El Gayar 1997 TheUseofInformationTechnologyinAquacultureManagement
El Gayar 1997 TheUseofInformationTechnologyinAquacultureManagement
El Gayar 1997 TheUseofInformationTechnologyinAquacultureManagement
net/publication/233118805
CITATIONS READS
14 5,866
1 author:
Omar F. El-Gayar
Dakota State University
183 PUBLICATIONS 2,039 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Understanding the Influence of Digital Divide and Socio-Economic Factors on the Prevalence of Diabetes View project
All content following this page was uploaded by Omar F. El-Gayar on 03 April 2019.
By
Omar F. El-Gayar
an alternative source of protein has further emphasized the need to adapt and develop
advanced IT for the better management of aquaculture facilities as well as the regionl
It is the objective of this paper to review the use and potential prospects of IT in
and process control, data management, computerized models, decision support systems,
artificial intelligence and expert systems, image processing and pattern recognition,
geographical information systems, and information centers and networks. The review
profound impacts on all walks of life. Indeed, we are now experiencing the information
age. Currently, aquaculture is one of the fields which are looked upon as a potential
Aquaculture is the science and art of cultivating aquatic species. The significant
importance of aquaculture stems from the fact that world wide demand for quality fish
protein is increasing dramatically, while in the meantime, the natural fisheries are near their
maximum sustainable yield (MSY) levels and are in the process of depletion. In addition,
when fish are compared to other alternative sources of protein, e.g., terrestrial livestock,
Fish are non-competitive with human beings in their habitat and nutrition. (1)
calories, a low content of saturated fatty acids, and a high content of poly-
complexity requires better management. Moreover, the decision making process is further
complicated because of the dynamic and stochastic nature of the biological, physical and
economic environments, thereby emphasizing the need for adapting advanced IT such as
alternative source of high quality cheap protein, particularly for developing countries
where protein shortages already exist. Such effort is led by local governments as well as
several international organizations that are involved in planning and financing aquaculture
development in various regions of the world. The World Bank, the Food and Agriculture
Organization of the United Nations, and the United States Agency for International
Development are just a few examples of such organizations. However, and due to the wide
range of issues involved in aquaculture development, the development and use of decision
aids (such as decision support systems) as well as the application of new IT techniques
inevitable.
This paper is written for the aquaculture practitioner at the farm level as well as the
regional level. It seeks to review the use and potential of IT in aquaculture management.
Specifically, the paper seeks to identify the potential benefits of the use of IT in
aquaculture, the current status of IT in aquaculture, and the main obstacles for adoption.
and process control, data management, computerized models, decision support systems,
artificial intelligence and expert systems, image processing and pattern recognition,
manually controlled systems (Zahradnik 1987). On the other hand, process control of
systems traditionally refers to the art of monitoring the system visually and with analytical
measurement techniques for the purpose of providing manual control of the inputs to guide
the system to the desired goal (Fridley 1987). However, nowadays, process control often
refers to the use of instrumentation techniques for providing feedback information on the
controlled system. Information can then be analyzed and appropriate actions initiated for
controlling the system. In effect, process control is synonymous to the automatic control
can prove to be highly beneficial. In that regard, Zahradnik (1987) identifies two major
applications where instrumentation can have the greatest potential in aquaculture. The first
application area lies in environmental monitoring and control. The importance of which
stems from the close correlation between the environmental conditions and factors
affecting aquaculture production such as animal health, feed utilization, animal growth
parameters include temperature, pH, salinity, dissolved oxygen, ammonia, nitrates, nitrites,
suspended solids, turbidity, and water flow rates. The second application area lies in fish
stock inventory assessment. The importance of which stems from the direct relation
between fish stock quantities, feed efficiency, and disease control. Current stock inventory
practice primarily involves stock sampling and mortality records. The use of visual sensors
presented in section 7.
With regard to process control, Fridley (1987) identifies the potential benefits of
reducing cost, primarily realized by reducing time, effort and labor as well as
optimizing resource use. For example, feeding can be matched closely with demand,
Moreover, Balchen (1989) notes that the purpose of instrumentation and process
parameters necessary to arrive at the optimal yield at the desired time to satisfy market
demand.
operation safety, attained by utilizing remotely operated actuators, thereby reducing the
need for physical interaction between the operator and the process unit, particularly in
hazardous situations.
of the technology in aquaculture are few relative to manufacturing and agriculture. Table
aquaculture.
Bjordal et al. (1987) describe a system for monitoring the behavior of salmon culture as
well as the environmental factors and different rearing conditions. The main objective of
this work is to minimize the stress level of the fish by optimizing critical factors in order
to avoid disease and to increase growth and quality of fish. Using appropriate sensors,
meteorological data, and light intensity. Sensors are interfaced to a computer where trend
and historical analysis of stored data is performed. While such system is primarily for
identifying the optimal levels of such environmental parameters and rearing conditions for
salmon i.e., for research purposes. Once such levels and conditions are identified a similar
system for the management of aquaculture facilities can be utilized for providing
continuous monitoring of the parameters of concern and alarming the operator if any such
With regard to process control, Hansen (1987) presents a computer system for the
control and monitoring of aquaculture plants. The system includes a special software
module for handling different tasks such as data gathering, automatic control, alarming,
logging, trend analysis, and reporting. With regard to monitoring and control, a software
library is available for water quality, water distribution, heating systems, biomass, and
feeding systems. The system is of a flexible configuration and can thus be adapted to the
It should be noted, that the above examples are by no means exhaustive, they
merely illustrate how computers can be used for monitoring and control in aquaculture
while it is easy to attach a number to the cost of adopting such a technology, it is not
the case when trying to estimate the perceived benefits, thereby complicating the
approach of relying on visual observation and their own instinct and experience for
aquaculture management.
adoption of this technology. Also, the rapid technological progress is likely to produce
sensors and equipment that would operate reliably in the harsh aquaculture environment at
a reasonable cost.
3- Data Management
Aquaculture production is a complex undertaking involving a system of interwoven
biological processes. The proper management of such system requires keeping track of a
large amount of data. Moreover, and due to the dynamic nature of the production process
and the high risk involved in making an inappropriate decision, the quality of the data and
its timely availability are of prime importance. Data management thus becomes a vital task
While manual data management can be suitable for a small scale operation, the task
quickly becomes difficult and complicated even for a modest farm size. In that regard,
computers possess the processing power and storage capabilities necessary for performing
virtually all data management activities such as storing, retrieving, analyzing and
presenting data with ease. Moreover, recent advances in computer technology have made
powerful computer hardware available at affordable prices with a large variety of ‘off the
shelf’ software to choose from. Such software have matured with respect to capabilities
and user friendliness to unprecedented levels not even predicted ten years ago.
The potential of computers in data management has spawned the interest of
researchers and farmers alike. For example, Muench, Thomsen and Croissant (1986) of
Island Science, Inc. present Pond Manager, a production data logging and decision
management system for aquaculture. In this system, data are recorded manually and
include environmental data such as air temperature, water temperature, salinity, pH, and
dissolved oxygen, as well as information pertaining to feed, harvest, and stocking. Besides
providing the basic data management routines for storing, retrieving, and reporting data,
Pond Manager supports the management of various farm jobs by providing guidance to
farm operators based on previously stored data. For example, feed scheduled per pond is
printed on the next day’s field data sheet, thereby guiding farm operators.
data management system for aquaculture. Their system is primarily composed of a desk-
top microcomputer for centrally storing and analyzing farm data using a Lotus1-2-3
worksheet, a pocket computer for directly recording field data, a printer and a modem for
remote downloading of field data from the pocket computer to the desktop computer.
Environmental and stock data are recorded in a standard form presenting the results in a
daily, or weekly format, as required with all the computations performed by the computer.
Killcreas (1988), documents FISHY 2.0, a pond-oriented, menu-driven
microcomputer program that is designed to allow fish farmers to accumulate and analyze
data regarding fish production operations such as stocking, feeding, moving, mortality and
harvest. Moreover, FISHY 2.0 is able to predict fish growth based on previously stored
fish growth parameters. Fish growth estimates can then be used to compute probable
harvest dates and weights. Another feature of FISHY 2.0 is the ability to automatically
generate day-to-day records of events associated with the production operation. Such
records can then be analyzed using RECORDS (Killcreas 1988) or using a spreadsheet
such as LOTUS 1-2-3.
It should be noted that the above examples of the application of computers to
aquaculture data management is by no means exhaustive. Numerous systems are available
or custom designed and are rarely reported in the literature. Moreover, the availability of
powerful user-friendly, ‘off the shelf’, database and spreadsheet software packages make
it possible for the individual fish farmer to effectively manage and analyze his own
production data with minimal technical expertise.
Nevertheless, while sound data records are invaluable for proper decisions,
computerization doesn’t come without cost and thus a cost/benefit analysis is always
warranted. In that regard, when appraising the investment, the computerized system should
be capable of offering a return which at least breaks even with the investment and running
costs. Typical capital expenditure include the necessary hardware and software. The
hardware consists of a computer system with adequate storage capacity and a printer for
printing reports. The software can either be purchased ‘off the shelf’, custom made or
provided through an extension service. Operating expenditure is primarily composed of the
operator’s time required to enter and analyze the data. On the other hand, benefits are in
the form of a more efficient and thus more profitable operation. In some cases, it would be
difficult to quantify the potential benefits, however, as the scale of operation is increased,
particularly through intensification, the adoption of computer database management can be
more easily justified.
4- Computerized Models
In general, a model is a simplified representation of reality for the purpose of
experimenting with alternative strategies (Leung 1986). In that regard, Cuenco (1989)
provides the following reasons on "why is there a need to model aquacultural systems?"
Modeling serves as a powerful tool for the formulation, examination and improvement
Modeling provides a working tool to conduct numerous "what if" experiments quickly
large and complex aquacultural systems which are seldom possible in their natural
environment.
Modeling accelerates the use of more quantitative and precise methods in aquaculture
research.
Models put together knowledge from theoretical, laboratory and field studies into a
inconsistent.
information handling. It may cost less to derive knowledge from the model than the real
world counterpart such as a large complex aquacultural system. The model may represent
an aquacultural system which does not exist or cannot be easily manipulated. In addition,
the model may provide a convenient medium to collect and/or transmit information.
In aquaculture, models are primarily numerical and thus computationally intensive.
In that regard, computers can provide the processing capabilities needed to make use of
relating its biological, physical, and economic elements for the purpose of assisting
producers and decision makers in identifying optimal production system design and
operation management strategies (Leung 1993). The work by Allen et al. (1984) provides
before 1984 (Table 2), while Leung (1993) reviews the modeling effort since 1984 (Table
3). Moreover, Leung (1993) indicates that while applications are few compared to
Gempesaw, Lipton and Goggin (1993) present AQUASIM PC, a microcomputer based
program for providing farm managers with an easy to use tool to help analyze aquaculture
business decisions based on the manager’s expectation of key production and market
performance and prices, analyzes the effect of this uncertainty on financial performance,
On the other hand, at the regional level, Sylvia and Anderson (1993) present a bio-
economic model for developing information for private and public salmon aquaculture
policy strategies when environmental issues are important. The two levels of analysis refer
to the two actors - salmon producers and policy makers. While the producers are assumed
to maximize profits, the public policy makers are faced with four policy objectives
including revenue, benthic quality, profits, and tax revenues. The policy instruments in
their study include the number of allowable sites and the effluent tax.
In practice, however, the issue of concern is how many of these models are
available for the practicing fish farmer or decision maker. After all, to make use of such
solve the often complex system of equations. Furthermore, the survey conducted by
Varvarigos (1991) already indicates that IT is not a priority among fish farm managers. All
of which boils down to the conclusion that although aquaculture models are systematically
developed by modelers from a multitude of disciplines and a large portion of such models
would have a direct impact on aquaculture management, such models are underutilized by
The future, however, has a lot to offer. In fact, the recent technological
support system technology provide the tools for modelers to bring their models to their
ultimate users.
making models available to the aquaculture practitioner by supporting the models with an
appropriate user interface and a data base management system. By making such ever-
support systems can significantly enhance the effectiveness of the decision making process,
Sprague and Carlson (1982) give a definition that captures the key aspects of decision
support systems. They define a DSS as an interactive computer-based system that helps
decision makers utilize data and models to solve managerial problems. Furthermore,
Sprague and Carlson (1982), propose a framework which regards a DSS to be composed
primarily of three main components: a dialog component, a data base component and a
The dialog component is primarily concerned with handling the interface between
the system components and the user. Typical capabilities of the dialog component include,
the ability to handle a variety of dialog styles, to accommodate user actions with a variety
of input devices, to present data in a variety of formats, to provide context sensitive on-line
help, to provide context sensitive error messages, and to allow the user to control
The data management component is primarily concerned with all the data
the ability to provide data maintenance and housekeeping tasks easily and quickly, to
capture/extract data from a variety of sources, to portray logical data structures in user
terms, and to handle personal and unofficial data so that users can experiment with
model base. Typical capabilities of the model management component include, the ability
to provide model maintenance and housekeeping tasks easily and quickly as well as the
Decision support systems are already being used as decision aids to enhance the
Eom and Lee (1990) present a survey of the literature on the applications of DSS from
1971 through April 1988. A total of 203 DSS applications articles are compiled and
classified according to 16 different application areas. In Table 4, the results of their survey
is presented. This survey strongly indicates that decision support systems are increasingly
applied in profit and non-profit organizations such as government, military, health care and
education. Moreover, 73% of the articles were published in the last 8 years, but only 27%
during the first 10 years (1971-1980) indicating that the number of DSS applications is
In that respect, Ernst, Bolte and Nath (1993) developed a DSS for finfish
aquaculture that is entirely concerned with operational level issues. This DSS consists of
applied and integrated expertise in fish genetics, biology and culture, aquatic chemistry,
On the other hand, at the regional level, El-Gayar, Leung and Rowland (1994)
present the preliminary design of a prototype DSS that would aid the decision maker or the
planner make choices regarding the planning and the development of the aquaculture
industry for a given region. The system is composed of three main components: a model
base containing all relevant models which are essentially multiple objective in nature, a
data base containing all relevant data, and a dialog component providing a user interface
to the other two primary components of the system. Based on the models and data stored
in the model base and the data base respectively, the system would answer questions such
as: what species to grow? what technology to use? and how much to grow of each species
and/or technology?
In sum, the rapid increase in the number of models developed for aquaculture,
together with their associated data requirements, emphasize the importance of developing
DSS that pool models and data into one easy to use computer system suitable for practical
(and not only research) applications either operational or strategic. The utilization of DSS
technology would serve as a delivery system bringing aquaculture models from the esoteric
definition of AI or AI-based systems, however, one can loosely define AI as the field of
study concerned with the development of tools and techniques for solving problems that
are normally associated with human intelligence. In briefly presenting basic AI techniques
and application areas it is helpful to adopt a modified version of Nilsson's 'onion' model
shown in Figure (2) and in which the basic AI techniques and tools are at the core and are
surrounded by the main application areas of AI, sometimes considered as sub fields within
AI. In the following paragraphs a brief description of each of the layers of the onion model
is presented.
that intelligent behavior is not so much due to reasoning methods as to a large stock of
have been developed for AI purposes, of which the most important are predicate logic,
production rules, frames and semantic nets (Rauch-Hindin 1986; Schutzer 1986).
Reasoning and logic is one property that sets an AI system apart from more
conventional programs. There are many different forms of reasoning: formal reasoning,
reasoning, and abduction. However, most current AI programs are based on the foundations
of formal reasoning often refereed to as resolution theorem proving. The forward and
backward inference control strategies commonly found in rule-based expert systems are in
problem solving is reduced to the process of merely searching for the goal state starting
from some initial state. There are orderly search procedures that guarantee that all paths
will be tried once, these are known as uninformed search methods and for AI problems
specific knowledge about the application area. Such informed search strategies are known
as heuristic search. Examples of such strategies are the best-first and the branch and bound
(Schutzer 1986).
As AI systems requirements differ from those of traditional programming, special
languages and environments are needed to cater for these differences and thus facilitate
and speed up AI systems development. The main AI languages are LISP and PROLOG
environments are also used such as expert systems 'shells' for expert systems development
have been a great success area for AI over the last 15 years. Expert systems are programs
that use knowledge and inferential procedures to solve problems in specific problem
diagnosis, and in process control (Rauch-Hindin 1986; Schutzer 1986). ES are commonly
developed using shells which are a collection of expert systems building tools that are
engineering and programming skills required to build an expert system. Several ES shells
are commercially available in the market such as KEE by Intellicorp and EXSYS by
raw signals we receive through our senses e.g., vision, hearing, and touch using the
concepts and knowledge we have previously acquired and stored in our cognitive processor
(Schutzer 1986). Machine perception is thus concerned with the development of techniques
that enable computers to understand and deal with information received through various
sensors. Machine perception can be further divided into two broad fields: computer vision
and speech understanding. Computer vision is concerned with developing techniques that
enable computers to understand the output of 2-D and 3-D sensors i.e., visual information.
Speech understanding, on the other hand, can be considered as an extension of NLP and is
Planning can be defined as the design process for selecting and combining together
individual actions into sequences to achieve goals. Some basic AI planning systems are
now being developed that are independent of the application area (Schutzer 1986).
The process of learning has attracted the attention of researchers for a considerable
amount of time. In AI, research into learning includes the desire not only to understand
how people learn, but to provide computers with the ability to learn and to be taught rather
than having to be programmed. It should be noted however that the practical application of
was held with the purpose of exploring the ways in which the considerable body of
knowledge developed in this computer related discipline can be applied in fisheries and
determination that there is a distinct need to examine the potential for knowledge-based
systems application in fisheries science and aquaculture. The primary motivation of which
is to take advantage of advances in computer sciences and apply these to the needs of
fisheries sciences and aquaculture (Palmer 1989). The Workshop identified areas in
such applications together with the associated AI approach. In the next paragraphs, a brief
implementations.
Referring to the section on the use of computers for control and monitoring tasks,
particularly in automating intensive systems, we say that the ever increasing complexity of
such monitoring and control systems and the high risk involved in dealing with a stock of
high economic value per unit mass indicate the need for timely intelligent decisions many
are already emerging. In that regard, Padala (1990) presents the RIAX prototype system of
Umecorp, Inc. that uses an ES to monitor and control a recirculating intensive aquaculture
system. The Expert Controller (EC) module in RIAX uses rules based on an expert’s
knowledge and experience to deliver control strategies to system activators. The EC can
also be programmed to send data to a computer simulation, run tests and examine the results
to decide upon appropriate responses. Another application also involving the automation
aquaculture and filtration systems. The author indicates that the technology results in the
real-time monitoring and control of aquaculture systems analogous to that used by human
managers.
site selection. The importance of this issue stems from the need to match the culture
requirements of the species under consideration with those available on site in a manner
that ensures growth and development of the cultured species. The problems involved with
site selection are not due to the unavailability of information, but is due to the fact that the
information available is not easily accessible and is usually not in a form that is
immediately useful to the potential user (Palmer 1989). Knowledge-based systems, when
coupled with a DSS and a geographical information system type database can assist in the
accessibility of information, thereby aiding the selection process. Stokoe and Gray (1990)
of interest for aquaculture in the Atlantic region of Canada. Another closely related issue
to site selection is environmental impact assessment. In that regard, Haakanson and Wallin
consequence analysis for natural aquatic ecosystems with particular emphasis on nutrient
1986) and there is no reason why that should not be the case in aquaculture. The availability
of such disease diagnosis and treatment systems can provide adequate diagnostic and
overall success of the aquaculture operation. Bossu, Mantoni and Saroglia (1989) present
an ES for the diagnosis and the relative therapeutic treatment of sea bass (D. labrax) reared
in thermal effluents. The system utilizes a data base containing data derived from eight
years of observations at the experimental station of Torre Valdalign (Rome, Italy), thereby
making it possible to date back to cases of disease already verified, to the environmental
factors possibly associated with them, and to the therapeutic treatment used in their effects.
that without the introduction of non-indigenous species, commercial aquaculture will not
grow into a major industry (Palmer 1989). Unfortunately, up to our knowledge, no actual
and determining which oyster on a conveyor belt has the desired orientation. Such
applications, however, are still investigated using traditional image processing and pattern
The measurement of physical properties such as size, shape and hardness of aquatic
products is particularly important for the design of processing equipment. AI can assist in
acquired through a variety of technologies including, but not limited to, computer-based
can prove beneficial. Several computer-aided systems design are reported in the literature,
Finally, training aids and education would be quite valuable for providing
1989). In that regard, intelligent computer-assisted learning and instruction can provide an
inexpensive means of refining management skills in an environment that avoids the costly
literature. However, on the education side, Hanfman, Bielawaki and Lemand (1989)
present REGIS, a regional information system for African aquaculture. REGIS merges
hypermedia and expert systems into a useful easy-to-access information retrieval system
that focuses on African aquaculture. The ES component acts as an advisor on both small
and commercial scale aquaculture ventures in a particular region.
current applications are few as compared to business and manufacturing. However, with
algorithms for the processing of digital images so that the result is more suitable than the
original image for subsequent processing and use. For example, smoothing algorithms are
used for reducing noise and other spurious effects that may be present in an image as a
compensate for effects such as shadows and ‘hot spot’ reflectance that may result in an
On the other hand, pattern recognition (PR) is primarily concerned with extracting
features regarding the content of an image and then recognizing the different components
of the image. Several recognition approaches are proposed in the literature ranging from
the use of statistical techniques to the utilization of AI concepts and techniques. For the
sake of clarity, AI-based pattern recognition systems are reviewed in section 6 as machine
perception or more specifically computer vision where the aim is of image understanding
techniques are emerging. Table 6 presents a list of such application. In the next paragraphs
Enumeration (NIFE) pattern recognition system. The system does not require artificial
lighting or manipulation of the fish. The system is primarily composed of three underwater
video cameras that are integrated with a microcomputer-based image analysis system to
capture fish images in a marine sea cage. The initial results indicate that the system is able
to count the fish within commercial sized sea cages within 5% of the actual count. As the
system was still under development as of 1993, the authors neither provided any estimate
on the cost of such system nor its potential success in a commercial aquaculture production
setting.
As another example, Foster et al. (1993) present an algorithm for food pellet
processing system linked to an under water camera and a video tape recorder. Such
wastage and to ensure that the fish receive the correct dosage of drugs. Unfortunately,
improvements are still needed to allow the system to determine the pellet count more
accurately.
So and Wheaton (1993) present a computer vision control system to sense, locate
and sever the oyster Crassostrea Virginica hinge. The overall efficiency varies from 14.2%
determining fish biomass, food management, algae count in live food production and image
analysis of remote sensing data that IP and PR can provide for aquaculture management,
aquaculture a reality.
integrated computer hardware and software for the purposes of inputting, storing,
Kapetsky 1991).
In aquaculture, the applications of GIS is proliferating at an unprecedented pace
particularly in the field of planning for aquaculture development. In that regard, the need
there is an urgent need to secure and optimize suitable aquaculture sites (Meaden and
Kapetsky 1991).
the need for a systematic approach to data acquisition and analysis that is able to cope
with a wide range of source data types and origins (Ross, Mendoza and Beveridge
1993).
GIS are already utilized in the planning for aquaculture development. Table 7
and Nanne (1987), in one of the earliest studies on the use of GIS in aquaculture, assess
the capabilities of a GIS and satellite remote sensing to rapidly provide synoptic
information to plan for aquaculture development. The region of concern is the Gulf of
Nicoya on the Pacific coast of Costa Rica. Utilizing remote sensing data, GIS is used to
and suspended culture; extensive culture of shrimp and fish in solar salt ponds; and semi-
intensive shrimp culture outside of mangrove areas. The criteria for evaluation included
salinity, infrastructure and water quality for all culture types. The results indicate that
opportunities exist for aquaculture development. However, further ground and water
investigations are needed to verify the suitability of individual areas identified before
development programs can be planned in detail. The authors then conclude that GIS and
useful for planning for aquaculture development. However, while this approach is
effectiveness, time and expenses for the development of a central GIS facility can be shared
with other departments of the government that are likely to benefit from its services.
Beveridge (1993), assess the potential of GIS for site selection in coastal aquaculture using
as an example salmonid cage culture development in Scotland. The criteria for evaluation
included bathymetry, current, shelter, and water quality. The results suggest that a total of
1.26 ha. (6.4% of the total area) are suitable for cage culture. The authors then conclude
that the GIS approach to site selection has the potential to give useful results, the reliability
of which depends on the accuracy of the source data. The total cost of the GIS approach is
about $9000; $90 for software, $1000 for skilled labor and $7910 for hardware. This is
compared to a manual analysis and processing cost of $2000. However, the authors note
that the cost differences is derived from the initial purchase of the required hardware and
will reduce rapidly with each subsequent use of the GIS. Moreover, manual assessment
produces results which may be biased by subjective personal assumptions and not based
technology.
In effect, several perceived benefits have promoted the quick expansion of GIS
applications in aquaculture. Most important of which include: the ease to analyze different
scenarios by varying the initial assumptions and criteria, the possibility to accommodate
data from widely differing spatial sources such as field work data, remote sensing imagery
and secondary maps, the efficient access to a huge volume of data thereby improving the
quality of decisions, and the cost effectiveness realized on the farm level by optimizing its
On the other hand, problems do exist with the proper applications of GIS in
aquaculture. Most important of which include: the difficulty in quantitatively assessing the
perceived benefits of a particular GIS application for comparison with the projected cost,
the extremely slow process of digitizing maps which in turn translates to cost overhead,
the potential errors encountered in utilizing much of the poor quality existing hard copies
of graphical and tabular data, the shortage of GIS skilled personnel, and the lack of user-
organized fashion, coupled with the advent of high speed reliable communication systems
form the foundations for information centers and computer networks. In aquaculture,
Swann, Jensen and Einstein (1995) illustrate the use of the Aquaculture Network
Information Center (AquaNIC). AquaNIC contains a wide variety of information that can
be either viewed on the user’s computer monitor, downloaded via modem, or a copy sent
to the user’s electronic mail (e-mail) address. Moreover, AquaNIC allows the user to link
to other aquaculture databases on the Internet, thereby acting as a gateway to the world’s
Benn and Kuhns (1995), introduce FISHNET, a special interest group forum on
CompuServe, a commercial, but widely used computer network. In their presentation, the
authors demonstrated how the forum’s message board can be used for information
exchange, toured the various libraries, and demonstrated how to access and acquire
information from them. They also demonstrated the live conferencing abilities of the
forum.
established in October 1991 by the Delaware Sea Grant Marine Advisory Service (Ewart
1995). The center’s collection provides public access to more than 1200 titles including
bibliographies, books, catalogs, extension fact sheets, periodicals, state aquaculture plans,
technical reports and videos. Moreover, the center provides access to AquaNIC and other
on-line information resources. During its years of operation, the center is proving to be an
effective means for organizing and disseminating technical, economic and legal/regulatory
electronic information system developed by the Institute for Food and Agricultural
different forms of information such as text, images, sound and video clips) database system
providing extension personnel and their related industry with a rapid diverse desktop
library (Lazur 1995). FAIRS contains over 2,000 agricultural extension publications,
building plans and several plant selection programs. The system also offers a special
publishing program that allows for existing or new publications, tables, graphs and
notes that FAIRS has greatly increased the counties extension offices’ abilities to provide
several issues:
to interface the computer to the telephone line and a printer to print the desired
information.
the existence of a suitable information source and the information required to access
that source. Such information can be in the form of Internet commands such as
TELNET and FTP accompanied by the necessary login information such as a user
for information sources only accessible through the Internet, access to an Internet
gateway is required. For individual fish farmers, Internet gateway access can be
For the planner, much of these issues are normally resolved by the information
technology support unit in his/her organization. However, for the individual fish farmer,
the situation is quite different. In fact, without the availability of extension services for
providing the necessary technical support for the initial set-up and ideally, for providing
an Internet gateway access, setting up a computer system for on-line information access
Moreover, and as with any farm investment, proper investment appraisal in which
cost is weighted against potential benefit is warranted. Costs include capital expenditure
on the necessary hardware and software, and operating expenditure primarily in the form
of communication costs and a monthly fee for the commercial Internet gateway provider
or the commercial network such as CompuServe or Prodigy. On the other hand, benefits
are primarily in the form of readily available technical and market information, the value
management at the farm as well as the regional level, IT potential in aquaculture has not
been realized yet. Even for mature and commercially available technologies such as
instrumentation and process control, there are relatively few applications compared to
agriculture and manufacturing. Obstacles for adoption are mainly attributed to the
difficulty in perceiving and quantifying the potential benefits as well as the tendency to
distrust new technologies. In fact, the result of a survey conducted in 1987 and reported by
293 salmon and trout farms in the United Kingdom, indicates that IT is not a priority among
fish farm managers. In effect, microcomputers were used mainly for accounting and word
and time requirements. Moreover, besides the cost factor in utilizing IT in aquaculture, fish
farmers and planners in countries that do not produce the necessary hardware and software
may face additional problems regarding the acquisition and maintenance of the necessary
technical support.
disappointing, the steady decline in computer hardware prices together with its increasing
functionality and the availability of flexible, user friendly and powerful software would
definitely contribute toward encouraging more fish farmers to adopt information
Adams, C. M., W.L. Griffin, J.P. Nichols, and R.W. Bricks (1980a). Bioengineering-
economic model for shrimp mariculture system. TAMU-SG-80-203, Texas A&M
University, 118 pp.
Adams, C. M., W.L. Griffin, J.P. Nichols, and R.W. Bricks (1980b). Applications of a
bioeconomic engineering model for shrimp mariculture systems. Southern J. Agr. Econ.
12:135-41.
Ali, C.Q., L.G. Ross and M.C.M. Beveridge (1991). Microcomputer spreadsheets for the
implementation of geographic information systems in aquaculture: A case study on carp in
Pakistan. Aquaculture 92:199-205.
Allen, P.G. and W.E. Johnston (1976). Research direction and economic feasibility: an
example of systems analysis for lobster aquaculture. Aquaculture 9:155-80.
Allen, P.G., L.W. Botsford, A.M. Schuur, and W.E. Johnson (1984). Bioeconomics of
Aquaculture. Elsevier Science Publishers, New York.
Alsagoff, K.S.A., H.A. Clonts, and C.M. Jolly (1990). An integrated poultry, multi-species
aquaculture for Malaysian rice farmers: A mixed integer programming approach.
Agricultural Systems 32:207-31.
Azizan, Z. (1983). Using simulation methods to determine the optimal harvesting period
for the cultured Malaysian freshwater prawns. Macrobrachium rosenbergii. Masters
Thesis, University of Illinois, 196 pp.
Bacon, J.R., C.M. Gempesaw II, I. Supitaningsih and J. Hankins (1993). Risk management
through integrating aquaculture with agriculture, in Techniques for Modern Aquaculture
(Ed.) J. K. Wang, St. Joseph, Minnesota: American Society of Agricultural Engineers, pp.
99-111.
Bala, B.K. and M.A. Satter (1989). System dynamic simulation and optimization of
aquacultural systems. Aquacultural Engineering 8:381-91.
Balchen, J.G. (1989). Instrumentation, information and control systems in aquaculture, in
Instrumentation in Aquaculture (Ed.) J.A. Wyban and E. Antill, Honolulu: The Oceanic
Institute, pp. 1-11.
Benn, J.R. and J.F. Kuhns (1995). FISHNET and Compuserve: Two stops on the
Aquaculturist’s information superhighway), in Book of Abstracts, Aquaculture ‘95,
February 1-4, 1995, San Diego: The World Aquaculture Society, p. 61.
Bevan, D.J. and D.L. Kramer (1988). A control system for the long term maintenance of
hyponic water. Aquaculture 71:165-169.
Bjordal A., S. Floen, J.E. Fosseidengen, B. Totland, J.T. Ovredal, A. Ferno and I. Huse
(1987). Monitoring biological and environmental parameters in aquaculture, in Automation
and Data Processing in Aquaculture (Ed.) J.G. Balchen, NY:Pergamon Press, pp. 151-155.
Bjorndal, T. (1988). Optimal harvesting of farmed fish. Marine Resource Economics
5:139-59.
Bossu, T., M. Mantoni and M. Saroglia (1989). Expert system for sanitary control in
intensive fish culture: The case of sea bass reared in thermal effluents, in Aquaculture
Europe 89 - Short Communications and Abstracts of Review Papers, Films/Slideshows and
Poster Paper (Ed.) R. Billard and N. De Pauw, Belgium: European Aquaculture Society,
pp. 39-40.
Botsford, L.W., H.E. Rauch and R.A. Shleser (1974). Optimal temperature control of a
lobster plant. IEEE Trans. Automatic Control AC-19:541-3.
Botsford, L.W., H.E. Rauch, A.A. Schuur and R.A. Schleser (1975). An economically
optimum aquaculture facility. Proc. World Maric. Soc. 6:407-20.
Botsford, L.W. (1977). Current economic status of lobster culture research. Proc. World
Maricult. Soc. 8:723-39.
Botsford, L.W., J.C. Van Olst, J.M. Carlberg and T.W. Gossard (1977). The use of
mathematical modeling and simulation to evaluate aquaculture as a beneficial use of
thermal effluent. Proc. 1977 Summer Computer Simulation Conference, Chicago, 18-20
July 1977, pp. 405-10.
Boyle, W.A., A. Asgeirsson and G.M. Pigott (1993). Advances in the development of a
computer vision fish biomass measurement procedure for use in aquaculture, in Techniques
for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota: American Society of
Agricultural Engineers, pp. 382-392.
Brown, L.M., I. Gargantini, D.J. Brown, H.J. Atkinson, J. Govindarajan and G.C.
Vanlerberghe (1989). Computer-based image analysis for the automated counting and
morphological description of microalgae in culture. Journal of Applied Phycology
1(3):211-225.
Cacho, O.J., H. Kinnucan and U. Hatch (1991). Optimal control of fish growth. American
Journal of Agricultural Economics 73:174-83.
Cacho, O.J., U. Hatch and H. Kinnucan (1990). Bioeconomic analysis of fish growth:
effects of dietary protein and ration size. Aquaculture 88:223-38.
Callaghan, S.S. (1975). A dynamic model of a proposed heated finishing plant for oysters.
M.S. thesis, University of Massachusetts, 112 pp.
Chen, S.S. and F.W. Wheaton (1989). Oyster hinge line detection using image processing.
Aquacultural Engineering 8(5):307-327.
Cuenco, M.L. (1989). Aquaculture systems modeling: an introduction with emphasis on
warmwater aquaculture. ICLARM Studies and Reviews 19, 46 pp.
Dartez, J. (1989). Continuous dissloved oxygen monitoring and control systems for
aquaculture, in Instrumentation in Aquaculture (Ed.) J.A. Wyban and E. Antill, Honolulu:
The Oceanic Institute, pp. 45-53.
Ebeling, J.E. and T.M. Losordo (1989). Continuous environmrntal monitoring systems for
aquaculture, in Instrumentation in Aquaculture (Ed.) J.A. Wyban and E. Antill, Honolulu:
The Oceanic Institute, pp. 54-70.
El-Gayar, O.F. and P.S. Leung (1994). A risk programming model for brackish water
polyculture: a case sturdy in Egypt. Paper presented at the 25th Conference of the World
Aquaculture Society, Jan. 1994, New Orleans.
El-Gayar, O.F., P.S. Leung and L. Rowland (1994). An aquacultural development decision
support system (ADDSS): a preliminary design. Paper presented at the 25th Conference of
the World Aquaculture Society, Jan. 1994, New Orleans.
Emanuel, W. R. and R.J. Mulholland (1975). Energy based dynamic model for Lago Pond,
Ga. IEEE Trans. Automatic Control AC-20:98-101.
Engle, C. R. and G. Pounds (1993). Economics of single- and multiple-batch production
regimes for catfish, in Aquaculture: Models and Economics (Ed.) U. Hatch and H.
Kinnucan, Boulder:Westview Press, pp. 75-89.
Engle, C. R. and U. Hatch (1988). Economic assessment of alternative aquaculture aeration
strategies. Journal of the World Aquaculture Society 19: 85-96.
Eom, H.B. and S.M. Lee (1990). A survey of decision support systems applications (1971-
April 1988) Interfaces 20:65-79.
Ernst, D.H., J.P. Bolte and S.S. Nath (1993). A decision support system for finfish
aquaculture, in Techniques for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph,
Minnesota: American Society of Agricultural Engineers, pp. 568-580.
Ewart, J.W. (1995). Delaware’s Aquaculture Resource Center), in Book of Abstracts,
Aquaculture ‘95, February 1-4, 1995, San Diego: The World Aquaculture Society, p. 108.
Fenaux, R., G. Malara and H. Clautre (1985). A turbidostat driven and controlled by
microcomputer. Aquaculture 48:91-95.
Foster, M., M.R. Ito, R. Petrell and R. Wang (1993). Detection and counting of uneaten
foos pellets in a sea cage using image analysis, in Techniques for Modern Aquaculture
(Ed.) J. K. Wang, St. Joseph, Minnesota: American Society of Agricultural Engineers, pp.
393-402.
Fridley, R.B. (1987). Modeling, identification and control of aquacultural processes and
facilities, in Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen,
NY:Pergamon Press, pp. 17-24.
Gates, J. M., C.R. MacDonald, and B.J. Pollard (1980a). Salmon culture in water reuse
system: an economic analysis. University of Rhode Island Marine Technical Report, 78,
52.
Gates, J.M., C.R. MacDonald, and B.J. Pollard (1980b). A dynamic linear programming
model of fish culture in water reuse systems. Rhode Island Agricultural Experiment Station
Contribution No. 1892: 20.
Gempesaw II, C. M., F.F. Wirth, J.R. Bacon, and L. Munasinghe (1993). Economics of
vertical integration in hybrid striped bass aquaculture, in Aquaculture: Models and
Economics (Ed.) U. Hatch and H. Kinnucan, Boulder:Westview Press, pp. 91-105.
Gempesaw, C.M., D. Lipton and S. Goggin (1993). AQUASIM PC ver. 1.7 User’s Manual.
The Extension Service of the USDA and the University of Maryland Sea Grant Extension
Program.
Griffin, W.L., J.S. Hanson, R.W. Brick and M.A. Johns (1981). Bioeconomic modeling
with stochastic elements in shrimp culture. J. World Maric. Soc. 12:94-103.
Griffin, W.L., L.A. Jensen and C.M. Adams (1983). A generalized budget simulation
model for aquaculture. TAMU-SG-83-202, Texas A&M University, 131 pp.
Griffin, W.L., W.E. Grant, R.W. Brick, and J.S. Hanson (1984). A bioeconomic model of
shrimp maricultural systems in the USA. Ecological Modeling 25:47-68.
Haakanson, L. and M. Wallin (1991). Use of ecometric analysis to establish load diagrams
for nutrients in coastal areas, in Marine Aquaculture and the Environment (Ed.) T.
Maekinen, pp. 9-23.
Hanfman, D., L. Bielawski and R. Lewand (1989). REGIS: REGional Information System
for African Aquaculture, full version, 5 1/4-inch disk.
Hansen, E. (1987). Computer aided control and monitoring of aquaculture plants, in
Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen, NY:Pergamon Press,
pp. 187-192.
Hanson, H.M. (1992). A flexible temperature and environmental controller. Progress in
Fish Culture 54(2):130-131.
Hanson, J.S., W.L. Griffin, J.W. Richardson and C.J. Nixon (1985). Economic feasibility
of shrimp farming in Texas: an investment analysis for semi-intensive pond grow-out.
Journal of the World Mariculture Society 16:129-150.
Hatch, U. and J. Atwood, (1988). A risk programming model for farm-raised catfish.
Aquaculture 70:219-30.
Hatch, U., S. Sindelar, D. Rouse, and H. Perez (1987). Demonstrating the use of risk
programming for aquacultural farm management: the case of penaeid shrimp in Panama.
Journal of World Aquaculture Society 18:260-69.
Hatch, U., J. Atwood, and J. Segar (1989). An application of safety-first probability limits
in a discrete stochastic farm management programming model. Southern Journal of
Agriculture Economics, 18:65-72.
Hochman, E., P.S. Leung, L.W. Rowland and J.A. Wyban (1990). Optimal scheduling in
shrimp mariculture: a stochastic growing inventory problem. American Journal of
Agricultural Economics 72:382-93.
Johnson, F.C. (1974). Hatch--a model for fish hatchery analysis. U.S. National Bureau of
Standards, Washington, D.C. Report NBSIR 74-521, 51 pp.
Johnston, W.E. and L.W. Botsford, (1981). Systems analysis for lobster aquaculture. Proc.
World Symp. on Aquaculture in Heated Effluents and Recirculation Systems, Stavanger 28-
30 May 1980, Berlin, Vol. II, pp. 455-464.
Jorgensen, L. (1987). An automated system for incubation of pelagic fish eggs, in
Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen, NY:Pergamon Press,
pp. 211-213.
Kapetsky, J.M, L. McGregor and H. Nanne (1987). A geographical information systems
and satellite remote sensing to plan for aquaculture development: a FAO - UNEP/GRID
cooperative study in Costa Rica. FAO Fisheries Technical Paper No. 287, FAO, Rome,
Italy.
Kapetsky, J.M., J.M. Hill and L.D. Worthy (1988). A geographical information system for
catfish farming development. Aquaculture 68:311-320.
Kapetsky, J.M. (1989). Malaysia - A Geographical Information System for Aquaculture
Development in Johor State. FAO, Rome, Italy.
Kapetsky, J.M., J.M. Hill, L.D. Worthy and D.L. Evans (1990). Assessing potential for
aquaculture development with a geographical information system. Journal of the World
Aquaculture Society 21(4): 241-249.
Kapetsky, J.M., U.N. Wijkstrom, N. MacPherson, M.M.J. Vincke, E. Ataman and F.
Caponera (1991).Where are the best opportunities for fish farming in Ghana? The Ghana
geographical information system as a decision making tool for fish farming development.
FAO Field Technical Paper No. 5, FAO, Rome, Italy.
Karp, L., A. Sadeh and W.L. Griffin (1986). Cycles in agricultural production: The case of
aquaculture. American Journal of Agricultural Economics 68:553-561.
Killcreas, W.E. (1988). FISHY 2.0 User’s Guide. Agricultural Economics Department,
Mississippi Agricultural and Forestry Experiment Station, Mississippi State University.
Krom, M.D., S. Grayer and A. Davidson (1985). An automated method of ammonia
determination for use in mariculture. Aquaculture 44:153-160.
LaFranchi, C. (1992). Optimal selection of species and harvest scheduling for cultured
shrimp. Department of Agricultural and Resource Economics, University of Hawaii,
Master Thesis, 66 pp.
Lazur, A.M. (1995). Information transfer through use of CD-ROM technology, in Book of
Abstracts, Aquaculture ‘95, February 1-4, 1995, San Diego: The World Aquaculture
Society, p. 160
Lee, P.G. (1993). Computer automation for recirculating aquaculture systems, in
Techniques for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota: American
Society of Agricultural Engineers, pp. 61-70.
Lester, L.J., S. Perkins and B.T. Wong (1987). Microcomputer use in aquaculture genetics.
NAGA 10(2):9-10.
Leung, P.S. (1986). Application of systems modeling in aquaculture. Aquacultural
Engineering 5(2-4):171-182.
Leung, P.S. and L.W. Rowland, (1989). Financial analysis of shrimp production: an
electronic spreadsheet model. Computers and Electronics in Agriculture 3:287-304.
Leung, P.S. and Y.C. Shang, (1989). Modeling prawn production management system: a
dynamic Markov decision approach. Agricultural Systems 29:5-20.
Leung, P.S. (1993). Bio-economic Modeling in Aquaculture After Two decades, in
Proceedings of the International Symposium on Socioeconomics of Aquaculture (Ed.) Y.C.
Shang, P.S. Leung, C.S. Lee, M.S. Su and I.C. Liao, Taiwan: Taiwan Fisheries Research
Institute, pp. 115-137.
Leung P.S., Y.C. Shang, and X. Tian, (1994). Optimal harvest age for giant clam Tridacna
derasa: an economic analysis. Journal of Applied Aquaculture 4:49-64.
Lipschultz, F. and G.E. Krantz (1980). Production optimization and economic analysis of
an oyster (Crassostrea virginica) hatchery on the Chesapeake Bay, Maryland, USA. Proc.
World Maricul. Soc. 11:580-91.
Logan, S.H. and K. Shigekawa (1986). Commercial production of sturgeon: the economic
dimensions of size and product mix. Giannini Research Report No. 335, University of
California, 69 pp.
Logan, S.H. and W.E. Johnston (1993). A replacement model for rainbow trout broodstock
under photoperiod control, in Aquaculture: Models and Economics (Ed.) U. Hatch and H.
Kinnucan, Boulder:Westview Press, pp. 107-122
Losordo, T.M., R.H. Piedrahita and J.M. Ebeling (1988). An automated water quality data
acquisition system for use in aquaculture ponds. Aquacultural Engineering 7(4): 265-278.
Lynch, D.B. (1991). Concise Dictionary of Computing and Information Technology.
London: Chartwell-Bratt.
Lyon, P.R., H.G. Arthurs, D. Hancock and J. Mullins (1993). Data acquisition system for
monitoring saturation levels of dissolved gases in fresh water aquaculture, in Techniques
for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota: American Society of
Agricultural Engineers, pp. 81-88.
Malara, G. and A. Sciandra (1991). A multiparameter phytoplankton culture system driven
by microcomputer. Journal of Applied Phycology 3(3):235-241.
McNown, W. and A. Seireg (1983). Computer aided optimum design and control of staged
aquaculture systems. J. World Maricul. Soc. 14:417-433.
Meaden, G.J. and J.M. Kapetsky. (1991). Geographical information systems and remote
sensing in inland fisheries and aquaculture. FAO Fisheries Technical Paper No. 318, FAO,
Rome, Italy.
Moller, B. and K.I. Dahl-Madsen (1987). Continuous monitoring respiration in production
scale trout culture, in Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen,
NY:Pergamon Press, pp. 133-136
Muench, K.A., R.D. Thomsen and R.D. Croissant (1986). Computers in aquaculture.
Aquacultural Engineering 5(2-4):199-217.
Munasinghe, L., C.M. Gempesaw II, J.R. Bacon, W.W. Lussier and L. Konwar (1993).
AMACS: A user friendly Windows-based aquaculture monitoring and controlling
software, in Techniques for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota:
American Society of Agricultural Engineers, pp. 71-80.
Naiberg, A., J. Petrell, C.R. Savage and T.P. Neufeld (1993). A non-invasive fish size
assessement method for tanks and sea cages using stereo video, in Techniques for Modern
Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota: American Society of Agricultural
Engineers, pp. 372-381.
O’Connor, B.D.S., J. Costelloe, B.F. Keegan and D.C. Rhoads (1989). The use of
REMOTS super technology in monitoring coastal enrichment resulting from mariculture.
Marine Pollution Bulletin 20(8):384-390.
Oiestad, V. (1987). Automatic feeding and harvesting of juvenile atlantic cod (Gadus
morhua L.) in a pond, in Automation and Data Processing in Aquaculture (Ed.) J.G.
Balchen, NY:Pergamon Press, pp. 199-203.
Padala, A. (1991). Expert systems for aquaculture. UMECORP notes.
Palmer, J.D. (1989). Application of Artificial Intelligence and Knowledge-Based systems
Techniques to Fisheries and Aquaculture. Fairfax:Virginia Sea Grant College Program.
Petrell, R.J., T.P. Neufeld and C.R. Savage (1993). A video method for non-invasively
counting fish in sea cages, in Techniques for Modern Aquaculture (Ed.) J. K. Wang, St.
Joseph, Minnesota: American Society of Agricultural Engineers, pp. 352-361.
Piedrahita, R.H., J.M. Ebeling and T.M. Losordo (1987). Use of data acquisition systems
in aquaculture, in Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen,
NY:Pergamon Press, pp. 259-262.
Plaia, W.C. (1987). A computerized environmental monitoring and control system for use
in aquaculture. Aquacultural Engineering 6(1): 27-37.
Poxton, M.G. and G.T. Goldsworthy (1987). The remote estimation of weight and growth
in turbot using image analysis, in Automation and Data Processing in Aquaculture (Ed.)
J.G. Balchen, NY:Pergamon Press, pp. 163-170
Rauch, H.E., L.W. Botsford and R.A. Shleser (1975). Economic optimization of an
aquaculture facility. IEEE Transactions on Automatic Control AC-20:310-19.
Rauch-Hindin, W.B. (1986). Artificial intelligence in business, science and industry.
Englewood Cliffs, New Jersey:Prentice-Hall, Inc.
Roegner, G.C. (1988). Use of image analysis in determination of growth and mortality rates
of newly settled Crassostrea virginica. Journal of Shellfish Research 7(1):190.
Ross, L.G., E.A. Mendoza and M.C.M. Beveridge (1993). The application of geographical
information systems to site selection for coastal aquaculture: An example based on
salmonid cage culture. Aquaculture 112:165-178.
Rusch, K.A. and R.F. Malone (1989). Development of an automated continuous algal
culture production system, in Instrumentation in Aquaculture (Ed.) J.A. Wyban and E.
Antill, Honolulu: The Oceanic Institute, pp. 71-83.
Rusch, K.A. and R.F. Malone (1993). A microcomputer control and monitoring strategy
applied to aquaculture, in Techniques for Modern Aquaculture (Ed.) J. K. Wang, St.
Joseph, Minnesota: American Society of Agricultural Engineers, pp. 53-60.
Sadeh, A. (1986). Value of information in production processes: the case of aquaculture.
Dissertation, Texas A&M University.
Sanno, J.O. (1987). Alfa-log: A computerized registering, control, and alarm system for
aquaculture, in Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen,
NY:Pergamon Press, pp. 205-210.
Schutzer, D. (1986). Artificial intelligence, an application oriented approach. New
York:Van Nostrand Reinhold Company.
Schuur, A.M., P.G. Allen and L.W. Botsford, (1974). An analysis of three facilities for the
commercial production of Homarus americanus. American Society of Agricultural
Engineers, Winter Meeting Paper No. 74-5517, 19 pp.
Shaftel, T.L. and B.M. Wilson (1990). A mixed-integer linear programming decision
model for aquaculture. Managerial and Decision Economics 11:31-38.
So, J.D. and F.W. Wheaton (1993). Computer vision control system for an oyster hinge
breaker, in Techniques for Modern Aquaculture (Ed.) J. K. Wang, St. Joseph, Minnesota:
American Society of Agricultural Engineers, pp. 382-392.
Sparre, P. (1976). A Markovian decision process applied to optimization of production
planning in fish farming. Meddr Danm. Fisk. - og Havunders. 7:111-97.
Sprague, R.H. and E.D. Carlson (1982). Building Effective Decision Support Systems.
Englewood Cliffs, New Jersey: Prentice-Hall, Inc..
Springborn, R.R., A.L. Jensen, W.Y.B. Chang, and C. Engle (1992). Optimum harvest time
in aquaculture: an application of economic principles to a Nile tilapia, Oreochromis
niloticus, growth model. Aquaculture and Fisheries Management 23:639-47.
Srekk, H.O., H. Kryvi and S. Elvestad (1992). Nation-wide assessment of the suitability of
the Norwegian coastal zone and rivers for aquaculture (LENKA), in Aquaculture and the
Environment (Ed.) N. de Pauw and J. Joyce, European Aquaculture Society Special
Publication 16, pp. 413-440.
Stokoe, P.K. and A.G. Gray (1990). AQUASITE: A computer site assessment system for
marine coastal aquaculture, in Proceedings of the Aquaculture Association of Canada
Conference, pp. 94-96.
Swann L., G. Jensen and M. Einstein (1995). Using the aquaculture network information
center (Aqua-NIC), in Book of Abstracts, Aquaculture ‘95, February 1-4, 1995, San Diego:
The World Aquaculture Society, p. 222.
Syed, A.L.A. (1985). A mixed integer programming approach for integrating poultry with
multispecies aquaculture on Malaysian rice farms. Ph.D. thesis, Auburn University,
Auburn, Alabama, 166 pp.
Sylvia, G. and J.L. Anderson (1993). An economic policy model for net-pen salmon
farming, in Aquaculture: Models and Economics (Ed.) U. Hatch and H. Kinnucan,
Boulder:Westview Press, pp. 17-38.
Talpaz, H. and Y. Tsur (1982). Optimizing aquaculture management of a single-species
with fish population. Agricultural Systems 9:127-142.
Tian, X. (1993). Optimal aquafarm structure and size: a case study of shrimp mariculture.
Department of Agricultural and Resource Economics, University of Hawaii, Dissertation,
179 pp.
Tisdell, C. A., L. Tacconi, J.R. Barker, and J.S. Lucas (1993). Economics of ocean culture
of giant clams, Tridacna gigas: internal rate of return analysis. Aquaculture 110:13-26.
Tsur, Y. and E. Hochman (1986). Economic aspects of the management of algal
production. CRC Handbook of Microalgal Mass Culture, CRC Press Boca Raton, pp. 473-
83.
Vandermeulen, H. (1991). A simple microcomputer-based data acquisition system for
monitoring pH shifts. Journal of Applied Phycology 3(4):373-374.
Varvarigos, P.(1991). Production planning and the management of information on fish
farms: results of a UL survey. Aquaculture and Fisheries Management 22:217-227.
Varvarigos, P. and M.T. Horne (1987). An inexpensive microcomputer based data record-
keeping system for the individual fish farm, in Automation and Data Processing in
Aquaculture (Ed.) J.G. Balchen, NY:Pergamon Press, pp. 263-272.
Wang, J. (1988). The microcomputer control system of aquaculture aerating devices. Fish.
Mach. Instrum. -Yuye-Jixie-Yiqi (5):6-8.
Warming, M. (1987). Oxygen control in aquaculture plant, in Automation and Data
Processing in Aquaculture (Ed.) J.G. Balchen, NY:Pergamon Press, pp. 193-197.
Wu, R.S.S. (1989). Biological and economic factors in the selection of cultured fish species
and the development of a bio-economic model. AQUACOP IFREMER Actes de Colloque,
9:437-444.
Zahradnik, J.W. (1987). Status and perspectives in the instrumentation of aquacultural
facilities, in Automation and Data Processing in Aquaculture (Ed.) J.G. Balchen,
NY:Pergamon Press, pp. 23-29.
Table 1: Instrumentation and process control applications in aquaculture.
1.1 Accounting/auditing 4
1.2 Finance 19
1.6 Marketing/Transportation/Logistics 44
2. Agriculture 3
3. Education 7
4. Government 6
6. Military 7
7. Natural resources 12
9. Miscellaneous 19
Total 203
Table 5 : AI approaches for aquaculture applications (Palmer 1989).
Catfish farming development in Lousiana, U.S.A. Kapetsky, Hill and Worthy (1988);
Kapetsky et al. (1990)
Aquaculture development in Costa Rica Kapetsky, McGregor and Nanne
(1987)
Aquaculture development in Johor state, Malaysia. Kapetsky (1989)
Fish farming opportunities in Ghana Kapetsky et al. (1991)
Assessment of the potential for salmonid cage culture in Ross, Mendoza and Beveridge
Scotland. (1993)
Assessing the potential for carp culture in Pakistan. Ali, Ross and Beveridge (1991)
Assessment of the suitability of the Norwegian coastal Srekk, Kryvi and Elvestad (1992)
zone and rivers for aquaculture.
Data base Modeling
component component
Dialog
component
User
Textual
reports
Tabular Statistics
and
Field tables
survey
Capture Store Manipulate Display
Encode and and and Data for
Remote other GIS
Edit Retrieve analyze Report
sensing
Data for
Other GIS other digital
base
Data for
models
User requirements