18933.
Fernandes, A.F.A., Turra, E.M., Alvarenga, É.R.d., Passafaro, T.L., Lopes, F.B., Alves, G.F.O., Singh, V., & Rosa, G.J.M. (2020). Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Computers and Electronics in Agriculture, 170.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. Cambridge: The MIT Press.
Hao, M., Yu, H., & Li, D. (2016). The Measurement of Fish Size by Machine Vision - A Review. In Computer and Computing Technologies in Agriculture IX, 15-32.
Iqbal, M.A., Wang, Z., Ali, Z.A., & Riaz, S. (2019). Automatic Fish Species Classification Using Deep Convolutional Neural Networks. Wireless Personal Communications, 116(2), 1043-1053.
Jongjaraunsuk, R., & Taparhudee, W. (2021). Weight Estimation of Asian Sea Bass (Lates calcarifer) Comparing Whole Body with and without Fins using Computer Vision Technique. Walailak Journal of Science and Technology (WJST), 18(10).
Jongjaraunsuk, R., Taparhudee, W., & Nimitkul, S. (2019). Feasibility Study Application of Aerial Photographic Using Unmanned Aerial Vehicle for Weight Estimation in River-Based Hybrid Red Tilapia Cage Culture. In Conference Proceeding: The 6 Annual Conference on Engineering and Information Technology. (pp. 45-56). Kyoto: Japan.
Kolding, J., Haug, L., & Stefansson, S. (2008). Effect of ambient oxygen on growth and reproduction in Nile tilapia (Oreochromis niloticus). Canadian Journal of Fisheries and Aquatic Sciences, 65(7), 1413-1424.
Lusiana, E.D., Musa, M., & Ramadhan, S. (2021). Determinants of Nile tilapia’s (Oreochromis niloticus) growth in aquaculture pond in Batu, Indonesia. Biodiversitas Journal of Biological Diversity, 22(2), 999-1005.
Medjahed, S. A. (2015). A Comparative Study of Feature Extraction Methods in Images Classification. International Journal of Image, Graphics and Signal Processing, 7(3), 16-23.
Odone, F., Trucco, E., & Verri, A. (2001). A trainable system for grading fish from images. Applied Artificial Intelligence, 15(8), 735-745.
Pickering, A.D. & Christie, P. (1981). Changes in the concentrations of plasma cortisol and thyroxine during sexual maturation of the hatchery-reared brown trout, Salmo trutta L. General and Comparative Endocrinologyen Comp Endocrinol. 44(4), 487-96.
Riche, M., Haley, D.I., Oetker, M., Garbrecht, S., & Garling, D.L. (2004). Effect of feeding frequency on gastric evacuation and the return of appetite in tilapia Oreochromis niloticus (L.). Aquaculture, 234(1-4), 657-673.
Ridha, M.T. (2006). Comparative study of growth performance of three strains of Nile tilapia, Oreochromis niloticus, L. at two stocking densities. Aquaculture Research, 37(2), 172-179.
Saberioon, M., & Císař, P. (2018). Automated within tank fish mass estimation using infrared reflection system. Computers and Electronics in Agriculture, 150, 484-492.
Silva, T.S.d.C., Santos, L.D.d., Silva, L.C.R.d., Michelato, M., Furuya, V.R.B., & Furuya, W.M. (2015). Length-weight relationship and prediction equations of body composition for growing-finishing cage-farmed Nile tilapia. Revista Brasileira de Zootecnia, 44(4), 133-137.
Tran-Duy, A., Van Dam, A.A., & Schrama, J.W. (2012). Feed intake, growth and metabolism of Nile tilapia (Oreochromis niloticus) in relation to dissolved oxygen concentration. Aquaculture Research, 43(5), 730-744.
Tseng, C-H., Hsieh, C-L., & Kuo, Y-F. (2020). Automatic measurement of the body length of harvested fish using convolutional neural networks. Biosystems Engineering, 189, 36-47.
Viazzi, S., Hoestenberghe, S.V., Goddeeris, B.M., & Berckmans, D. (2015). Automatic mass estimation of Jade perch Scortum barcoo by computer vision. Aquacultural Engineering, 64, 42-48.
Wang, T., Lefevre, S., Huong, D.T.T., Cong, N.V. & Bayley, M. (2009). Chapter 8 The Effects of Hypoxia On Growth and Digestion. In J.G. Richards, A.P. Farrell, & C.J. Brauner. (Eds.), Fish Physiology. (pp. 362-396) Burlington: Academic Press.
Watanabe, W. O., Losordo, T. M., Fitzsimmons, K., & Hanley, F. (2010). Tilapia Production Systems in the Americas: Technological Advances, Trends, and Challenges. Reviews in Fisheries Science, 10(3-4), 465-498.
Xu, H.-h., Wang, X.-q., Wang, D., Duan, B.-g., & Rui, T. (2021). Object detection in crowded scenes via joint prediction. Defence Technology.
Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2020). Deep learning for smart fish farming: applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66-90.
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2023-01-04
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