A PROPOSED FISH COUNTING ALGORITHM USING DIGITAL IMAGE PROCESSING TECHNIQUE

Ibrahim Aliyu, Kolo Jonathan Gana, Aibinu Abiodun Musa, James Agajo, Abdullahi Mohammed Orire, Folorunso Taliha Abiodun, Mutiu Adesina Adegboye

Abstract


Fish product contributes a significant amount of protein demand of human nutrition and made up of about 16% of human diet all around the world. However, Fish production is one of the factors that have been a bottleneck for development of fish farming for most developing countries such as Nigeria. One of the major and time consuming task in production is providing an accurate estimate of the fingerlings to farmers. The methods of counting fingerlings in most developing countries is done manually. These manual methods are inevitably influence by inaccuracies and exposer of the fingerlings to unnecessary stress that could lead to death. This paper proposed a fingerling counting algorithm using digital image technique. To achieved this aim, a robust segmentation algorithm, feature extraction algorithm and machine learning algorithm for fingerlings classification and counting are hereby formulated. At the end of this research, the proposed algorithm is expected to count different sizes of fingerlings with high accuracy.


Full Text:

PDF

References


Alver, M. O., Tennøy, T., Alfredsen, J. A., & Øie, G. (2007). Automatic measurement of rotifer Brachionus plicatilis densities in first feeding tanks. Aquacultural Engineering, 36(2), 115-121.

AquaScan. (2016). AquaScan Fishcounters. Retrieved from http://www.aquascan.com/

Arnarson, H. (1991). Fish and fish product sorting. In Fish quality control by machine vision, Marcel Dekker, New York, 245-261.

Cadieux, S., Michaud, F., & Lalonde, F. (2000). Intelligent system for automated fish sorting and counting. Paper presented at the Intelligent Robots and Systems, 2000.(IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on.

Costa, C., Antonucci, F., Boglione, C., Menesatti, P., Vandeputte, M., & Chatain, B. (2013). Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis. Aquacultural Engineering, 52, 58-64.

Costa, C., Loy, A., Cataudella, S., Davis, D., & Scardi, M. (2006). Extracting fish size using dual underwater cameras. Aquacultural Engineering, 35(3), 218-227.

Costa, C., Scardi, M., Vitalini, V., & Cataudella, S. (2009). A dual camera system for counting and sizing Northern Bluefin Tuna (Thunnus thynnus; Linnaeus, 1758) stock, during transfer to aquaculture cages, with a semi automatic Artificial Neural Network tool. Aquaculture, 291(3), 161-167.

Daniel, E. (2015). Quality fingerlings: Hatcheries to the rescue. The Nation. Retrieved from http://thenationonlineng.net/quality-fingerlings-hatcheries-to-the-rescue/

Dowlati, M., de la Guardia, M., & Mohtasebi, S. S. (2012). Application of machine-vision techniques to fish-quality assessment. TrAC Trends in Analytical Chemistry, 40, 168-179.

Duan, Y., Stien, L. H., Thorsen, A., Karlsen, Ø., Sandlund, N., Li, D., . . . Meier, S. (2015). An automatic counting system for transparent pelagic fish eggs based on computer vision. Aquacultural Engineering, 67, 8-13.

Fabic, J., Turla, I., Capacillo, J., David, L., & Naval, P. (2013). Fish population estimation and species classification from underwater video sequences using blob counting and shape analysis. Paper presented at the Underwater Technology Symposium (UT), 2013 IEEE International.

FAO. (2016). Mass production of fry and fingerlings of the african catfish clarias gariepinus. Retrieved from http://www.fao.org/docrep/field/003/ac182e/ac182e03.htm

Fisher, R., Perkins, S., Walker, A., & Wolfart, E. (2003a). Pixel Addition. HYPERMEDIA IMAGE PROCESSING REFERENCE. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/pixadd.htm

Fisher, R., Perkins, S., Walker, A., & Wolfart, E. (2003b). Pixel Substraction. HYPERMEDIA IMAGE PROCESSING REFERENCE. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/pixsub.htm

Friedland, K., Ama-Abasi, D., Manning, M., Clarke, L., Kligys, G., & Chambers, R. (2005). Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. Journal of Sea Research, 54(4), 307-316.

Han, J., Asada, A., Takahashi, H., & Sawada, K. (2010). Automated three-dimensional measurement method of in situ fish with a stereo camera. Paper presented at the OCEANS 2010 IEEE-Sydney.

Han, J., Honda, N., Asada, A., & Shibata, K. (2009). Automated acoustic method for counting and sizing farmed fish during transfer using DIDSON. Fisheries Science, 75(6), 1359-1367.

Harvey, E., Cappo, M., Shortis, M., 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(3), 315-326.

Huang, I.-W., Hwang, J.-N., & Rose, C. S. (2016). Chute based automated fish length measurement and water drop detection. Paper presented at the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

IMPEX. (2016). TPS Fish counters. Retrieved from impexagency.dk/uploads/product_sheets/Impex_FishCounters.pdf

Kang, M. (2011). Semiautomated analysis of data from an imaging sonar for fish counting, sizing, and tracking in a post-processing application. Fisheries and aquatic sciences, 14(3), 218-225.

Khantuwan, W., & Khiripet, N. (2012). Live shrimp larvae counting method using co-occurrence color histogram. Paper presented at the Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2012 9th International Conference on.

Labuguen, R., Volante, E., Causo, A., Bayot, R., Peren, G., Macaraig, R., . . . Tangonan, G. (2012). Automated fish fry counting and schooling behavior analysis using computer vision. Paper presented at the Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on.

Loh, B. C., Raman, V., & Then, P. H. (2011). First Prototype of Aquatic Tool Kit: Towards Low-Cost Intelligent Larval Fish Counting in Hatcheries. Paper presented at the Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on.

Luo, S., Li, X., Wang, D., Li, J., & Sun, C. (2015). Automatic Fish Recognition and Counting in Video Footage of Fishery Operations. Paper presented at the Computational Intelligence and Communication Networks (CICN), 2015 International Conference on.

Martınez-Palacios, C. A., Tovar, E. B., Taylor, J. F., Durán, G. R., & Ross, L. G. (2002). Effect of temperature on growth and survival of Chirostoma estor estor, Jordan 1879, monitored using a simple video technique for remote measurement of length and mass of larval and juvenile fishes. Aquaculture, 209(1), 369-377.

Mathiassen, J., Jansson, S., Veliyulin, E., Njaa, T., Lønseth, M., Bondø, M., . . . Skavhaug, A. (2006). Automatic weight and quality grading of whole pelagic fish. Paper presented at the In Proceedings NFTC 2006, the 1st Nor-Fishing Technology Conference, Trondheim, Norway.

Mathiassen, J. R., Misimi, E., Toldnes, B., Bondø, M., & Østvik, S. O. (2011). High‐Speed Weight Estimation of Whole Herring (Clupea harengus) Using 3D Machine Vision. Journal of food science, 76(6), E458-E464.

Morais, E. F., Campos, M. F. M., Padua, F. L., & Carceroni, R. L. (2005). Particle filter-based predictive tracking for robust fish counting. Paper presented at the XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05).

Newbury, P. F., Culverhouse, P. F., & Pilgrim, D. A. (1995). Automatic fish population counting by artificial neural network. Aquaculture, 133(1), 45-55.

Potongkam, K., & Miller, J. (2006). Catfish Hatchery and Production Manual.

Rosenberry, B. (2012). The Larcos PL-Counter. . Shrimp News International. Retrieved from http://www.shrimpnews.com/FreeReportsFolder/FarmReportsFolder/TheLarcosPLCounter.html

Ruff, B., Marchant, J., & Frost, A. (1995). Fish sizing and monitoring using a stereo image analysis system applied to fish farming. Aquacultural Engineering, 14(2), 155-173.

SMITH-ROOT. (2016). Fish Harvesting. Retrieved from http://www.smith-root.com/aquaculture/

Strachan, N. (1993). Length measurement of fish by computer vision. Computers and electronics in agriculture, 8(2), 93-104.

Toh, Y., Ng, T., & Liew, B. (2009). Automated fish counting using image processing. Paper presented at the Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on.

VAKI. (2016). Product Line- Fish Counter. Retrieved from https://www.pinterest.com/pin/294704369346159913

Westling, F., Sun, C., & Wang, D. (2014). A modular learning approach for fish counting and measurement using stereo baited remote underwater video. Paper presented at the Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on.

Yada, S., & Chen, H. (1997). Weighing type counting system for seedling fry. Bulletin of the Japanese Society of Scientific Fisheries (Japan).

Zheng, X., & Zhang, Y. (2010). A fish population counting method using fuzzy artificial neural network. Paper presented at the Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on.

Zion, B. (2012). The use of computer vision technologies in aquaculture–a review. Computers and electronics in agriculture, 88, 125-132.


Refbacks

  • There are currently no refbacks.