Developing knowledge based system for predominant chicken diseases diagnosis, prevention and management

Abstract

Poultry diseases remain one of the major threats to poultry production. The diseases of chickens need to be observed intensively because of its impact on the health and quality of chicken production. Chicken disease becomes one of the problems that are very detrimental to chicken farmers. Accurate information about handling chicken disease is still difficult to obtain. Expert knowledge (including agricultural and medical experts), which is valuable to control and/or treat such diseases are limited in developing countries in general and in Ethiopia in particular. In attempt to solve this problem, knowledge based system is identified as a powerful tool with extensive potential in alleviating agricultural and medical problems. Therefore, it is necessary to design a knowledge base system application in detecting diseases that are experienced by chickens based on the symptoms shown and how to handle them. This study aims at developing knowledge base system for diagnosing, prevention and management of predominant chicken diseases. The predominant chicken diseases selected for the purpose of this study include: Newcastle disease, marek’s disease, infectious coryza, fowl cholera, chicken mite, coccidiosis, aspergillosis and favus. Design Science research method was used to develop the prototype system. To select domain experts for knowledge acquisition, purposive sampling technique was used. The domain experts were selected from Jimma University College of Agriculture and Veterinary Medicine and from Kito Furdisa Poultry Farm. The knowledge was acquired using both structured and unstructured interviews from domain experts and relevant document analysis method was also followed to capture explicit knowledge. The acquired knowledge was modeled using decision tree that represents concepts and procedures involved in diagnosis, prevention and management of predominant chicken disease and production rules (If-ThenAction) were used to represent the domain knowledge and Knowledge-based system was developed using SWI Prolog editor tool. Backward chaining algorithm was used in this study. At the end performance of the system was evaluated and produced a result of 83%. Moreover, user acceptance of the developed system was done by visual interaction method, namely, by showing the system to the domain experts and it was found to produce 83.4%. Thus, the average performance of the prototype system is 83.2%. The prototype system achieves a very good performance and meets the objectives of the study. Lastly, it is recommended that the stake holders should have to take part in deploying the developed system.