Meshesha, Million (PhD)Alemu, Dejen2018-11-262023-11-292018-11-262023-11-292009-04http://etd.aau.edu.et/handle/123456789/14522Finding the correct identity of trees is the beginning of any inventory and management activities as well as any studies regarding the tree species. Identification of plant species in Ethiopia is conducted only in the National Herbarium. At present, the centre is not supported by information systems, which makes the identification process and dissemination of information inefficient and difficult. The need of KBS for technical information transfer and efficacy in dendrology can be identified by recognizing the problems in using the current system for technical information transfer and by proving that KBS can help to overcome the problems addressed, and are feasible to be developed. This study attempts to design prototype KBS for woody plant species identification. As compared to existing way of identification we come up with new knowledge/rules with minimum features that registers comparable performance. By using this system, users can get access to expert knowledge and will be able to identify woody plant species like taxonomists do/judge. Using taxonomic KBS in different forestry research centers, high-paid taxonomists will reduce the costs of scientific research and will allow many researchers to conduct their research more independently (without going to the National Herbarium for identification). This research is conducted in a step-wise manner. After problem selection, knowledge acquisition process is conducted. In this process, a key informant interview is held with experts (two taxonomists and one researcher). In addition to the key informant interview,x manuals and books used in woody plant species identification are also consulted. The knowledge extracted from the experts’ and relevant documents that uses to solve a problem is modeled in hierarchical or laddering technique. Based on the final knowledge modeled in decision laddering, domain knowledge is represented using production rules in prolog to construct the knowledge base. The system is developed to load the knowledge base and starts to infer from the knowledge base based on the users input/ facts. The prolog built in backward inferring mechanism is used for the identification of the species. The user interface is designed in vb.net. Finally, the system is tested and evaluated by the users. The result shows that, the system identifies the woody plant species correctly and can be applicable in woody plant species identification. Key words: knowledge based system, prolog, tree species identification, knowledge acquisition, knowledge modeling, and KBS evaluation.enknowledge based systemprologtree species identificationtree species identificationknowledge acquisitionknowledge modelingand KBS evaluationThesis