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Expert Systems with Applications 23 (2002) 311–320 www.elsevier.com/locate/eswa

Fish-Expert: a web-based expert system for fish disease diagnosis Daoliang Lia, Zetian Fua, Yanqing Duanb,* a

Agricultural Information Technology Institute, China Agricultural University, Beijing 100083, People’s Republic of China b Luton Business School, University of Luton, Luton LU1 3JU, UK

Abstract Fish disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based system dealing with disease diagnosis has to overcome various difficulties. This paper describes a Chinese National Funded Research Project (863 project) aiming to develop a web-based intelligent diagnosis system for fish diseases. The paper explains the need for a web-based expert system, the fish diagnosis process and the difficulties involved in developing the system. The system structure and its components, such as database, knowledge base and image base and their functions are described. The system has over 300 rules and 400 images and graphics for different types of diseases and symptoms. It can diagnose 126 types of diseases amongst nine species of primary freshwater fishes. The system has been tested and is now in pilot use by fish farmers in the North China region. Some issues on developing web-based expert systems from the experience gained from the research are discussed. q 2002 Elsevier Science Ltd. All rights reserved. Keywords: Expert system; Fish disease diagnosis; Web-based expert systems; China

1. Introduction Aquaculture production in China has grown rapidly for the last 10 years and China is the biggest country in aquaculture production in the world. The total aquatic production is 42.79 million tons in 2000. In China, aquaculture plays a very important role in agricultural structure adjustment and generating farmers’ income (Guo, 2001). However, the rapid and often uncontrolled development of aquaculture has led to frequent occurrences of infectious diseases that are threatening the sustainability of aquaculture in China. At the same time, the problem of fish disease becomes more serious with the development of the intensive breeding and the increase in culture scale and density. The death rate of adult fish has risen to 40%, and the survival rate of fry is only 30%, because of various fish diseases. Thus, the development of a disease control system is an urgent issue in China. How to help fish farmers to diagnose, treat and prevent fish disease timely and effectively poses a serious challenge for Chinese authorities and fish farmers. Fish disease diagnosis is a rather complicated process in aquaculture production activities. The diseases commonly resulted from nutritional and environmental problems as * Corresponding author. Tel.: þ 44-1582-743134; fax: þ 44-1582743926. E-mail address: [email protected] (Y. Duan).

well as infections by parasites, viruses, bacteria and fungal agents (Post, 1983; Stoskopf, 1993). The infected fish will normally die very quickly if no correct treatment is provided in time. The death of the whole pond of fish happens frequently in China every year (Su & Huang, 2000). Ideally, the fish disease diagnosis and treatment should have on-thespot investigation by the vet, but in practice this is impossible due to the unavailability of expertise and remote location of sites. Most of the culture sites are remote from vet centres, and scattered in rural areas. It can take a fish breeding worker or a fish farmer more than 3 years to correctly identify diseases by self-learning and practicing. Some diseases are just a single infection, but most of the others are mixed infections with multiple pathogens. Some fish diseases can be correctly diagnosed only by fish body inspection and anatomization. However, most fish diseases can be diagnosed by microscopic examination (Xia, 1987). All of these pose a major challenge for any attempt in developing a computer-based system capable of providing accurate and timely diagnosis. The rapid increase of Internet application and the costeffective growth of its key enabling technologies are revolutionizing information technology and creating unprecedented opportunities for developing large scale distributed applications. Rapid advances of Internet technologies have opened new opportunities for enhancing traditional decision support systems and expert systems (Power, 2000). A number of web-based expert systems are reported in the literature (Potter et al., 2000; Riva, Bellazzi, & Montani,

0957-4174/02/$ - see front matter q 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 7 - 4 1 7 4 ( 0 2 ) 0 0 0 5 0 - 7

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D. Li et al. / Expert Systems with Applications 23 (2002) 311–320

1998; Sedbrook, 1998), but most of them are prototyping systems. It appears that research and development of webbased expert systems are still in their early stage. More attention should be paid to address the challenges posed by developing and deploying web-based intelligent systems. Most of the existing disease diagnosis programs are in the field of human medicine (Zeldis & Prescott, 2000). Most of these applications are developed for stand-alone PCs, such as an expert system for the differential diagnosis of erythemato-seuamous diseases by Guvenir and Emeksiz (2000), an expert system to diagnosis periodontal disease by Deschamps and Fernandes (2000) and a fuzzy rule-based expert system for clinical diagnosis by Wiriyasuttiwong (1998). Zeldis and Prescott (2000) discuss the problems and solutions in developing a fish disease diagnosis program and explore the different expert system techniques in dealing with difficulties involved in fish disease diagnosis, but their system is not a web-based application. A web-based expert system for fish disease diagnosis, called Fish-Expert, has been developed by Agricultural Information Technology Institute at China Agricultural University. It was a major outcome of a Chinese National Funded High Science and Technology Research Project (863 project). This web-based intelligent system can mimic human fish disease expertise and diagnose a number of fish diseases with a user-friendly interface. It contains a large amount of fish disease data and images, which are used to conduct online disease diagnosis. The system has been tested and is in pilot use in certain regions in North China. Experience with developing web-based expert systems, lessons learnt and users’ feedback are discussed and conclusions are provided at the end of the paper.

2. Domain background 2.1. General process of fish disease diagnosis According to general practices, there are normally five steps involved in fish disease diagnosis. (1) On-the-spot pond inspection. The main aim of this step is to observe the culture environment and changes in fish behaviour. The inspection includes collecting information on: the number, age, and type of the fish; behaviour of the affected fish; the culture season; water temperature, transparency, quality and colour; rainfall and other pollution indicators. Changes in fish behaviour could include flashing, not eating, clamped fins, heavy respiration, self-isolation, gasping at the surface, loss of equilibrium, or jumping out of water. All of these indicate the need for further investigation. (2) Fish inspection and anatomization. This step includes general fish inspection and anatomization, which are the primary and most important steps in traditional fish disease diagnosis. General examination can be carried out in the pond or tank. Affected fish can be examined for fin erosion,

cuts, lesions, reddening of the skin, raised scales, larger parasites such as lice or anchor-worm, swollen gills or damaged gills, swellings, lumps or growths. Alternatively, they can be removed from water and lightly anaesthetized for easier handling and closer examination. Anatomization can be conducted with infected sample fish. Examinations are normally conducted on three parts of body: stomach, muscle and viscera. In most cases, with the information collected from steps (1) and (2), it is possible to identify the type of the disease and its causes. Actions for treatment can then be decided. If information collected in steps (1) and (2) is not sufficient for a conclusive diagnosis, it will be necessary to carry out further investigation which could involve postinvestigations of recently dead fish, bacterial sampling from lesions to determine the type of bacterium involved and their antibiotic sensitivity and histological examination which involves preparation and examination of body tissues and organs for signs of malfunction and disease. In this case, microscopic and water quality examinations are required. (3) Water quality examination. In many diseases water quality is the cause: for example, salinity, alkalinity acidity, dissolved oxygen, ammonia nitrogen, nitrite, sulfureted hydrogen and other pollution indicators are the most important factors in causing fish diseases. Changes in these factors will cause diseases, so pH value, dissolved oxygen, ammonia nitrogen and other factors should be examined in this step. This step involves carrying out water quality tests for ammonia, nitrite, pH value, water hardness and history if available, which indicate a core water quality problem or toxic conditions. (4) Microscopic examination. Without a microscope it is simply impossible to tell the difference between a water quality problem and a parasite problem. The microscope should be considered as the most basic equipment in fish disease diagnosis. Indeed, an accurate and full diagnosis of the disease and its cause is sometimes not possible without a microscope. Microscopic examination refers to the process of detailed observation of infected fish with the microscope and other instruments. It is often conducted simultaneously with anatomization. (5) Recommendation for treatments and prevention methods. Normally, the first two steps can identify the possible category of disease, and the latter two steps can further confirm and support the diagnosis. Having collected all necessary evidence, a diagnostic conclusion can be reached. It is, in most cases, possible to determine the type of disease and its causes. From this, fish experts can decide what treatment should be given. When considering developing a web-based expert system for fish disease diagnosis, it is vital to have a thorough investigation of the domain problems. In this case, problems inherited in disease diagnostic processes need to be considered and methods need to be explored on how to deal with them in a computer based system. According to Zeldis and Prescott (2000), no disease


exhibits all the signs described in the literature. In most cases, there are acute and chronic phases of disease having differing signs. Therefore, the program has to reach the most reliable diagnosis with a partial set of the signs. A certainty level is needed to show the users how confident the expert is in giving his diagnosis result. The confidence level will be increased when more evidence is provided by users. Many of these clinical signs can be caused by more than one disease condition, which is why multiple examinations and tests are essential to make a correct diagnosis. Without sufficient examination, the expert could only guess what is wrong with the limited evidence he has.

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diseases have been identified. Farmers believe that this will help them to prevent the disease in the future. Images of typical symptoms should be provided for farmers to describe the symptom, because farmers occasionally find it is difficult to articulate some symptoms. User interface should be intuitive and easy to use. The use of jargon should be minimum. Online information and knowledge elicitation functions should be provided for experts, farmers, and technicians. This will enable them to suggest new experience and knowledge into the system database and rule base. A visitor’s feedback form should be provided, so the user can report their problems, opinions, suggestions, etc.

3. Users’ needs investigation China is a large country and most aquaculture sites are spread in the remote rural areas. Although fish farming is growing rapidly, the level of farmers’ skills and knowledge are evidently low or even non-existent. As a result, fish diseases occur frequently and the consequences on farmers’ financial status are enormous, as sometimes the whole pond of fish could die overnight. The demand for help from fish disease experts is increasing rapidly. However, experts are scarce and not readily available, especially in rural areas. It takes long time to train novices and even longer for them to establish their experience in practice. An expert system is considered as an effective tool to help fish farmers in solving the problems they meet in practice. Therefore, an expert system for fish disease diagnosis is required to substitute human fish vets in helping farmers in China. To better meet the needs of farmers, a questionnaire was designed to investigate the farmers’ problems and needs. The questionnaires were sent to 130 farmers from 13 regions in Tianjin. Some interviews were also carried out afterwards to clarify the results collected. The main topics covered in the questionnaire include: 1. The most common species of fish farmers breed. 2. The most common fish diseases or symptoms farmers encounter. 3. The farmer’s own experience in identifying, treating and preventing fish disease, which they believe, can be shared by other farmers. 4. Help needed to tackle fish disease problem. 5. Expected help and support functions from a computerbased system. The findings have helped the developers to focus on providing help in diagnosing 126 types of diseases among nine common farm fish as they account for 90% of fresh water farming fish in the investigated regions. The users’ needs investigation also provides some useful suggestions on systems functions, such as: † If possible, the cause of disease should be given when

4. Data collection and knowledge acquisition Through years of experience, fish disease experts have developed a body of knowledge, which they can use to make correct diagnosis. Many techniques have been developed for knowledge acquisition (KA). Some commonly used approaches are interviews, observations, taking experts through case studies and rule induction by machines (Turban, 1995). KA is seen as a crucial problem concerning the success of an expert system and has always been regarded as the bottleneck in developing any expert system (Hart, 1989). This bottleneck is mainly caused by communication difficulties between the knowledge engineer (KE) and the expert, the inability of the expert to describe expertise, and the inability of the KE to obtain expertise (Liebowitz & Baek, 1996). A multiple KA approach has been adopted in this project. The KA process consists user surveys and interviews, expert interviews, case studies and knowledge elicitation by computers. Surveying and interviewing farmers to understand and identify problems. Questionnaire surveys and interviews were carried out with around 130 fish farmers to identify the common problems and disease in fish farming. A large number of data have been collected in describing symptoms. Farmers own experience in identifying, preventing and treating fish diseases established through their practice were also collected by surveys and interviews. Interviewing human experts. Thirty-five fish disease experts from Beijing, Tianjin and Shandong province took part in KA. Amongst them are the five most famous experts in north China. Knowledge elicitation by computers. A web-based knowledge elicitation system was designed to help the experts and KE gather the facts and generate rules (see Fig. 1 for a sample screen shot). The interface can collect data on typical fish disease symptoms, its related disease, treatment and prevention methods. This online system can be used by experts who are authorized to access it. It can facilitate experts to input, update, modify and search data,

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Fig. 1. A sample interface for online knowledge elicitation.

information and rules associated with Fish-Expert knowledge and database. Experts can access online knowledge elicitation tool to input symptoms, diseases and treatment methods. Once the expert has updated the database, a KE is responsible for checking the information and updating the database and rule base. A process of continuous feedback was used to ensure that domain knowledge has been correctly represented. This process not only helps the KE to represent the domain knowledge correctly, but also helps the expert to clarify the reasoning processes. All domain knowledge is analysed and represented in production rules in the rule base that is used to control the diagnosing process.

5. Fish-Expert system design and development Fish-Expert consists of a database, knowledge base, an inference engine, a knowledge elicitation tool, an explanation subsystem, fish farming information systems, and a user interface. Its general structure is showed in Fig. 2. 5.1. Software selection Traditional ESs were developed for stand-alone computers and a number of development tools are available for developing traditional ESs. With the rapid development of the Internet, more web-based ESs are beginning to emerge. Unfortunately, WWW was originally conceived simply as a document distribution infrastructure (Riva et al., 1998) and was not created with applications such as expert systems in mind (Huntington, 2000). Any attempt to use it for

distributing expert systems must cope with certain difficulties (Huntington, 2000; Riva et al., 1998). Until recently, there seems to be a lack of easy-to-use supporting tools for developing web-based ESs. At the time the Fish-Expert system was developed, no suitable tool for web-based ESs was available. Thus, Fish-Expert was developed using a mixture of Internet techniques and SQL programming languages. DHTML (Dynamic Hypertext Markup Language), Java Script, Java, VB script and ASP (Active Server Page) were used in the programming. Client/Server/ Server 3 layer structure was also adopted. ASP is the key technique which can make the explorer, middle logic traction and the database separated. As a result, several software such as MS SQL Server 7.0 Database, Windows NT 4.0 and Windows 2000 Server, Visual InterDev, Visual Jþ þ , photoshop 5.0 were used in system development. 5.2. Database Server side database plays an important role in the development of Fish-Expert. It is used for storing all the information needed for disease diagnosis. The database is mostly designed with SQL Server 7.0. Most of the data and images were initially collected from fish disease experts with Excel spreadsheets. Excel spreadsheets were translated into SQL Server 7.0 later by system developers. The databases include a symptom database, a pond inspection database, a microscope examination database, a water quality database, a fish medicine information database, a fish disease database, a fish disease treatment and prevention database, image base, etc. Information on fish type, age, seasons, symptoms, causes of disease and actions needed to


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Fig. 2. System structure of Fish-Expert.

establish the type of disease are also included in fish disease database. The graphic image base includes a fish disease symptom photo base, a microscopic examination graphic base and a diagnosing results graphic base. They are used to store pictures and graphics related to symptoms, microscopic examination, diseases, etc. Tables 1 and 2 show some example databases used in the system.

IF (species (F1), breeding condition (F(1,2)), symptoms (C106, C203, T505), THEN (disease (D12), Action (E33)) A simple example that shows the main frame of the knowledge base is given in Table 3.

5.3. Fish farming information systems

Fish-Expert users can query the system using an inference process that automatically matches facts against patterns and determines which rules are applicable. The inference process of the system is shown in Fig. 3. For example, the on-the-spot pond inspection finds that fish rub against solid objects and ‘flash’ as their undercarriage is exposed, when they rub their sides, and the fish also leaps out the water. A direct single match could not find with this limited information. The possible diseases which a fish could suffer are: (1) raised levels of ammonia nitrogen or high/low pH value, (2) ectoparasites such as flukes, Trichodina, white-spot, etc. on the skin or gills. Therefore further water quality examination and gills examinations with a microscope are needed. If the result of water parameters nitrite is very high and microscope examination is white-spot, then the fish suffer from ectoparasites. The cause of the disease is water environment and fresh water is required. When calling for the results of the diagnosis, the system will explain the inference processes. After an examination of the facts collected from users, the system

The online fish farming information system provides comprehensive information on fish farming and its related issues such as: basic knowledge on fish farming, frequently asked questions (FAQ), fish food, environmental factors, fish disease prevention measures, contact information on fish disease experts, etc. This part of the system is being constantly extended and updated. 5.4. Knowledge base The knowledge base contains all the rules for the fish disease diagnosis. Each rule has two sections—a symptom pattern section and an action section, in the form of ‘IF symptom pattern E, THEN the disease H’, for example, if the head is black and gill full of blood then the fish suffered the rot gill. A typical rule used to match the symptoms with identified disease and treatment can look like:

5.5. Inference process

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Table 1 A sample database for symptom codes with explanations

Table 2 A sample disease database


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Table 3 A simple example of knowledge base Rule no.

If (E)

Then (H)

Recommended action

1 2

White head and white mouse Red intestines and did not eat fodder, or intumescent anus Red and inflamed areas on the body and fins and lesions or haemorrhages in internal organs and bacteria has been found by microscope

White head and mouse disease intestines inflammation disease

0.7 ppm CuSO4 and FeSO4 Disinfector which contain chlorine

Bacterial disease

Terramycin; oxytetracycline

3

will produce conclusions of the diagnosis and treatment methods. 5.6. User interface A multimedia interface was used in the system. Matching of pre-defined text description and images of symptoms is provided to users. Users can choose text and/or images to describe the symptoms that the fish suffer. Different interfaces are designed for pond inspection, fish inspection, water quality examination and microscopic examination, and conclusions and recommendations. Screenshots for pond fish inspection and diagnosing results are showed in Figs. 4 and 5. In the fish inspection interface, users can input the required information by selecting matching symptom pictures and symptom descriptions from eight designated symptom groups (both single and multiple selections are allowed).

6. System testing and implementation System tests, such as logic tests, debugging, rule checking and sample field tests were carried out by

system developers. This was to ensure the system would work correctly before it was distributed to farmers. After the system testing, Fish-Expert was made available for pilot implementation in North China, in cities such as Beijing, Tianjin, and Shandong provinces. User feedback was gathered by conducting interviews and collecting information through the system’s built-in visitor feedback form. In general, the system has been an effective aid to fish farmers, fishery experts and a reference system to fish vets. In more detail, an analysis of system implementation results shows that: † The system is practical and useful for fish farmers and technicians. † It is easy to access the system via the Internet. The comprehensive database in the system is able to cover most of symptoms observed. Identified diseases and suggested actions on treatment and prevention measures are very informative and helpful. † The multimedia interface is effective and welcomed by farmers. They find it easier and more accurate to describe symptoms by matching images than texts due to the low level of literacy among some farmers.

Fig. 3. Fish-Expert inference process.

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Fig. 4. A screen shot for user interface on fish body examination.

† Fish farming information subsystem is welcomed and regarded as very convenient tool for farmers. However, some shortcomings are also revealed, such as: † The multimedia interface slows down the access speed to the system. The slow speed may be due to low level of IT infrastructure in rural areas in China. The improvement of network systems in the rural areas remains a challenge to the Chinese government. † Some farmers felt that the diagnosis process is quite complex and sometimes, it is difficult for them to learn how to use the system.

† Some fish farms have no microscopes and water quality examination equipment. In this case, it is not possible for them to provide information on these factors. However, the microscopic and water quality examination equipment are necessary to confirm and support the fish disease diagnostic process. Some alternative methods may need to be considered for farmers who are not able to provide this information. † An expert support system for fish disease prevention is needed to help farmers monitor and prevent the occurrence of fish diseases. † Fish-Expert is limited to diagnose nine types of common fish, but with the development of agriculture structure

Fig. 5. A sample screen shot for user interface for diagnosing results.


adjustment in China, special species of fish aquaculture develop rapidly. As a result, the demand to extend the knowledge base for more types of fish diseases is increasing. † Any knowledge base has limitations. Some problems may not be solved by Fish-Expert. A tele-diagnosis system may be required. In this case, farmers can talk to human experts via tele-diagnosis equipment when needed. † The current inference process uses forward-chaining to identify diseases. The use of backward-chaining inference functions could be provided when farmers want to confirm the disease they suspect, or if they want to use Fish-Expert as a learning tool.

7. Discussion and conclusions This paper presents the development of a web-based expert system called Fish-Expert. This system has the following characteristics: † It is a web-based expert system that provides an easy access for fish farmers. † It is able to mimic the real practice of fish disease diagnostic processes by focusing on the analysis of etiology and pathogeny by matching combinations of symptoms, microscopic examinations and water quality inspections. † Its large database, image base and knowledge base are able to help farmers to identify about 126 kinds of fish diseases amongst nine primary freshwater fish and to suggest prescriptions and other treatment actions. † It has an online data and KA system, which enables farmers, technicians and experts to input and update the information contained in its database. † It uses a multimedia user interface to enhance the effectiveness of information communication between users and the expert system. † The users’ feedback form serves as an effective and efficient mechanism to collect system users’ comments, problems and suggestions. † It also provides general information support tools, such as a dictionary, information on medicine, contact information of fish medicine vendors, vet centres and fish disease experts, etc. The experience and lessons learned from the development of Fish-Expert suggest that: † A good expert system requires tight cooperation and collaboration among users, human experts, KEs and system developers. The final complete system is the result of the close collaboration among all parties concerned in the system. † New diseases are emerging continuously as new species

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are commercially developed and old species are raised in new geographical areas. Most of the disease types and symptoms of current aquaculture are very different from those of the traditionally bred fish. Therefore, the system needs to be expanded and updated to match changes regularly. † How to improve the speed of the system is a challenge in any web-based system implementation. As Grove (2000) points out that slower connections due to limited infrastructure can cause communication bottleneck. In the case of Fish-Expert, there are several ways to solve this problem: one is to reduce the use of multimedia elements in user interface, but most of images are essential in helping collect evidence. Another way is to improve the Internet access speed. A third alternative is to run Fish-Expert on CDROM, which can be easily delivered to fish farmers. † Any expert system has limitations. When the webbased expert system is unable to solve a farmer’s problem, a tele-diagnosis system could be integrated for long-distance face-to-face communications between an expert and a user in order to help the user to obtain help from a human expert. In summary, the rapid development of the Internet technology has changed the way that an expert system can be developed and distributed. The essence of an expert system is to mimic expertise and distribute expert knowledge to non-experts. These benefits have been greatly enhanced with the emergence of the Internet. However, the development of web-based expert systems has also brought a number of challenges from the methodological and technological point of view. There is a lack of a general methodology for the development of web-based expert systems. There is also a shortage of easy-to-use development tools for web-based exert systems. More research should be carried out to address the challenges posed by developing and implementing web-based expert systems.

Acknowledgments We would like to thank many domain experts from Beijing Aquaculture Science Institute, Aquaculture department of Tianjin Agricultural College, Aquaculture Bureau of Shandong province, for their cooperation and support. Special thanks should also go to Mr Ding Wen in Beijing Aquaculture Science Institute for his contribution in knowledge refining, and Dr Zhang Xiaoshuan and Tian Dong for their contribution in system programming. We would also like to acknowledge Professor Xing Kezhi in Tianjin Agricultural College for his valuable suggestions on the improvement of the system.

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