Research Journal of Animal, Veterinary and Fishery Sciences ___________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 19 Review Paper Application of Expert Systems in Fisheries Sector – A ReviewAlagappan M.*1,2 and Kumaran M.Department of Fisheries, Govt. of Tamilnadu, INDIA Central Institute of Brackishwater Aquaculture, Chennai, INDIA Available online at: www.isca.in Received 2nd August 2013, revised 19th August 2013, accepted 14th September 2013Abstract The concept for expert system development comes from the subject domain of Artificial Intelligence (AI). Expert system is a computer programme that uses available information and inference to suggest solutions to problems in a particular discipline emulating the logic and reasoning process using artificial intelligence technology. The present paper discusses the expert systems, its advantages and disadvantages and its application in the fisheries sector. A total of 91 expert systems developed in the field of fisheries are reviewed by grouping the expert systems under six categories viz., fish identification, fisheries management, aquaculture management, fish disease diagnosis and health management, fisheries information management and fish product marketing. Expert system could play a lead role for knowledge updation and transfer of technology in fisheries where the research extension linkage is poor, manpower and funds available are inadequate for extension service. Keywords: Expert system, decision support system, information technology, fisheries, aquaculture.Introduction World fisheries have grown dramatically in the last 50 years. From a production of few million tonnes in the early 1950s, fisheries production in 2006 was reported to have risen to 143.6 million tones. This clearly indicates that fisheries sector continues to grow more rapidly than any other animal food-producing sectors. Demand for fisheries products continues to increase to meet the needs of consumers, reflecting recognition of the dietary benefits of fish and shellfish in both developed and developing countries. The oceans of the world have a finite supply of environmental goods and services available to support human activities and needs. In this era of computer revolution, relevant expertise is need of the hour. Information of the required technical advice always has the potential of improving efficiency in all spheres of fisheries development and Information Technology (IT) has a major role to play in this development. One of the branches of IT, Expert systems, has drawn the interest of researchers and professionals in different fields in the recent years. Expert systems currently find success in areas such as science and engineering, finance, medicine, transportation and communications. Expert systems have also found its application in the field of agriculture including fisheries sector. Almost every field currently has a special interest group for expert systems technology. This widespread interest in expert system technology is due to the ability of the expert systems to solve practical and real-world problems. This paper provides an overview of this technology and the expert systems developed in fisheries. The expert systems developed in the field of fisheries over a period of about last 25 years are reviewed. Experts and Expert Systems ‘Experts’ are Subject Matter Specialists who have the knowledge and skill to solve specific type of problems. Experts’ skill usually comes from extensive experience and detailed specialized knowledge of the problems they handle. Unavailability and scarcity of expert in a particular field and if available, inaccessibility for common people, and expensive consultation charges are the major problems in accessing a human expert. Besides, repetitive kind of job might discourage the expert and affect his efficiency. Further, limitation of memory and processing inability of all the essential knowledge required in the process of decision-making is considered to be the obstacle faced by human experts. Due to continuous research and development, new knowledge in enormous amount is being added in every discipline and thus more relevant and accurate advice can be taken from a human expert if his own knowledge is updated which is not an easy task. Human experts are bounded by limitations and it is quite difficult for a human expert to consider all the essential factors while taking a decision. Something is always escaped and remains unattended. Thus some tool or assistance is needed even for an expert to update his knowledge and get help in decision making process. A computer-based expert system seeks to capture enough of the human specialist’s knowledge so that it will solve the problem directly as well. Expert systems are basically computer programs that either recommend or make decisions for people, based on knowledge collected from experts in the field. Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 20 Typically, the problem areas are complex enough such that a simpler traditional algorithm is insufficient to provide the proper solution. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts. As such, expert systems do not typically provide a definitive answer, but provide a probabilistic recommendation. Expert system is defined as an interactive computer programme that uses available information and inference to suggest solutions to problems in a particular discipline emulating the logic and reasoning process using artificial intelligence technology, with built-in knowledgeable pool and solutions on different subjects, performs on par with highly skilled experts, and is a simulation of a consultation process between an expert of a particular field and a non expert. An expert system differs from a conventional computer program. The conventional program is algorithmic in nature and will not entail subjective information, whereas, the expert system tends to behave as human experts in decision making and is highly interactive. It has the ability to capture human decision making expertise and represent this expertise as a series of rules and facts. The management policies that have to be adopted under practical situations can conveniently be incorporated in an expert system. Advantages and limitations of expert systems Expert systems have both advantages and limitations as listed in the table 1 . Despite the fact that expert systems have several limitations, they have been widely successful and proved their value in a number of important applications by being able to analyze many practical problems that lie within defined parameters. Table 1 Advantages and limitations of Expert Systems Advantages Limitations 1. Permanent and not dependent on its use 1. Lacks creativity and commonsense 2. Rapid and easy transfer of knowledge between expert systems 2. Non-adaptable without human intervention 3. Easy to document the knowledge 3. Lacks sensory input of the problem 4. Relatively inexpensive 4. Narrow focus Expert systems in fisheries sector Expert systems are rapidly becoming an integral part of applications in a number of domains ranging from traditional manufacturing processes to applications in outer space. Expert systems have been shown to improve traditional approaches by as much as an order of magnitude. There are number of areas, including fisheries and aquaculture, in which the return on investment in an expert system can be tremendous. Expert systems are tools for fisheries and aquaculture management since they can provide the site-specific, integrated and interpreted advice that fishers, farmers and extension agents need to more efficiently manage fisheries and aquaculture concerns. However, the development of expert systems for fisheries sector is fairly recent, and the use of these systems in commercial venture is rare to date10, 11. This paper provides the bibliography on different expert systems developed in the field of fisheries. To have better understanding in the subject, 91 expert systems developed in the field of fisheries have been grouped under six categories as summarized in the table 2. Table 2 Categories of Expert Systems developed in FisheriesSl. No. Category No. of Expert Systems reviewed 1 Fish identification4 2 Fisheries management24 3 Aquaculture management26 4 Fish Disease diagnosis and health management30 5 Fisheries information management2 6 Fish product marketing5 TOTAL 91 Fish identification The taxonomic identification of fishes is a time-consuming process and making errors is indispensable for those who are not specialists. Automated species identification provides a good option to the burden of routine fish taxonomic identification, however, automated species identification based on morphological characteristics has not been widely employed in any discipline of the biology12. Four expert systems developed for taxonomic identification of fish are discussed in this section. IDEXSYS, an identification expert system was developed for identifying fish larvae of Northeast Atlantic13. In IDEXSYS, morphometric measurements in combination with discriminate analysis were used to identify and separate stocks. This expert system guided the user very fast to the most probable species at a convincing success rate. Another expert system was developed based on the content-based image recovery which could provide statistical clues for assisting taxonomists to identify new species or subspecies14. This expert system had a learning component that can identify representative body shape characters of known species of fish based on digitized landmarks. An automated taxonomic identification system named as IPez assists taxonomists in identification of fishes based on its morphological characters15. Besides helping in the taxonomic identification of fishes in short time with minimal errors, this expert system allowed determining of the main morphometric Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 21 characters that have promoted divergence among closely related species. Whale Watcher Expert System, an online system for the identification of whales was developed by the Ministry of Fisheries and Oceans, Canada16. The Whale Watcher Expert System produces pictures, movies and sounds to support its communication with the user. Fisheries Management For developing the strategies for effective fisheries management and planning, consideration of long-term viability of fish population is essential. Although the information on fishery resources exists, it is mostly in scattered form and is not available in a consolidated form. For the proper utilization, equitable distribution and optimal management of fishery resources the most needed are an inventory of resources, present day utilization levels and future utilization possibilities. Expert system has been realized as an important tool for fisheries management and are developed to efficiently manage the fishery resources. This section reviews 24 expert systems developed for fisheries management. CANOFISH (CANOnical FISHery Management Expert System) demonstrated the strength, necessity and utility of an expert system for fishery management17. This system provided information on management of multispecies fishery resources. The expert system PISCES controls the introduction of exotic fish species into the waters in Southern Africa18. An expert system was designed to provide annual catch quotas of the northern anchovy, Engraulis mordax, optimize sampling effort in time and space, and provide an environmental description for recruitment prediction19. The system also provides advice on measurements for optimization of the sampling effort. Another expert system provides prediction on the fishing conditions of Japanese anchovy Engraulis japonicus off the coast of Kanagawa Prefecture20. In this expert system, a total of 28 variables including egg abundance, catch of larvae, Kuroshio path, etc., were considered for making forecast of the fishing conditions. An expert system for use in fishery stock assessment and management described four levels of support to scientists and managers to increase fish productivity by providing the technical explanations, managing and cataloging the repetitive processes, guiding the low-level decisions, and contributing directly to the strategic and policy decisions21. For management of Tuna fisheries, an expert system was developed by integrating the expert system with Remote sensing22. The system makes use of environmental remotely sensed data and behaviour of tuna for providing the information on tuna fisheries management. MRAG Ltd. developed two fisheries management expert systems viz, ProTuna and ProFish23. ProTuna expert system was intended for the management of tuna fisheries which draws up a list of recommended management plan of action and delivers appropriate advice. ProFish expert system was a more generalized fisheries management expert system not specializing in any one species and provided a broader range of advice on fishery characteristics, policy environment, current management measures and management objectives concerning the most likely resource species in the target area. Another expert system was developed to analyze and recommend the relationship of fish and its environment for optimal fisheries management24. FDES, Fishway Design Expert System recommends the most suitable fishway type for given design conditions25. This expert system provides recommendations on the basis of fishways hydraulics, fish passage performance, and cost requirements, as Fishways contribute to the sustainable development of water resource projects by providing a path for fish migrations. An expert system was developed to recommend a suitable design of fish vessel26. The system incorporates knowledge bases derived from existing vessel series, government regulations, fishing vessel replacement rules, and classification society rules with design software such as resistance and stability algorithms. Besides recommending the suitable fish vessel design, the system also reasons out the constraints involved in various designs of fishing vessel. FISHMAP, a sampling expert system for fish stock assessment was developed by the Center for Marine Science, Environment and Technology, University of Maryland27. FISHMAP enables the user to evaluate potential environmental, anthropogenic, and fishing influences on stock abundance and thus aids in taking appropriate management actions. Another expert system for fish stock prediction developed at the University of Skoevde can use complex relations between environmental information, fish stocks, migration of fish and conventional data used in Virtual Population Analysis to estimate the fish stock size28. The LCSS (Lamprey Control Selection System) expert system assists in developing an environmentally and economically efficient sea lamprey control strategy in Great Lakes29. LCSS assists in long and short term management of sea lamprey populations. An expert system approach was successful in relating recruitment of South African anchovy, Engraulis capensis to wind and sea surface temperature, and environmental and biological indices30. The system proved to have great potential in supporting the future management of the South African anchovy fishery in the dynamic environment of the Benguela Current. Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 22 SimerFish, an expert system for management of marine biological natural resources, evaluates and predicts the marine fish stocks and determines the allowable quotas31. This expert system could forecast population abundance and biomass, possible total allowable catch, determine the quota allocation to subjects of fishery, based on various criteria. CLUPEX provides measures for management of herring Clupea harengus) fisheries. It captures and integrates scientific and local knowledge in the form of heuristic rules. Using input on biotic and abiotic environmental conditions, CLUPEX uses the rules to provide quantitative and qualitative predictions on the structure, dynamics and mesoscale distribution of shoals of migratory adult herring during different stages of their annual life cycle32. An expert system was developed to identify the probable fishing ground for anchovy (Engraulis ringens) in the northern zone of Chile33. The probable fishing ground model in this expert system is supported by past evidence of the spatial and temporal distribution of anchovy, and by the optimum ranges of sea surface temperature, thermal gradients and chlorophyll recorded in fishing zones. The probable fishing ground charts are calculated with remote sensed satellite data and a decision support analysis using GIS. Another expert system integrates life history and ecological characteristics of marine fishes to estimate their vulnerability to fishing to help biologist overcome the risk using conventional methods, which is difficult for the majority of fish species, as the population data normally required by such methods are unavailable. This system describes the known relationship between biological characteristics and vulnerability of marine fishes from published literature, and could be used as a decision support tool in fishery management and marine conservation planning34. A decision support system for fisheries policy and management decisions on optimal harvesting plan for the fishing industry was developed and applied to the real situation in the Northeastern U.S35. A simulation optimization is built in the system to assist the authorities in planning for a fleet of vessels in terms of time and location of fishing, as well as amount and target species to be fished. A knowledge based expert system for prediction of fishing ground for pelagic fish in the coastal area of Central and South Sulawesi was developed by using a series of satellite data of sea surface temperature, sea surface chlorophyll-a and turbidity36. With these parameters, the system understands the temporal and seasonal variability of the marine environment of the study area, and identifies the oceanographic phenomena, i.e. upwelling, front or eddy. To generate spatial configuration of fishing ground prediction map, GIS model was integrated with the result of Knowledge-Based Expert System. A Fisheries Management Decision Support System was developed for management of the Northeastern US multi species fishery37. This system determines the optimal fishing plans, in terms of the amount of fish to be caught from each species at each sub-area and at each time period, utilizing the data provided by the user. To provide the best management advice, this system integrates multiple sources of information, including stock status, spatial distribution, catch per unit effort, and environmental and economic factors collected from fishery independent research surveys as well as fishery dependent observations. STRAT-TECH, Inc. developed an expert system called Fishing Expert that provides fishing tips for bass (smallmouth and largemouth), trout (brook, brown, and rainbow trout), musky, walleye, northern pike, crappie, and bluegill38. The expert system advices are based on weather, time of year (e.g. spring, summer, and fall), type of water (e.g. lake, river, or stream), wind, spawning conditions, high / low pressure systems, day or night fishing, etc. The techniques and tips given are unique for the fish specified by the user. Aquaculture Management Aquaculture, from a backyard farming operation has developed into a commercial farming enterprise, and is considered as an alternative source of protein. Due to on-farm and off-farm exigencies aquaculture production practices are constantly getting improved/ modified to deal with the emerging scenarios. The change in aquaculture scenario has emphasized the need to adapt and develop expert systems for the better management of aquaculture facilities. 26 Expert systems developed in aquaculture for performing various tasks have been reviewed in this section. An expert system was developed for monitoring the sanitary condition of Sea bass, Dicentrachus labrax reared in thermal effluents39. This expert system was used to monitor mortality rates and effects on the thermal plant, and also single out causes of mortality and environmental and technological risk factors. AQUASITE, a computer site assessment tool assists farmers and policy makers in selection of ideal site for undertaking marine coastal aquaculture40. This expert system considers the full range of site assessment criteria and the main species of interest for mariculture in the Atlantic Region of Canada. SCHUBERT AUDIT evaluates the ability of trout farms to breed fish of defined quality for consumption and restocking41. This expert system has been developed from a methodology under use in the industry and the quality audit to comply with laws on the management of fish farms. UMECORP developed Recirculating Intensive Aquaculture eXpert (RIAX), an expert system to monitor and control culture of Tilapia mozambique in an intensive aquaculture system42. Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 23 RIAX guides inexperienced operators through important maintenance and emergency procedures, besides delivering predictive advice about complex problems. Forecasting biological, chemical and mechanical events before they occur gives operators and system managers’ ample warning to deal with spurious or large scale events. ‘Crystal’ expert system monitors and controls the water quality in aquaculture farm43. The system stores rule base, fed with real time measured data to ensuring the health of the fish. By building sensor knowledge into the expert system, the novice user could specify the needed sensors, or build them to drawings supplied by the system. An expert system was developed for dealing with complexities in sustainable integrated mariculture systems44. This expert system provides information on many area of integrated mariculture, which includes spreading of overheads, waste reduction and leveraging of factors such as feed, power, expertise, management resources, capitalized assets and transportation systems. Besides these, the expert system also provides decision support on site selection, species selection, system design and operation control. DESTA (Decision Support Tool for Aquaculture) allows a novice computer user to enter data for the environmental assessment of a new aquaculture pond or site through a visually interactive dialogue session45. This tool is a valuable decision support tool for assisting anyone engaged in the selection and development of sites for aquaculture. FIsheries Nutriology Expert System (FINES) studies the aquaculture farming way by building a fish farming dynamic model46. This expert system provides a quasi-optimal farming way as the factors of growth phase physiology condition and environment change. FINES also automatically design the compound feed and diagnose nutritive disease as per the inputs of the user. An expert system was used to reproduce expert evaluations of data from investigations of the benthic impact of a fish farm in the Red Sea47”. This expert system was used to study the geochemical profiles of a farm as it provided the valuable data on the benthic impacts of fish farming, as the interpretation of these data was a complex process requiring scientific sophistication and understanding of benthic processes. A user-friendly expert system was developed as a generic reference relating to TEechnological and MAnagement (TEMA) aspects of shrimp, Litopenaeus vannamei farming practices48. This expert system provides assistance to confirm, complement or modify the information that users of the expert system have of semi-intensive and intensive technologies for grow-out, and management practices for controlling pond water quality, productivity and feeding of L. vannamei. ADDSS (Aquacultural Development Decision Support System) aids the decision maker/ planner in making choices regarding the planning and development of the aquaculture industry in a given region49. This system is composed of three main components: a model base containing all relevant models which are essentially multiple objectives in nature, a database containing all relevant data, and a dialog component providing a user interface to the other two components of the system. ADDSS was successfully demonstrated in Northern Egypt to plan aquaculture development and demonstrate it as a viable industry for supplying cheap and good quality protein, balancing the foreign exchange deficits, improving the standard of living, and creating employment opportunities. AquaFarm, a simulation and decision support system aids in design and management planning of finfish and crustacean aquaculture facilities50. AquaFarm has its use in aquaculture production facilities of any design and management intensity, for brood fish maturation, egg incubation, grow out of finfish or crustaceans in cage, single pass, serial reuse, water recirculation, or solar-algae pond systems. The user has total control over all facility and management specifications, including site climate and water supplies, components and configurations of fish culture systems, fish and facility management strategies, unit costs of budget items, and production species. POND, a decision support system analyses pond aquaculture facilities by the use of a combination of simulation models and enterprise budgeting51. POND provides the ability to simulate pond dynamics and fish growth for warm water pond aquaculture facilities, and to compute enterprise budgets relating various cost and returns from a particular facility to determine short and long term profitability. An expert system was developed to optimize the denitrification rates and eliminate discharge of toxic by products, i.e., NO, NO, NO or HS in commercial aquaculture system to improve the production capacity52. This expert system maintains Nitrate-nitrogen concentration 5 ppm while avoiding any increase in NO or HS concentrations. Similarly, another expert system for water quality evaluation of aquaculture pond was developed which could inspect the factors of water quality in the aquaculture pond, analyse the water quality and evaluate them, and then diagnose the environment of the aquaculture water53. The expert system also provides feasible suggestions for the effective water quality management. CADS_TOOL (Cage Aquaculture Decision Support Tool) assists the cage aquaculture managers to optimize their choice of sites for placement of cages54. This system covers four essential activities starting from classifying a site, selecting the best site from several site alternatives, calculating a sustainable holding density from a chosen site, and finally performing an economic appraisal of a site. Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 24 An expert system was developed to improve shellfish production on mussel rafts in Maine55. This expert system combines computer based methodologies for determining tidally driven flows, wave heights, flow through shellfish raft systems and consumption of food by the shellfish to optimize the production cycles on shell fish rafts. An internet-based expert system was developed for management of freshwater fish farming operation56. The installed measurement system that measures water temperature, pH and oxygen dissolved in water and the expert system that supervises and controls the fish production process are connected via the internet for communication. Access to the on-site measurement equipment via internet enables better supervision, planning and better utilization of production resources, which results in increased quality of fish, increased fish growth and bigger profits. An expert system helps farm managers in decision making in infrastructure planning and management of nitrogen-rich water in aquaculture farm57. The expert system provides improved strategies for the optimisation and control of bioprocesses involved in nitrogen removal by phyto-treatment pond. A feeding expert system was developed for indoor intensive culture of South Flounder by analyzing the feed fed and impact of various factors on feeding58. This expert system assists in effective feed management and better supervision of the culture system through Personal Digital Assistant (PDA). An Israeli company has developed SmartFish Expert System (SES), an expert system for aquaculture that provides fish growers with web-based farm management and analysis through global aquaculture database system; SmartFeed (SF), an optimal feeding system; and Smart Control (SC) offering tailored turnkey projects for aquaculture sites59. Apart from its use in prime aquaculture activities, expert systems have also found its application in licensing the aquaculture activities. A decision support system for marine finfish aquaculture, MFADSS (Marine Finfish Aquaculture Decision Support System) aids the Department officials in decision making for evaluating finfish lease applications60. MFADSS provides scientific advice on aquaculture lease applications by applying a standard set of criteria which increases consistency in decision making. Another decision support system was developed for regularizing the licensing for finfish aquaculture activities61. Similarly, a decision support system was also developed for licensing to undertake freshwater cage aquaculture62. Fish Disease diagnosis and health management The expert system technology has been most widely applied in the field of medical diagnosis. Similarly, it has also found its way into disease diagnosis and health management in the fields of agriculture, animal sciences and fisheries. The Fish health management is a crucial part of aquaculture. The development of expert systems in fish health management has shown an increased use of information technology to support aquaculture information systems research and development. Thirty (30) expert systems developed in fish disease diagnosis and health management are presented in table 3 in chronological order. Different parameters like visible symptoms, water quality, image of the diseased fish, microscopic image, etc. have been employed by various expert systems for diagnosing the fish disease. Table 3 Expert Systems developed in Fish disease diagnosis and health management Sl. No. Expert System Description/ Method of disease diagnosis 1 AQUADOC – for diagnosis of various fish diseases63External systems, histopathological symptoms and confirmatory test 2 SALMEX – to diagnose diseases of farmed salmonid fishes in seawater64Behaviour signs, external signs and internal symptoms 3 SEDIP – for diagnosing diseases in freshwater and seawater fish65External and internal symptoms 4 Fish Doctor – diagnosis of various fish disease 66 Behaviour signs and external signs 5 FINES – for diagnosis of nutritive diseases of farm fish 46 Diagnose nutritive disease of farmed fish 6 Expert system for diagnosis of various fish diseases 6 7 Behaviour signs and external signs 7 Fish-Vet – PC based expert system for diagnosis of various fish disease68Behaviour signs and external signs 8 Fish disease diagnosis expert system 69 Web based disease diagnosis 9 Fish-Expert – web based fish disease diagnosis for freshwater fishes70Behaviour signs, external signs and internal symptoms, water quality examination and microscopic examination 10 HAMES – for diagnosis and treatment of farmed tilapia diseases71Observations and selected water parameters to diagnose the diseases of farmed tilapia Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 25 11 Fish disease diagnosis expert system72Reasoning system for diagnosis of various fish disease with description and solution 12 T-vet – web based tele-diagnosis of fresh water fish diseasesTele-diagnosis with farmers (synchronous) and off-line diagnosis (asynchronous) enabling farmers to send multimedia information such as text, images and graphics to receive diagnosis 13 Expert system for fish disease diagnosis based on ontology 73 Diagnosis based on ontology 14 Expert system for fish disease diagnosis based on image retrieval74 Diagnosis based on image retrieval 15 SEDPA – expert system for Eel disease diagnosis75Primary data – symptoms and eel pathologies and secondary data – historic data of physical, chemical, biological and yield 16 AWQEE-DSS –water quality evaluation and early warning for fish disease 76Evaluation and early warning on water quality parameters with management measures 17 Crab-Expert –crab disease diagnosis77Tele-diagnosis and treatment services to farmers with access to internet 18 H-vet – tele-diagnosis of fish disease78Call centre oriented hybrid disease diagnosis and consulting system 19 Expert system for fish disease diagnosis based on fuzzy inference model79Fuzzy inference model through a set fish disease diagnostic cases 20 Expert system for fish disease diagnosis based on image processing of pathogen’s microscopic image80Disease diagnosis through image processing from the microscopic image and sending a message about diagnosed disease and treatment to fish farmers on their mobile 21 Expert system for fish disease diagnosis 81 Case-based reasoning: two step case retrieve model 22 Call centre based expert system for fish disease diagnosis82Automation of fish diagnostic process based on call centre via telephone 23 Expert system for diagnosis of various fish diseases 83 Tele diagnosis of fish disease 24 EWS-FDWQ – fish disease warning based on water quality 84 Fish disease diagnosis based on water quality 25 ES-FDD – fish disease diagnosis based on disease data and image85Fish disease data and images 26 Expert system for fish disease diagnosis using SMS (Short Message Service)86Automation of fish diagnostic process through SMS from mobile phones 27 AquaSDS –fish disease diagnosis based on fuzzy logic decisions87Intelligent agents based diagnosis method 28 Expert system for identification of diseases of farmed flounder88Early warning index through Expert survey method 29 Expert system for fish disease diagnosis based on rough set and classifier fusion89Diagnosis by combination of rough set theory and classifier fusion using data from reduction fish disease diagnosis case database 30 Two stage fish disease diagnosis expert system 90 Clinical signs and Microscopic images Fisheries information management The National Agricultural Library's Aquaculture Information Center had developed an expert-system based advisor called AQUAREF for supporting reference-related to aquaculture information inquiries91. It is an automated system developed for storage and access on mircrocomputers. This expert system was developed to help improve access to basic resource tools and information in aquaculture in U.S. Another expert system, REGIS (Regional Information System for African aquaculture) provides information on aquaculture of the Sub-Saharan Africa region in an easy-to-use form for administrators92. It presents a descriptive summary of African aquaculture, covering areas such as consumption and production statistics, extension, training, credit programmes, associations, government policy and legislation, and development projects. Fish product Marketing Experts systems have found its application in forecasting of future market prospects for various products. It has also been applied in the field of fish market forecasting. STRATEX (STRATegic decision making system for EXport firms), a knowledge based system aids in decision making in trade of fish and fisheries products by supporting the choice of Research Journal of Animal, Veterinary and Fishery Sciences ________________________________________ ISSN 2320 – 6535 Vol. 1(8), 19-30, September (2013) Res. J. Animal, Veterinary and Fishery Sci. International Science Congress Association 26 market segments and export trade of raw and manufactured fish products in Norway93. APPFSS (Aquatic Products Price Forecasting Support System) was developed by China Agricultural University to provide decision aids to price forecasting to avoid market imbalance94. This expert system combines models, data, expert knowledge and a user interface and support participants of aquaculture industry to predict market price and related information. By using a Case Based Reasoning (CBR) approach, an expert system was developed forecasting the price of aquatic products and constructs a forecasting system which can automatically collect internet data on aquatic product prices95. The system could effectively analyze and forecast the aquatic product prices using source data from dedicated web sites. Expert systems have also been developed to predict the fishery market in Jiangxi Province in China96, to forecast Danish ex-vessel seafood prices97, and to forecast aquatic products price based on agent98. Conclusion Expert system technology is a promising area of information technology having its applications in varied sectors including fisheries. Expert systems are not only the means to apply subject matter specialist’s knowledge to a particular problem area but are also potentially powerful learning resources to help the novice end users of the expert system to develop their own expertise. Combined knowledge of multiple human experts gives the system more breadth that a single person is likely to achieve. Expert systems are used to capture the knowledge of human experts to either assist the expert or use them in those situations where the expert is not available. The application of expert systems in fisheries has gradually increased over the years, and is expected to expand further in the near future. This review of expert systems developed in the fisheries sector in past, provide an insight into the types of applications which can be expected in future. AcknowledgementThe authors thank the Director, Central Institute of Brackishwater Aquaculture (CIBA), Chennai, India for his support in preparation of this manuscript. The authors extend their special gratitude to the Head, Social Sciences Division, CIBA for his valuable suggestions and encouragements. 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