Knowledge Based System for Oilseed Crop Disease Diagnosis and Treatment

Ambachew, Biruk (2015) Knowledge Based System for Oilseed Crop Disease Diagnosis and Treatment. Masters thesis, Addis Ababa University.

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Knowledge is currently attracting a great deal of interest in the business community including agriculture. Organizations are increasingly corning to regard their knowledge as a key asset and resource for organizational success. To manage organizational knowledge it is crucial to develop a knowledge based system that can facilitate intelligent and sound decision making. In this study the researcher has tried to develop a knowledge base system for oilseed crop disease diagnosis and treatment. The knowledge is acquired from agricultural research center which is found in Holeta, Ethiopia and also from production manuals and books that was collected from, agricultural research center and the internet. After acquiring the knowledge the next step was concept modeling and knowledge representation. Accordingly, decision tree was used for the knowledge modelling and rule based (if – then) method for the knowledge representation. The final step was implementation and testing of the system. The prototype was implemented using visual prolog 7.5. The performance of the prototype was evaluated by five domain experts and two development agents. The evaluation result shows that the prototype has rated 87.2 percent by the evaluators which is promising result. The researcher has also tried to see the prospect of the system and how to make it applicable by discussing with experts. The researcher believes that implementing the system with local language would greatly improve the system. The researcher also believes integrating the system with machine learning technique would greatly improve the performance of the system. Finally deploying the system on smart phones will make the system accessible to farmers and extension workers.

Item Type: Thesis (Masters)
Subjects: Q Science > QH Natural history > QH301 Biology
Q Science > QK Botany
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: Africana
Depositing User: Selom Ghislain
Date Deposited: 24 Sep 2018 13:05
Last Modified: 24 Sep 2018 13:05

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