Information fusion in aquaculture: a state-of the art review

Abstract

Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multi-sensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing, computer vision, sensors and wireless sensor networks. Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an “industrialized” ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, pH, dissolved oxygen, turbidity, temperature etc., necessary for the fish feeding process, are examined. Moreover, the different waste management and fish disease diagnosis techniques using different technologies, expert systems and modeling are also reviewed.

Keywords

aquaculture / computer vision / information fusion / modeling / sensor

References

[1] Boström H, Andler S F, Brohede M, Johansson R, Karlsson A, Laere J V, Niklasson L, Nilsson M, Persson A, Ziemke T. On the definition of information fusion as a field of research. Neoplasia, 2007, 13(2): 98–107

[2] Konieczny S, Grégoire É. Logic-based information fusion in artificial intelligence. Information Fusion, 2007, 7(1): 4–18

[3] Castellanos J A, Tardós J D. Mobile robot localization and map building: a multisensor fusion approach. Dordrecht: Kluwer Academic Publishers, 1998, 57–62

[4] Qu G, Zhang D, Yan P. Information measure for performance of image fusion. Electronics Letters, 2002, 38(7): 313–315

[5] Krishnapuram R, Lee J. Fuzzy-set-based hierarchical networks for information fusion in computer vision. Neural Networks, 1992, 5(2): 335–350

[6] Sun S L, Deng Z L. Multi-sensor optimal information fusion Kalman filter. Automatica, 2004, 40(6): 1017–1023

[7] Liu Z, Li X, Fan L, Lu H, Liu L, Liu Y. Measuring feeding activity of fish in RAS using computer vision. Aquacultural Engineering, 2014, 60: 20–27

[8] Amer M, Bilgazyev E, Todorovic S, Shah S, Kakadiaris I, Ciannelli L. Fine-grained categorization of fish motion patterns in underwater videos. In: 13th international conference on computer vision, 2011, Barcelona. New York: IEEE, 2011, 1488–1495

[9] Ferriss B E, Essington T E. Can fish consumption rate estimates be improved by linking bioenergetics and mercury mass balance models? Application to tunas. Ecological Modelling, 2014, 272: 232–241

[10] Flood M J, Noble C, Kagaya R, Damsgård B, Purser G J, Tabata M. Examining the daily feeding rhythms of amago Oncorhynchus masou masou using self-feeding systems. Aquaculture, 2011, 318(1–2): 244–247

[11] Zhang Y, Zhang H, Nasrabadi N M, Huang T S. Multi-metric learning for multi-sensor fusion based classification. Information Fusion, 2013, 14(4): 431–440

[12] Lloret J, Garcia M, Tomás J, Boronat F. GBP-WAHSN: a group-based protocol for large wireless ad hoc and sensor networks. Journal of Computer Science and Technology, 2008, 23(3): 461–480

[13] Mauri G L, Mauri J L. Simulator software for marine fish farms sustainability. WSEAS Transactions on Environment and Development, 2007, 12(3): 214–222

[14] Zhu X, Li D, He D, Wang J, Ma D, Li F. A remote wireless system for water quality online monitoring in intensive fish culture. Computers and Electronics in Agriculture, 2010, 71(1): S3–S9

[15] Buentello J A, Gatlin D M III, Neill W H. Effects of water temperature and dissolved oxygen on daily feed consumption, feed utilization and growth of channel catfish (Ictalurus punctatus). Aquaculture, 2000, 182(3–4): 339–352

[16] Bhatnagar A, Devi P. Water quality guidelines for the management of pond fish culture. International Journal of Environmental Sciences, 2013, 3(6): 1980

[17] Sendra S, Parra L, Ortuño V, Lloret J. A low cost turbidity sensor development. In: Proceedings of the 7th International Conference on Sensor Technologies and Applications 2013, Barcelona. Wilmington: IARIA, 2013, 266–272

[18] Bin Omar A F, Bin MatJafri M Z. Bin MatJafri M Z. Turbidimeter design and analysis: a review on optical fiber sensors for the measurement of water turbidity. Sensors, 2009, 9(10): 8311–8335

[19] Alanärä A, Winberg S, Brännäs E, Kiessling A, Höglund E, Elofsson U. Feeding behaviour, brain serotonergic activity levels, and energy reserves of Arctic char (Salvelinus alpinus) within a dominance hierarchy. Canadian Journal of Zoology, 1998, 76(2): 212–220

[20] Andrew J E, Holm J, Kadri S, Huntingford F A. The effect of competition on the feeding efficiency and feed handling behaviour in gilthead sea bream (Sparus aurata L.) held in tanks. Aquaculture, 2004, 232(1–4): 317–331

[21] Lloret J, Garcia M, Sendra S, Lloret G. An underwater wireless group-based sensor network for marine fish farms sustainability monitoring. Telecommunication Systems, 2015, 60(1): 67–84

[22] Miest J J, Roth O, Reusch T B. Linking fish diseases and animal welfare in aquaculture–a study of the turbot herpesvirus. Fish & Shellfish Immunology, 2013, 34(6): 1724–1725

[23] Zion B. The use of computer vision technologies in aquaculture—a review. Computers and Electronics in Agriculture, 2012, 88(88): 125–132

[24] Li D, Fu Z, Duan Y. Fish-Expert: A web-based expert system for fish disease diagnosis. Expert Systems with Applications, 2002, 23(3): 311–320

[25] Luo R C, Yih C C, Su K L. Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors Journal, 2002, 2(2): 107–119

[26] Toran F, Ramirez D, Casans S, Navarro A E, Pelegri J. Distributed virtual instrument for water quality monitoring across the internet. In: Proceedings of the 17th Instrumentation and Measurement Technology Conference, 2000, Baltimore. New York: IEEE, 2000, 652–656

[27] Serra-Toro C, Montoliu R, Traver V J, Hurtado-Melgar I M, Nuňez-Redó M, Cascales P. Assessing water quality by video monitoring fish swimming behavior. In: 20th International Conference on Pattern Recognition (ICPR) 2010, Istanbul. New York: IEEE, 2010, 428–431

[28] Garcia M A, Sendra S, Lloret G, Lloret J. Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 2011, 5(12): 1682–1690

[29] Tai H, Liu S, Li D, Ding Q, Ma D. A multi-environmental factor monitoring system for aquiculture based on wireless sensor networks. Sensor Letters, 2012, 10(1–2): 265–270

[30] Lambrou T P, Panayiotou C G, Anastasiou C C. A low-cost system for real time monitoring and assessment of potable water quality at consumer sites. In: Sensors 2012, Taipei. New York: IEEE, 2012, 1–4

[31] Sánchez E, Colmenarejo M F, Vicente J, Rubio A, García M G, Travieso L, Borja R. Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecological Indicators, 2007, 7(2): 315–328

[32] Simões F S, Moreira A B, Bisinoti M C, Gimenez S M N, Yabe M J S. Water quality index as a simple indicator of aquaculture effects on aquatic bodies. Ecological Indicators, 2008, 8(5): 476–484

[33] Akkoyunlu A, Akiner M E. Pollution evaluation in streams using water quality indices: a case study from Turkey’s Sapanca Lake Basin. Ecological Indicators, 2012, 18(5): 501–511

[34] Koçer M A, Sevgili H. Parameters selection for water quality index in the assessment of the environmental impacts of land-based trout farms. Ecological Indicators, 2014, 36(1): 672–681

[35] Lermontov A, Yokoyama L, Lermontov M, Machado M A. River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. Ecological Indicators, 2009, 9(6): 1188–1197

[36] Navas J M, Telfer T C, Ross L G. Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques. Marine Pollution Bulletin, 2011, 62(8): 1786–1799

[37] Hernández J J, Fernández L P, Pogrebnyak O. Assessment and prediction of water quality in shrimp culture using signal processing techniques. Aquaculture International, 2011, 19(6): 1083–1104

[38] Carbajal-Hernández J J, Sánchez-Fernández L P, Carrasco-Ochoa J A, Martínez-Trinidad J F. Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmospheric Environment, 2012, 60(6): 37–50

[39] Ferreira N C, Bonetti C, Seiffert W Q. Hydrological and water quality indices as management tools in marine shrimp culture. Aquaculture, 2011, 318(3–4): 425–433

[40] Charef A, Ghauch A, Baussand P, Martin-Bouyer M. Water quality monitoring using a smart sensing system. Measurement, 2000, 28(3): 219–224

[41] Wang G, Li D. A fish disease diagnosis expert system using short message service. In: WRI international conference on Communications and Mobile Computing, 2009, Yunnan. New York: IEEE, 2009, 299–303

[42] Zion B, Shklyar A, Karplus I. In vivo fish sorting by computer vision. Aquacultural Engineering, 2000, 22(3): 165–179

[43] Cubitt K F, Williams H T, Rowsell D, McFarlane W J, Gosine R G, Butterworth K G, McKinley R S. Development of an intelligent reasoning system to distinguish hunger states in Rainbow trout (Oncorhynchus mykiss). Computers and Electronics in Agriculture, 2008, 62(1): 29–34

[44] Yao J, Raghavan V V, Wu Z. Web information fusion: a review of the state of the art. Information Fusion, 2008, 9(4): 446–449

[45] Gümüş B, Balaban M Ö, Ünlüsayın M. Machine vision applications to aquatic foods: a review. Turkish Journal of Fisheries and Aquatic Sciences, 2011, 11(1): 167–176

[46] Polonschii C, Bratu D, Gheorghiu E. Appraisal of fish behaviour based on time series of fish positions issued by a 3D array of ultrasound transducers. Aquacultural Engineering, 2013, 55(1): 37–45

[47] Lee J V, Loo J L, Chuah Y D, Tang P Y, Tan Y C, Goh W J. The use of vision in a sustainable aquaculture feeding system. Research Journal of Applied Sciences Engineering and Technology, 2013, 6(19): 3658–3669

[48] Garcia M, Sendra S, Atenas M, Lloret J. Underwater wireless ad-hoc networks: a survey. In: Mobile ad hoc networks: Current status and future trends. Florida: CRC Press, 2011, 379–411

[49] Azzaydi M, Madrid J A, Zamora S, Sánchez-Vázquez F J, Martınez F J. Effect of three feeding strategies (automatic, ad libitum demand-feeding and time-restricted demand-feeding) on feeding rhythms and growth in European sea bass (Dicentrarchuslabrax L.). Aquaculture, 1998, 163(3–4): 285–296

[50] Mallekh R, Lagardere J P, Eneau J P, Cloutour C. An acoustic detector of turbot feeding activity. Aquaculture, 2003, 221(1–4): 481–489

[51] Rubio V C, Vivas M, Sánchez-Mut A, Sánchez-Vázquez F J, Coves D, Dutto G, Madrid J A. Self-feeding of European sea bass (Dicentrarchus labrax L.) under laboratory and farming conditions using a string sensor. Aquaculture, 2004, 233(1–4): 393–403

[52] Covès D, Beauchaud M, Attia J, Dutto G, Bouchut C, Begout M L. Long-term monitoring of individual fish triggering activity on a self-feeding system: an example using European sea bass (Dicentrarchus labrax). Aquaculture, 2006, 253(1–4): 385–392

[53] Millot S, Bégout M L, Person-Le Ruyet J, Breuil G, Di-Poï C, Fievet J, Pineau P, Roué M, Severe A. Feed demand behavior in sea bass juveniles: effects on individual specific growth rate variation and health (inter-individual and inter-group variation). Aquaculture, 2008, 274(1): 87–95

[54] Noble C, Mizusawa K, Suzuki K, Tabata M. The effect of differing self-feeding regimes on the growth, behaviour and fin damage of rainbow trout held in groups. Aquaculture, 2007, 264(1–4): 214–222

[55] Nunes A J, Parsons G J. A computer-based statistical model of the food and feeding patterns of the Southern brown shrimp (Farfantepenaeus subtilis) under culture conditions. Aquaculture, 2006, 252(2–4): 534–544

[56] Pauly D. A simple method for estimating the food consumption of fish populations from growth data and food conversion experiments. Fish Bulletin, 1986, 84(4): 827–840

[57] Pauly D, Christensen V, Sambilay V Jr. Some features of fish food consumption estimates used by ecosystem modelers. ICES Council Meeting, 1990

[58] Tudor M. Inter-comparison of some equations for evaluating fish daily ration by numerical experiment with an impulse-input feeding model. Ecological Modelling, 2001, 136(2–3): 167–174

[59] Richter H, Focken U, Becker K. A review of the fish feeding model MAXIMS. Ecological Modelling, 1999, 120(1): 47–64

[60] Papandroulakis N, Markakis G, Divanach P, Kentouri M. Feeding requirements of sea bream (Sparus aurata) larvae under intensive rearing conditions: development of a fuzzy logic controller for feeding. Aquacultural Engineering, 2000, 21(4): 285–299

[61] McDermot D, Rose K A. An individual-based model of lake fish communities: application to piscivore stocking in Lake Mendota. Ecological Modelling, 2000, 125(1): 67–102

[62] Trudel M, Rasmussen J B. Predicting mercury concentration in fish using mass balance models. Ecological Applications, 2001, 11(2): 517–529

[63] Cho C Y. Development of computer models for fish feeding standards and aquaculture waste estimations: a treatise. In:VII international symposium on advances in aquaculture nutrition 2004, Hermosillo. Sonora: Mexico, 2004, 375–394

[64] Canale R P, Breck J E. Comments on proper (and improper) solutions of bioenergetic equations for modeling fish growth. Aquaculture, 2013, s 404–405(1): 41–46

[65] Árnason T, Björnsson B, Steinarsson A, Oddgeirsson M. Effects of temperature and body weight on growth rate and feed conversion ratio in turbot (Scophthalmus maximus). Aquaculture, 2009, 295(3–4): 218–225

[66] Chowdhury M K, Siddiqui S, Hua K, Bureau D P. Bioenergetics-based factorial model to determine feed requirement and waste output of tilapia produced under commercial conditions. Aquaculture, 2013, 410–411(2): 138–147

[67] Johnston P, Bérubé F, Bergeron N E. Development of a flatbed passive integrated transponder antenna grid for continuous monito-ring of fishes in natural streams. Journal of Fish Biology, 2009, 74(7): 1651–1661

[68] Spampinato C, Chen-Burger Y H, Nadarajan G, Fisher R B. Detecting, tracking and counting fish in low quality unconstrained underwater videos. In: Proceeding of 3rd International Conference on Computer Vision Theory and Application (VISAPP) 2008, Funchal. Madeira: Springer, 2008, 514–519

[69] Spampinato C, Giordano D, Di Salvo R, Chen-Burger Y H, Fisher R B, Nadarajan G. Automatic fish classification for underwater species behavior understanding. In: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams 2010, New York. New York: USA ACM Publisher, 2010, 45–50

[70] Zhu L M, Zhang Y L, Zhang W, Tao Z C, Liu C F. Fish motion tracking based on RGB color space and interframe global nearest neighbour. In: International Conference on Automatic Control and Artificial Intelligence 2012, Xiamen. London: IET Publisher, 2012, 1061–1064

[71] Pornpanomchai C, Leerasakultham B P L, Kitiyanan W. Shape and texture-based fish image recognition system. Kasetsart Journal-Natural Science, 2013, 47(4): 624–634

[72] Armstrong J D, Huntingford F A, Herbert N A. Individual space use strategies of wild juvenile Atlantic salmon. Journal of Fish Biology, 1999, 55(6): 1201–1212

[73] Greenberg L A, Giller P S. The potential of flat–bed passive integrated transponder antennae for studying habitat use by stream fishes. Ecology Freshwater Fish, 2000, 9(1–2): 74–80

[74] David B O, Closs G P. Continuous remote monitoring of fish activity with restricted home ranges using radio telemetry. Journal of Fish Biology, 2001, 59(3): 705–715

[75] Bégout Anras M L, Lagardère J P. Measuring cultured fish swimming behaviour: first results on rainbow trout using acoustic telemetry in tanks. Aquaculture, 2004, 240(1–4): 175–186

[76] Masaló I, Reig L, Oca J. Study of fish swimming activity using acoustical Doppler velocimetry (ADV) techniques. Aquacultural Engineering, 2008, 38(1): 43–51

[77] Thida M, Eng H L, Chew B F. Automatic analysis of fish behaviors and abnormality detection. In: MVA IAPR Conference on Machine Vision Applications 2009, Yokohama. New York: IEEE, 2009, 278–282

[78] Yan H, Su Y M, Yang L. Experimentation of fish swimming based on tracking locomotion locus. Journal of Bionics Engineering, 2008, 5(3): 258–263

[79] Pinkiewicz T H, Purser G J, Williams R N. A computer vision system to analyse the swimming behaviour of farmed fish in commercial aquaculture facilities: a case study using cage-held Atlantic salmon. Aquacultural Engineering, 2011, 45(1): 20–27

[80] Cha B J, Bae B S, Cho S K, Oh J K. A simple method to quantify fish behavior by forming time-lapse images. Aquacultural Engineering, 2012, 51(2): 15–20

[81] Georgiadis M P, Gardner I A, Hedrick R P. The role of epidemiology in the prevention, diagnosis, and control of infectious diseases of fish. Preventive Veterinary Medicine, 2001, 48(4): 287–302

[82] Nan L, Zetian F, Ruimei W, Xiaoshuan Z. Developing a web-based early warning system for fish disease based on water quality management. In: first IEEE conference on Industrial Electronics and Applications 2006, Singapore. New York: IEEE, 2006, 1–6

[83] Zhang X S, Fu Z T, Cai W G, Tian D, Zhang J. Applying evolutionary prototyping model in developing FIDSS: an intelligent decision support system for fish disease/health management. Expert Systems with Applications, 2009, 36(2): 3901–3913

[84] Hu J, Li D, Duan Q, Han Y, Chen G, Si X. Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronics in Agriculture, 2012, 88(4): 133–140

[85] Hong S R, Jeong H D, Hong S. QCM DNA biosensor for the diagnosis of a fish pathogenic virus VHSV. Talanta, 2010, 82(3): 899–903

[86] Cho C Y, Bureau D P. Reduction of waste output from salmonid aquaculture through feeds and feeding. Progressive Fish-Culturist, 1997, 59(2): 155–160

[87] Cho C Y, Bureau D P. A review of diet formulation strategies and feeding systems to reduce excretory and feed wastes in aquaculture. Aquaculture Research, 2001, 32(S1): 349–360

[88] Cripps S J, Bergheim A. Solids management and removal for intensive land-based aquaculture production systems. Aquacultural Engineering, 2000, 22(1–2): 33–56

[89] Foster M, Petrell R, Ito M R, Ward R. Detection and counting of uneaten food pellets in a sea cage using image analysis. Aquacultural Engineering, 1995, 14(3–4): 251–269

[90] Dudley R W, Panchang V G, Newell C R. Application of a comprehensive modeling strategy for the management of net-pen aquaculture waste transport. Aquaculture, 2000, 187(3–4): 319– 349

[91] Booij N. User Manual for the program duchess, Delft University computer program for 2D horizontal estuary and sea surges. Delft: Delft University of Technology, 1989

[92] Ang K P, Petrell R J. Pellet wastage, and subsurface and surface feeding behaviours associated with different feeding systems in sea cage farming of salmonids. Aquacultural Engineering, 1998, 18(2): 95–115

[93] Parsonage K D, Petrell R J. Accuracy of a machine-vision pellet detection system. Aquacultural Engineering, 2003, 29(3–4): 109–123

[94] Acker T, Burczynski J, Hedgepeth J, Ebrahim A. Digital scanning sonar for fish feeding monitoring in aquaculture. http://www. biosonicsinc.com, 2015–<Date>12–20</Date>

[95] Chamberlain J, Stucchi D. Simulating the effects of parameter uncertainty on waste model predictions of marine finfish aquaculture. Aquaculture, 2007, 272(1–4): 296–311

Acknowledgements

The authors would like to thank a linguistic expert and Dr. Murtaza Hasan for advice when drafting this review. The undertaking of this review was supported by International Science & Technology Cooperation Program of China (2013DFA11320).ƒ

Compliance with ethics guidelines

Shahbaz Gul Hassan, Murtaza Hasan, and Daoliang Li declare that they have no conflict of interest or financial conflicts to disclose. This article is a review and does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2016. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)