Image Based Method to Identify Trout Fish Biometric Characteristics and Mapping Growth Curve Trout Fish by Digital Image Analysis under Laboratory Conditions | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| Article 9, Volume 20, Issue 73, March 2020, Pages 113-128 PDF (955.36 K) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/erams.2019.123921.1286 | ||
| Authors | ||
| Behzad Behzadi1; Ali Mohammad Borghei* 1; Arjang Javadi2; Saeid Minaei3; Morteza Almassi4 | ||
| 11. Department of Agricultural Machinery, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Professor, Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Karaj, Iran | ||
| 3Department of Agricultural Machinery Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran | ||
| 4Department of Agricultural Machinery, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| Abstract | ||
| Study on biometric parameters of trout species in the traditional method (based on human and non-automatic factors) are low-efficient due to serious problems such as disease transmission, stress-induced death, inaccuracy, & time-consuming. To overcome the problems of traditional method, an image-based vision system, including imaging and lighting chambers, was developed in this research. The fish biometric parameters were measured using fish movement videography during passage from the dark chamber. Then the selected images from the video were processed. The length, surface area, weight, amount of food consumed, & growth rate of trout were measured under fully controlled conditions and used as comparison criteria (control). Six mathematical models were used to estimate fish weight through measured parameters and among them the weight/length model was used as the best model for estimating fish weight. The accuracy of the system in estimating fish biometric parameters was above than 90% and the system’s capability to estimate the fish required food during the growth process was 98%. | ||
| Keywords | ||
| Image Processing: Videography; Growth Rate | ||
| References | ||
|
Andrew, S. K.., James, D. S., Geoffrey, T. G., Timothy, C. A., & Colin, H. (2004). A video-based movement analysis system to quantify behavioral stress responses of fish. Water Research, 38, p. 3993-4001.
Anibeze, C. I. P. (2000). Length-weight relationship and relative condition of Heterobranchus longifilis (Valencienness) from Idodo River, Nigeria. Naga: the ICLARM Quarterly, 23, p. 34-35.
Balaban, M., Chombeau, M., Cırban, D., & Gümüş, B. (2010a). Prediction of the weight of Alaskan pollock using image analysis. Journal of Food Science, 75(8): p. 552-556.
Balaban, M., Gulgun, F., Soriano, M., & Ruiz, E. G. (2010b). Using image analysis to predict the weight of Alaskan salmon of different species. Journal of Food Science, 75(3): p. 157–162.
Beddow, T. A., Ross, L.G., & Marchant, J. A. (1996). Predicting salmon biomass remotely using a digital stereo-imaging technique. Aquaculture, 146, p. 189-203.
Chu, W. S., Hou, Y. Y., Ueng, Y. T., & Wang, J. P. (2012). Correlation between the length and weight of Arius maculatus off the wouthwestern coast of Taiwan. Brazilian Archives of Biology and Technology, 55, p. 705-708.
Costa, C., Loy, A., Cataudella, S., Davis, D., & Scardi, M. (2006). Extracting fish size using dual underwater cameras. Aquacultural Engineering, 35(3): 218-227.
Fabic, J. N., Turla, I. E., Capacillo, J. A., David, L. T., & Naval, P. C. (2013). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. International Underwater Technology Symposium (UT), March 5, Tokyo, Japan.
Froese, R. (2006). Condition factor and weight-length relationships: history, meta-analysis and recommendations. Journal of Applied Ichthyology, 22, p. 241-253.
Harvey, E. S., Cappo, M., Shortis, M. R., Robson, S., Buchanan, J., & Speare, P. (2003). The accuracy and precision of underwater measurements of length and maximum body depth of southern bluefin tuna (Thunnus maccoyii) with a stereo-video camera system. Fisheries Research, 63, p. 315-326.
Jeong, S. J., Yang, Y. S., Lee, K., & Kang, J. G. and Lee, D. G. (2013). Vision-based automatic system for non-contact measurementof morphometric characteristics of flatfish. Journal of Electrical Engineering and Technology, 8(5): p. 1194-1201
Karpov, E. T., & Tsikliras, A. C. (2009). Length-weight relationships of fishes from
Khairenizam, M. Z., & Norma-Rashid, Y. (2002). Length–Weight relationship of mudskippers (Gobidae: Oxudercinae) in the coastal areas of Sclangor, Malaysia International Centre for living Aquatic Resources Management, World Fish Centre Quarterly, 25, p. 20-22.
Klimley, A. P., & Brown, S. T. (1993). Stereo photography for the field biologist: measurement of lengths and three-dimensional positions of free-swimming sharks. Marine Biology, 74, p. 175-185.
Man, M., Zakaria, M. Z., & Zaki, F. A. M. (2010). A web based fish stock assessment tools for generating fish statistical population information using fish length method. OCEANS 2010 IEEE. May. 24-27 May, Sydney, Australia.
Sarkar, U. K., Khan, G. E., Dabas, A., Pathak, A. K., Mir, J. I., Rebello, S. C., Pal, A., & Singh, S. P. (2013). Length weight relationship and condition factor of selected freshwater fish species found in river Ganga, Gomti and Rapti, India. Journal of Environmental Biology, 34, p. 951-956.
Shafry, M., Rehman, A., Kumoi, R., Abdullah, N., & Saba, T. (2012). FileDI framework for measuring fish length from digital images. International Journal of Physical Sciences, 7(4): p. 607-618.
Shieh, A. C. R., & Petrell, R. J. (1998). Measurement of fish size in Atlantic salmon (salmo salar l.) cages using stereographic video techniques. Aquacultural Engineering, 17(1): p. 29-43.
Torisawa, S., Kadota, M., Komeyama, K., Suzuki, K., & Takagi, T. (2011). A digital stereo-video camera system for three-dimensional monitoring of free-swimming Pacific bluefin tuna, Thunnus orientalis, cultured in a net cage. Aquat. Living Resour. 24, p. 107–112.
Zion, B., Ostrovsky, V., Karplus, I., Lidor, G., & Barki, A. (2012). Ornamental Fish Mass Estimation by Image Processing. Agricultural Research Organization, Bet Dagan. | ||
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